Dissertationen: „Fault detection and prediction“

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Halligan, Gary. "Fault detection and prediction with application to rotating machinery." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Halligan_09007dcc80708356.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2009. Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed November 25, 2009) Includes bibliographical references.

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Walden, Love. "Fault prediction in information systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254670.

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Fault detection is a key component to minimizing service unavailability. Fault detection is generally handled by a monitoring system. This project investigates the possibility of extending an existing monitoring system to alert based on anomalous patterns in time series.The project was broken up into two areas. The first area conducted an investigation whether it is possible to alert based on anomalous patterns in time series. A hypothesis was formed as follows; forecasting models cannot be used to detect anomalous patterns in time series. The investigation used case studies to disprove the hypothesis. Each case study used a forecasting model to measure the number of false, missed and correctly predicted alarms to determine if the hypothesis was disproved.The second area created a design for the extension. An initial design of the system was created. The design was implemented and evaluated to find improvements. The outcome was then used to create a general design.The results from the investigation disproved the hypothesis. The report also presents a general software design for an anomaly detection system. Feldetektering är en nyckelkomponent för att minimera nedtid i mjukvarutjänster. Feldetektering hanteras vanligtvis av ett övervakningssystem. Detta projekt undersöker möjligheten att utöka ett befintligt övervakningssystem till att kunna skicka ut larm baserat på avvikande mönster i tidsserier.Projektet bröts upp i två områden. Det första området genomförde en undersökning om det är möjligt att skicka ut larm baserat på avvikande mönster i tidsserier. En hypotes bildades enligt följande; prognosmodeller kan inte användas för att upptäcka avvikande mönster i tidsserier. Undersökningen använde fallstudier till att motbevisa hypotesen. Varje fallstudie använde en prognosmodell för att mäta antalet falska, missade och korrekt förutsedda larm. Resultaten användes sedan för att avgöra om hypotesen var motbevisad.Det andra området innefattade skapadet av en mjukvarudesign för utökning av ett övervakningssystem. En initial mjukvarudesign av systemet skapades. Mjukvarudesignen implementerades sedan och utvärderades för att hitta förbättringar. Resultatet användes sedan för att skapa en generell design. Resultaten från undersökningen motbevisade hypotesen. Rapporten presenterar även en allmän mjukvarudesign för ettanomalitetsdetekteringssystem.

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Ingham, James. "A domain-specific language based approach to component composition, error-detection, and fault prediction." Thesis, Durham University, 2001. http://etheses.dur.ac.uk/3954/.

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Current methods of software production are resource-intensive and often require a number of highly skilled professionals. To develop a well-designed and effectively implemented system requires a large investment of resources, often numbering into millions of pounds. The time required may also prove to be prohibitive. However, many parts of the new systems being currently developed already exist, either in the form of whole or parts of existing systems. It is therefore attractive to reuseexisting code when developing new software, in order to reduce the time andresources required. This thesis proposes the application of a domain-specific language (DSL) to automatic component composition, testing and fault-prediction. The DSL ISinherently based on a domain-model which should aid users of the system m knowing how the system is structured and what responsibilities the system fulfils. The DSL structure proposed in this thesis uses a type system and grammar hence enabling the early detection of syntactically incorrect system usage. Each DSL construct's behaviour can also be defined in a testing DSL, described here as DSL-test. This can take the form of input and output parameters, which should suffice for specifying stateless components, or may necessitate the use of a special method call, described here as a White-Box Test (WBT), which allows the external observer to view the abstract state of a component. Each DSL-construct can be mapped to its implementing components i.e. the component, or amalgamation of components, that implement(s) the behaviour as prescribed by the DSL-construct. User-requirements are described using the DS Land appropriate implementing components (if sufficient exist) are automatically located and integrated. That is to say, given a requirement described in terms of the DSL and sufficient components, the architecture (which was named Hydra) will be able to generate an executable which should behave as desired. The DSL-construct behaviour description language (DSL-test) is designed in such a way that it can be translated into a computer programming language, and so code can be inserted between the system automatically to verify that the implementing component is acting in a way consistent with the model of its expected behaviour. Upon detection of an error, the system examines available data (i.e. where the error occurred, what sort of error was it, and what was the structure of the executable), to attempt to predict the location of the fault and, where possible, make remedialaction. A number of case studies have been investigated and it was found that, if applied to the appropriate problem domain, the approach proposed in this thesis shows promise in terms of full automation and integration of black-box or grey-box software. However, further work is required before it can be claimed that this approach should be used in real scale systems.

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Sundberg, Jesper. "Anomaly Detection in Diagnostics Data with Natural Fluctuations." Thesis, KTH, Optimeringslära och systemteori, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170237.

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In this thesis, the red hot topic anomaly detection is studied, which is a subtopic in machine learning. The company, Procera Networks, supports several broadband companies with IT-solutions and would like to detected errors in these systems automatically. This thesis investigates and devises methods and algorithms for detecting interesting events in diagnostics data. Events of interest include: short-term deviations (a deviating point), long-term deviations (a distinct trend) and other unexpected deviations. Three models are analyzed, namely Linear Predictive Coding, Sparse Linear Prediction and Wavelet Transformation. The final outcome is determined by the gap to certain thresholds. These thresholds are customized to fit the model as well as possible. I den här rapporten kommer det glödheta området anomalidetektering studeras, vilket tillhör ämnet Machine Learning. Företaget där arbetet utfördes på heter Procera Networks och jobbar med IT-lösningar inom bredband till andra företag. Procera önskar att kunna upptäcka fel hos kunderna i dessa system automatiskt. I det här projektet kommer olika metoder för att hitta intressanta företeelser i datatraffiken att genomföras och forskas kring. De mest intressanta företeelserna är framfärallt snabba avvikelser (avvikande punkt) och färändringar äver tid (trender) men också andra oväntade mänster. Tre modeller har analyserats, nämligen Linear Predictive Coding, Sparse Linear Prediction och Wavelet Transform. Det slutgiltiga resultatet från modellerna är grundat på en speciell träskel som är skapad fär att ge ett så bra resultat som mäjligt till den undersäkta modellen..

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Williams, Darren Thomas. "Dynamic modelling of a linear friction welding machine actuation system for fault detection and prediction." Thesis, University of Bath, 2013. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604889.

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Linear Friction Welding (LFW) is a relatively new process adopted by aircraft engine manufacturers utilising new technologies to produce better value components. With increasing fuel prices and economical drives for reducing CO2 emissions, LFW has been a key technology in recent years for aircraft engine manufacture in both commercial and military market sectors. For joining Blades to Discs (‘Blisks’), LFW is the ideal process as it is a solid state process which gives reproducibility and high quality bonds therefore improving performance. The welding process is also more cost effective than machining Blisks from solid billets, and a reduction in weight can also be achieved with the use of hollow blades. The LFW process also allows dissimilar materials to be joined and a reduction in assembly time. The main aim of the research is to create a simulation model of a Linear Friction Welding machine and also apply systems thinking to fully understand the LFW process with a view to reduce total production costs. As this EngD focuses on systems thinking, a holistic approach will be used. The hard systems parts of this project will involve the mechanics of the system and understanding relationships between the key system interactions during the welding process in order to create an analytical model of the machine to use for fault diagnosis and prediction. The soft systems parts will focus on the machine users to gain an understanding of how to effectively implement the model with the process and its users. The benefits of the new model include the ability to execute it in a real- time environment with machine operation, allowing weld anomalies to be detected as (and in some cases before) they occur, as well as the monitoring of the machine’s condition. Therefore the business benefits would be realised through a reduction in machine downtime enabling the timely supply of goods providing customer value. Further benefits will be the greater understanding of the complex operation of the whole system and the welding process. Developing a robust research investigation framework, a research hypothesis is introduced and subsequent research questions are developed. Through a combination of hard system investigation using mathematical modelling and soft systems understanding through an action case study intervention, a holistic model is developed.

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Bergentz, Tobias. "Identifying symptoms of fault in District Heating Substations : An investigation in how a predictive heat load software can help with fault detection." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-174442.

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District heating delivers more than 70% of the energy used for heating and domestichot water in Swedish buildings. To stay competitive, district heating needs toreduce its losses and increase capabilities to utilise low grade heat. Finding faultysubstations is one way to allow reductions in supply temperatures in district heatingnetworks, which in turn can help reduce the losses. In this work three suggestedsymptoms of faults: abnormal quantization, drifting and anomalous values, are investigatedwith the help of hourly meter data of: heat load, volume flow, supplyand return temperatures from district heating substations. To identify abnormalquantization, a method is proposed based on Shannon’s entropy, where lower entropysuggests higher risk of abnormal quantization. The majority of the substationsidentified as having abnormal quantization with the proposed method has a meterresolution lower than the majority of the substations in the investigated districtheating network. This lower resolution is likely responsible for identifying thesesubstation, suggesting the method is limited by the meter resolution of the availabledata. To improve result from the method higher resolution and sampling frequencyis likely needed.For identifying drift and anomalous values two methods are proposed, one for eachsymptom. Both methods utilize a software for predicting hourly heat load, volumeflow, supply and return temperatures in individual district heating substations.The method suggested for identifying drift uses the mean value of each predictedand measured quantity during the investigated period. The mean of the prediction iscompared to the mean of the measured values and a large difference would suggestrisk of drift. However this method has not been evaluated due to difficulties infinding a suitable validation method.The proposed method for detecting anomalous values is based on finding anomalousresiduals when comparing the prediction from the prediction software to themeasured values. To find the anomalous residuals the method uses an anomalydetection algorithm called IsolationForest. The method produces rankable lists inwhich substations with risk of anomalies are ranked higher in the lists. Four differentlists where evaluated by an experts. For the two best preforming lists approximatelyhalf of the top 15 substations where classified to contain anomalies by the expertgroup. The proposed method for detecting anomalous values shows promising resultespecially considering how easily the method could be added to a district heatingnetwork. Future work will focus on reducing the number of false positives. Suggestionsfor lowering the false positive rate include, alternations or checks on theprediction models used.

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Piretti, Andrea. "Fault Detection in Industry 4.0 with Deep Learning Approaches." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22368/.

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Con il costante aumento dell'utilizzo di macchinari automatici in ambito industriale, nasce la ricerca della creazione di sistemi in grado garantire ottime prestazioni e tolleranza ai comportamenti anomali di essi. L'obbiettivo di questa tesi è la realizzazione di modelli di Machine Learning in grado di svolgere operazioni di Anomaly Detection per la classificazione di comportamenti sbagliati da parte di questo tipo di macchinari mediante l'utilizzo di un AutoEncoder con un approccio di Semi-Supervised learning. Attraverso i risultati di questi modelli sarà poi possibile svolgere un'ampia analisi sulle ragioni di questi comportamenti errati e fare predizioni di essi in modo da avere una tolleranza maggiore ai guasti sulla macchina.

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Mohamed, Ahmed. "Fault-detection in Ambient Intelligence based on the modeling of physical effects." Phd thesis, Supélec, 2013. http://tel.archives-ouvertes.fr/tel-00995066.

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This thesis takes place in the field of Ambient Intelligence (AmI). AmI Systems are interactive systems composed of many heterogeneous components. From a hardware perspective these components can be divided into two main classes: sensors, using which the system observes its surroundings, and actuators, through which the system acts upon its surroundings in order to execute specific tasks.From a functional point of view, the goal of AmI Systems is to activate some actuators, based on data provided by some sensors. However, sensors and actuators may suffer failures. Our motivation in this thesis is to equip ambient systems with self fault detection capabilities. One of the particularities of AmI systems is that instances of physical resources (mainly sensors and actuators) are not necessarily known at design time; instead they are dynamically discovered at run-time. In consequence, one could not apply classical control theory to pre-determine closed control loops using the available sensors. We propose an approach in which the fault detection and diagnosis in AmI systems is dynamically done at run-time, while decoupling actuators and sensors at design time. We introduce a Fault Detection and Diagnosis framework modeling the generic characteristics of actuators and sensors, and the physical effects that are expected on the physical environment when a given action is performed by the system's actuators. These effects are then used at run-time to link actuators (that produce them) with the corresponding sensors (that detect them). Most importantly the mathematical model describing each effect allows the calculation of the expected readings of sensors. Comparing the predicted values with the actual values provided by sensors allows us to achieve fault-detection.

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Verma, Anoop Prakash. "Performance monitoring of wind turbines : a data-mining approach." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3398.

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The rapid growth of wind turbines in terms of turbine size, number of installations and rated capacity has a huge impact on its operations and maintenance costs. Monitoring the performance of wind turbines and early fault prediction is highly desirable. To date, traditional maintenance strategies such as reactive maintenance, periodic maintenance etc. are more prevalent in wind industry. However, over the last couple of years, the research pertaining to wind turbine has been shifted towards the condition monitoring and maintenance. Condition monitoring approaches have shown their potential in wind industry by providing continuous monitoring of the wind turbines, and identifying fault signatures in the event of faults. However, most of the studies reported in literature are based on the simulated dataset, or in constrained experiments. In reality, the external environment plays an important role in governing the turbine operations. Moreover, the cost associated with condition monitoring cannot be justified as it often requires installations of specific sensors, equipment. Another stream of research focuses on utilizing historical turbine data for turbine performance assessment in real time. The cost associated with such approaches is almost negligible as most of the wind farms are equipped with SCADA systems which records turbine performance data in regular time-interval. Such approaches are called as performance monitoring. In this dissertation, the performance monitoring of wind turbines is accomplished using the historical wind turbine data. The information from SCADA operational data, and fault logs is used to construct accurate models predicting the critical wind turbine faults. Depending upon the nature of turbine faults, monitoring wind turbines with different objectives is studied to accomplish different research goals. Two research directions of wind turbines performance are pursued, (1) identification and prediction of critical turbine faults, and (2) monitoring the performance of overall wind farm. The goal of predicting critical faults is to facilitate planned maintenance, whereas, monitoring the performance of overall wind farm provides the status-quo of all wind turbines installed in a wind farm. Depending on the requirement, the performance of overall wind farm can be assessed on a daily, weekly, or monthly basis. Solution methodologies presented in the dissertation are generic enough to be applicable to other industries such as wastewater treatment facilities, flood prediction, etc.

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Pereira, Cássio Martini Martins. "Detecção de faltas: uma abordagem baseada no comportamento de processos." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-12052011-141404/.

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A diminuição no custo de computadores pessoais tem favorecido a construção de sistemas computacionais complexos, tais como aglomerados e grades. Devido ao grande número de recursos existentes nesses sistemas, a probabilidade de que faltas ocorram é alta. Uma abordagem que auxilia a tornar sistemas mais robustos na presença de faltas é a detecção de sua ocorrência, a fim de que processos possam ser reiniciados em estados seguros, ou paralisados em estados que não ofereçam riscos. Abordagens comumente adotadas para detecção seguem, basicamente, três tipos de estratégias: as baseadas em mensagens de controle, em estatística e em aprendizado de máquina. No entanto, elas tipicamente não consideram o comportamento de processos ao longo do tempo. Observando essa limitação nas pesquisas relacionadas, este trabalho apresenta uma abordagem para medir a variação no comportamento de processos ao longo do tempo, a fim de que mudanças inesperadas sejam detectadas. Essas mudanças são consideradas, no contexto deste trabalho, como faltas, as quais representam transições indesejadas entre estados de um processo e podem levá-lo a processamento incorreto, fora de sua especificação. A proposta baseia-se na estimação de cadeias de Markov que representam estados visitados por um processo durante sua execução. Variações nessas cadeias são utilizadas para identificar faltas. A abordagem proposta é comparada à técnica de aprendizado de máquina Support Vector Machines, bem como à técnica estatística Auto-Regressive Integrated Moving Average. Essas técnicas foram escolhidas para comparação por estarem entre as mais empregadas na literatura. Experimentos realizados mostraram que a abordagem proposta possui, com erro \'alfa\' = 1%, um F-Measure maior do que duas vezes o alcançado pelas outras técnicas. Realizou-se também um estudo adicional de predição de faltas. Nesse sentido, foi proposta uma técnica preditiva baseada na reconstrução do comportamento observado do sistema. A avaliação da técnica mostrou que ela pode aumentar em até uma ordem de magnitude a disponibilidade (em horas) de um sistema The cost reduction for personal computers has enabled the construction of complex computational systems, such as clusters and grids. Because of the large number of resources available on those systems, the probability that faults may occur is high. An approach that helps to make systems more robust in the presence of faults is their detection, in order to restart or stop processes in safe states. Commonly adopted approaches for detection basically follow one of three strategies: the one based on control messages, on statistics or on machine learning. However, they typically do not consider the behavior of processes over time. Observing this limitation in related researches, this work presents an approach to measure the level of variation in the behavior of processes over time, so that unexpected changes are detected. These changes are considered, in the context of this work, as faults, which represent undesired transitions between process states and may cause incorrect processing, outside the specification. The approach is based on the estimation of Markov Chains that represent states visited by a process during its execution. Variations in these chains are used to identify faults. The approach is compared to the machine learning technique Support Vector Machines, as well as to the statistical technique Auto-Regressive Integrated Moving Average. These techniques have been selected for comparison because they are among the ones most employed in the literature. Experiments conducted have shown that the proposed approach has, with error \'alpha\'= 1%, an F-Measure higher than twice the one achieved by the other techniques. A complementary study has also been conducted about fault prediction. In this sense, a predictive approach based on the reconstruction of system behavior was proposed. The evaluation of the technique showed that it can provide up to an order of magnitude greater availability of a system in terms of uptime hours

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Ayad, Fady. "How is AI research applied in the field of network fault management." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20124.

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The internet growth rapidly increased by the years, and the traffic is increasing daily. The management of the network is becoming more and more complexed for humans to handle on their own, with that being said a new direction of using Artificial Intelligence (AI) technologies is being implemented in the direction of network fault management. In order to keep up with the development network, new solutions need to be implemented. Traditional network fault management are dependent of system administrators and there is too much human error that can happen during operations. That’s why AI is a great tool to be used in future network fault management. There are currently many challenges within network fault management, and this makes an opportunity for AI to be implemented. The studies shows that AI subpart “supervised learning” is the most popular used in network fault management. AI have shown that there is potential to tackle problems such as detection, prediction and also improve the system as whole.

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Feng, Dawei. "Efficient end-to-end monitoring for fault management in distributed systems." Phd thesis, Université Paris Sud - Paris XI, 2014. http://tel.archives-ouvertes.fr/tel-01017083.

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In this dissertation, we present our work on fault management in distributed systems, with motivating application roots in monitoring fault and abrupt change of large computing systems like the grid and the cloud. Instead of building a complete a priori knowledge of the software and hardware infrastructures as in conventional detection or diagnosis methods, we propose to use appropriate techniques to perform end-to-end monitoring for such large scale systems, leaving the inaccessible details of involved components in a black box.For the fault monitoring of a distributed system, we first model this probe-based application as a static collaborative prediction (CP) task, and experimentally demonstrate the effectiveness of CP methods by using the max margin matrix factorization method. We further introduce active learning to the CP framework and exhibit its critical advantage in dealing with highly imbalanced data, which is specially useful for identifying the minority fault class.Further we extend the static fault monitoring to the sequential case by proposing the sequential matrix factorization (SMF) method. SMF takes a sequence of partially observed matrices as input, and produces predictions with information both from the current and history time windows. Active learning is also employed to SMF, such that the highly imbalanced data can be coped with properly. In addition to the sequential methods, a smoothing action taken on the estimation sequence has shown to be a practically useful trick for enhancing sequential prediction performance.Since the stationary assumption employed in the static and sequential fault monitoring becomes unrealistic in the presence of abrupt changes, we propose a semi-supervised online change detection (SSOCD) framework to detect intended changes in time series data. In this way, the static model of the system can be recomputed once an abrupt change is detected. In SSOCD, an unsupervised offline method is proposed to analyze a sample data series. The change points thus detected are used to train a supervised online model, which gives online decision about whether there is a change presented in the arriving data sequence. State-of-the-art change detection methods are employed to demonstrate the usefulness of the framework.All presented work is verified on real-world datasets. Specifically, the fault monitoring experiments are conducted on a dataset collected from the Biomed grid infrastructure within the European Grid Initiative, and the abrupt change detection framework is verified on a dataset concerning the performance change of an online site with large amount of traffic.

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Stenudd, Joakim. "Development of method for early fault detection in small planetary gear sets in nutrunners." Thesis, Luleå tekniska universitet, Maskinelement, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83227.

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The objective of this thesis work was to develop a method to detect early damage on small planetary gear sets that are installed in Atlas Copco nutrunners. The project has gone through several stages of product development, from idea to working product and signal analysis. Currently, Atlas Copco have a test rig for testing these planetary gears, this rig has been proven to be insufficient at detecting faults during an ongoing test. A new tailored test rig was therefore designed and manufactured. Low noise and low amount of vibration was of interest when designing the rig. Four concepts was thought of and evaluated through simulations using Matlab and Simulink. Most of the components of the rig were manufactured in the workshop at Atlas Copco in Nacka. Methods fo rmeasuring torsional, transverse and acoustic vibration was implemented and analyzed. There are many different parameters considering fault of fixed shaft gears. However, these are not easily applicable on a planetary gear because of the nature of its design. Therefore, special techniques are required. Two “new” parameters were tested (NSDS,FRMS [Lei. et al.]) with positive results. Pitting of individual gear members could befound.

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Bin, Hasan M. M. A. "Current based condition monitoring of electromechanical systems : model-free drive system current monitoring : faults detection and diagnosis through statistical features extraction and support vector machines classification." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5732.

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A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severity

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Shaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.

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The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of the response time. This thesis proposes a detection algorithm that utilizes RNN in predicting sensor drifts from time-series data streams. The detection algorithm was later named; Predictive Sliding Detection Window (PSDW) and consisted of both forecasting and classification models. Three different RNN algorithms, i.e., LSTM, CNN-LSTM, and GRU, were designed to predict sensor drifts using forecasting and classification techniques. The algorithms were compared against each other in terms of relevant accuracy metrics for forecasting and classification. The operability of the solution was investigated by developing a web server that hosted the PSDW algorithm on an AWS computing instance. The resulting forecasting and classification algorithms were able to make reasonably accurate predictions for this particular scenario. More specifically, the forecasting algorithms acquired relatively low RMSE values as ~0.6, while the classification algorithms obtained an average F1-score and accuracy of ~80% but with a high standard deviation. However, the response time was ~5700% slower during the simulation of the HTTP requests. The obtained results suggest the need for future investigations to improve the accuracy of the models and experiment with other computing paradigms for more reliable deployments.

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Xu, Feng. "Diagnosis and fault-tolerant control using set-based methods." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/284831.

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The fault-tolerant capability is an important performance specification for most of technical systems. The examples showing its importance are some catastrophes in civil aviation. According to some official investigations, some air incidents are technically avoidable if the pilots can take right measures. But, relying on the skill and experience of the pilots, it cannot be guaranteed that reliable flight decisions are always made. Instead, if fault-tolerant strategies can be included in the decision-making procedure, it will be very useful for safer flight. Fault-tolerant control is generally classified into passive and active fault-tolerant control. Passive fault-tolerant control relies on robustness of controller, which can only provide limited fault-tolerant ability, while active fault-tolerant control turns to a fault detection and isolation module to obtain fault information and then actively take actions to tolerate the effect of faults. Thus, active fault-tolerant control generally has stronger fault-tolerant ability. In this dissertation, one focuses on active fault-tolerant control, which for this case considers model predictive control and set-based fault detection and isolation. Model predictive control is a successful advanced control strategy in process industry and has been widely used for processes such as chemistry and water treatment, because of its ability to deal with multivariable constrained systems. However, the performance of model redictive control has deep dependence on system-model accuracy. Realistically, it is impossible to avoid the effect of modelling errors, disturbances, noises and faults, which always result in model mismatch. Comparatively, model mismatch induced by faults is possible to be effectively handled by suitable fault-tolerant strategies. The objective of this dissertation is to endow model predictive control with fault-tolerant ability to improve its effectiveness. In order to reach this objective, set-based fault detection and isolation methods are used in the proposed fault-tolerant schemes. The important advantage of set-based fault detection and isolation is that it can make robust fault detection and isolation decisions, which is key for taking right fault-tolerant measures. This dissertation includes four parts. The first part introduces this research, presents the state of the art and gives an introduction of used research tools. The second part proposes set-based fault detection and isolation for actuator or=and sensor faults, which are involved in interval observers, invariant sets and set-membership estimation. First, the relationship between interval observers and invariant sets is investigated. Then, actuator and sensor faults are separately coped with depending on their own features. The third part focuses on actuator or=and sensor fault-tolerant model predictive control, where the control strategy is robust model predictive control (tube-based and min-max approaches). The last part draws some conclusions, summarizes this research and gives clues for the further work. La capacidad de los sistemas para tolerar fallos es una importante especificación de desempeño para la mayoría de sistemas. Ejemplos que muestran su importancia son algunas catástrofes en aviación civil. De acuerdo a investigaciones oficiales, algunos incidentes aéreos son técnicamente evitables si los pilotos pudiesen tomar las medidas adecuadas. Aun así, basándose en las habilidades y experiencia de los pilotos, no se puede garantizar que decisiones de vuelo confiables serán siempre posible de tomar. En cambio, si estrategias de tolerancia a fallos se pudieran incluir en el proceso de toma de decisión, los vuelos serían mucho más seguros. El control tolerante a fallos es generalmente clasificado en control pasivo y activo. El control pasivo se basa en la robustez del controlador, el cual sólo provee una habilidad limitada de tolerancia a fallos, mientras que el control tolerante a fallos de tipo activo se convierte en un modulo de detección y aislamiento de fallos que permite obtener información de éstos, y luego, activamente, tomar acciones para tolerar el efecto de dichos fallos. Así pues, el control activo generalmente tiene habilidades más fuertes de tolerancia a fallos. Esta tesis se enfoca en control tolerante a fallos activo, para lo cual considera el control predictivo basado en modelos y la detección y aislamiento de fallos basados en conjuntos. El control predictivo basado en modelos es una estrategia de control exitosa en la industria de procesos y ha sido ampliamente utilizada para procesos químicos y tratamiento de aguas, debido a su habilidad de tratar con sistemas multivariables con restricciones. A pesar de esto, el desempeño del control predictivo basado en modelos tiene una profunda dependencia de la precisión del modelo del sistema. Siendo realistas, es imposible evitar el efecto de errores de modelado, perturbaciones, ruidos y fallos, que siempre llevan a diferencias entre el modelo y el sistema real. Comparativamente, el error de modelo inducido por los fallos es posible de ser manejado efectivamente por estrategias adecuadas de control tolerante a fallos. Con el fin de alcanzar este objetivo, métodos de detección y aislamiento de fallos basados en conjuntos son utilizados en los esquemas de tolerancia a fallos propuestos en esta tesis. La ventaja importante de estas técnicas de detección y aislamiento de fallos basadas en conjuntos es que puede tomar decisiones robustas de detección y aislamiento, lo cual es clave para tomar medidas acertadas de tolerancia a fallos. Esta tesis esta dividida en cuatro partes. La primera parte es introductoria, presenta el estado del arte y hace una introducción a las herramientas de investigación utilizadas. La segunda parte expone la detección y aislamiento de fallos en actuadores y/o sensores, basándose en teoría de conjuntos, a partir de observadores de intervalo, y conjuntos invariantes. La tercera parte se enfoca en el control predictivo robusto (con enfoques basados tanto en tubos robustos como en min-max) con tolerancia a fallos en actuadores y/o sensores. La cuarta parte presenta algunas conclusiones, hace un resumen de esta investigación y da algunas ideas para trabajos futuros.

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Chen, Kunru. "Recurrent Neural Networks for Fault Detection : An exploratory study on a dataset about air compressor failures of heavy duty trucks." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38184.

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Olivier, Laurentz Eugene. "On lights-out process control in the minerals processing industry." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/59322.

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The concept of lights-out process control is explored in this work (specifically pertaining to the minerals processing industry). The term is derived from lights-out manufacturing, which is used in discrete component manufacturing to describe a fully automated production line, i.e. with no human intervention. Lights-out process control is therefore defined as the fully autonomous operation of a processing plant (as achieved through automatic process control), without operator interaction. Thesis (PhD)--University of Pretoria, 2017. National Research Foundation (NRF) Electrical, Electronic and Computer Engineering PhD Unrestricted

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Shahi, Durlabh, and Ankit Gupta. "Forecasting Components Failure Using Ant Colony Optimization For Predictive Maintenance." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42457.

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Failures are the eminent aspect of any machine and so is true for vehicle as it is one of the sophisticated machines of today’s time. Early detection of faults and prioritized maintenance is a necessity of vehicle manufactures as it enables them to reduce maintenance cost and increase customer satisfaction. In our research, we have proposed a method for processing Logged Vehicle Data (LVD) that uses Ant-Miner algorithm which is a Ant Colony Optimization (ACO) based Algorithm. It also utilizes processes like Feature engineering, Data preprocessing. We tried to explore the effectiveness of ACO for solving classification problem in the form of fault detection and prediction of failures which would be used for predictive maintenance by manufacturers. From the seasonal and yearly model that we have created, we have used ACO to successfully predict the time of failure which is the month with highest likelihood of failure in vehicle’s components. Here, we also validated the obtained results. LVD suffers from data imbalance problem and we have implemented balancing techniques to eliminate this issue, however more effective balancing techniques along with feature engineering is required to increase accuracy in prediction.

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Lembke, Benjamin. "Bearing Diagnosis Using Fault Signal Enhancing Teqniques and Data-driven Classification." Thesis, Linköpings universitet, Fordonssystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158240.

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Rolling element bearings are a vital part in many rotating machinery, including vehicles. A defective bearing can be a symptom of other problems in the machinery and is due to a high failure rate. Early detection of bearing defects can therefore help to prevent malfunction which ultimately could lead to a total collapse. The thesis is done in collaboration with Scania that wants a better understanding of how external sensors such as accelerometers, can be used for condition monitoring in their gearboxes. Defective bearings creates vibrations with specific frequencies, known as Bearing Characteristic Frequencies, BCF [23]. A key component in the proposed method is based on identification and extraction of these frequencies from vibration signals from accelerometers mounted near the monitored bearing. Three solutions are proposed for automatic bearing fault detection. Two are based on data-driven classification using a set of machine learning methods called Support Vector Machines and one method using only the computed characteristic frequencies from the considered bearing faults. Two types of features are developed as inputs to the data-driven classifiers. One is based on the extracted amplitudes of the BCF and the other on statistical properties from Intrinsic Mode Functions generated by an improved Empirical Mode Decomposition algorithm. In order to enhance the diagnostic information in the vibration signals two pre-processing steps are proposed. Separation of the bearing signal from masking noise are done with the Cepstral Editing Procedure, which removes discrete frequencies from the raw vibration signal. Enhancement of the bearing signal is achieved by band pass filtering and amplitude demodulation. The frequency band is produced by the band selection algorithms Kurtogram and Autogram. The proposed methods are evaluated on two large public data sets considering bearing fault classification using accelerometer data, and a smaller data set collected from a Scania gearbox. The produced features achieved significant separation on the public and collected data. Manual detection of the induced defect on the outer race on the bearing from the gearbox was achieved. Due to the small amount of training data the automatic solutions were only tested on the public data sets. Isolation performance of correct bearing and fault mode among multiplebearings were investigated. One of the best trade offs achieved was 76.39 % fault detection rate with 8.33 % false alarm rate. Another was 54.86 % fault detection rate with 0 % false alarm rate.

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Romano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.

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In questo lavoro di tesi è stato analizzato l'avvento dell'industria 4.0 all'interno dell' industria nel settore packaging. In particolare, è stata discussa l'importanza della diagnostica predittiva e sono stati analizzati e testati diversi approcci per la determinazione di modelli descrittivi del problema a partire dai dati. Inoltre, sono state applicate le principali tecniche di Machine Learning in modo da classificare i dati analizzati nelle varie classi di appartenenza.

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Medeiros, Juliana Pegado de. "Estudo e implementa??o de algoritmos inteligentes para detec??o e classifica??o de falhas na medi??o de g?s natural." Universidade Federal do Rio Grande do Norte, 2009. http://repositorio.ufrn.br:8080/jspui/handle/123456789/12895.

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Made available in DSpace on 2014-12-17T14:08:33Z (GMT). No. of bitstreams: 1 JulianaPM.pdf: 4255756 bytes, checksum: 0f65b2b3a4f0afafcf55cda7d138bb36 (MD5) Previous issue date: 2009-06-29 This master dissertation presents the study and implementation of inteligent algorithms to monitor the measurement of sensors involved in natural gas custody transfer processes. To create these algoritmhs Artificial Neural Networks are investigated because they have some particular properties, such as: learning, adaptation, prediction. A neural predictor is developed to reproduce the sensor output dynamic behavior, in such a way that its output is compared to the real sensor output. A recurrent neural network is used for this purpose, because of its ability to deal with dynamic information. The real sensor output and the estimated predictor output work as the basis for the creation of possible sensor fault detection and diagnosis strategies. Two competitive neural network architectures are investigated and their capabilities are used to classify different kinds of faults. The prediction algorithm and the fault detection classification strategies, as well as the obtained results, are presented Esta disserta??o apresenta o estudo e implementa??o de algoritmos inteligentes para o monitoramento da medi??o de sensores envolvidos em processos de transfer?ncia de cust?dia de g?s natural. Para a cria??o destes algoritmos s?o investigadas arquiteturas de Redes Neurais Artificiais devido a caracter?sticas particulares, tais como: aprendizado, adapta??o e predi??o. Um preditor ? implementado com a finalidade de reproduzir o comportamento din?mico da sa?da de um sensor de interesse, de tal forma que sua sa?da seja comparada ? sa?da real do sensor. Uma rede recorrente ? utilizada para este fim, em virtude de sua capacidade em lidar com informa??o din?mica. A sa?da real do sensor e a sa?da estimada do preditor formam a base para a cria??o das estrat?gias de detec??o e identifica??o de poss?veis falhas. Duas arquiteturas de redes neurais competitivas s?o investigadas e suas potencialidades s?o utilizadas para classificar tipos diferentes de falhas. O algoritmo de predi??o e as estrat?gias de detec??o e classifica??o de falhas, bem como os resultados obtidos, ser?o apresentados

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Feroldi, Diego Hernán. "Control and design of pem fuel cell-based systems." Doctoral thesis, Universitat Politècnica de Catalunya, 2009. http://hdl.handle.net/10803/5958.

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Las pilas de combustible son muy ventajosas debido a su alta eficiencia en la conversión de energía y nula contaminación. En esta tesis se realiza un extenso estudio sobre el control y diseño de sistemas de generación eléctrica basados en pilas de combustible. El núcleo principal de la misma son los sistemas híbridos con pilas de combustible y supercapacitores como elementos almacenadores de energía, orientado a aplicaciones automotrices. La determinación del Grado de Hibridización (i.e. la determinación del tamaño de la pila de combustible y del número de supercapacitores) se realiza mediante una metodología propuesta con el objetivo de satisfacer requisitos de conductibilidad y consumiendo la menor cantidad de hidrógeno posible. El proceso de diseño comienza con la determinación de la estructura eléctrica de generación del vehículo y utiliza un modelo detallado realizado en ADVISOR, una herramienta para modelado y estudio de sistemas híbridos. Se analiza el flujo de energía a través de los componentes del vehículo cuando el vehículo sigue diferentes ciclos de conducción estándares, mostrando las pérdidas en cada componente que degradan la eficiencia del sistema y limitan la recuperación de energía de frenado. Con respecto a la recuperación de energía, se ha definido y analizado un parámetro que cuantifica la cantidad de energía que realmente es reaprovechada: el ratio frenado/hidrógeno. Para controlar el flujo de energía entre la pila de combustible, los almacenadores de energía y la carga eléctrica, se proponen tres Estrategias de Gestión de Energía (EMS) para Vehículos Híbridos con Pila de Combustible (FCHVs) basadas en el mapa de eficiencia de la pila y se validan mediante un montaje experimental desarrollado para emular el sistema híbrido. Los resultados de consumo de hidrógeno son comparados con dos referencias: el consumo correspondiente al caso del vehículo sin hibridización y el caso óptimo con el menor consumo para el vehículo propuesto. El consumo óptimo se calcula mediante una metodología propuesta que, a diferencia de otras, evita la discretización de las variables de estado. Para operar el sistema eficientemente, la pila de combustible es controlada mediante una metodología de control, basada en Control de Matriz Dinámica (DMC). Esta metodología de control utiliza como variables de control el voltaje de compresor y una nueva variable propuesta: la apertura de una válvula proporcional ubicada a la salida del cátodo. Los objetivos de control son controlar el exceso de oxígeno en el cátodo y el voltaje generado por la pila. Se analiza tanto en régimen estacionario como transitorio las ventajas de emplear esta nueva variable de control y se muestran resultados de funcionamiento por simulación del controlador ante perturbaciones en la corriente de carga. Por otro lado, se aborda el diagnóstico y el control tolerante a fallos del sistema basado en pila de combustible proponiendo una metodología de diagnóstico basada en las sensibilidades relativas de los fallos y se muestra que la estructura de control con las dos variables propuestas tiene buena capacidad de rechazo a fallos en el compresor cuando se controla el exceso de oxígeno en el cátodo. The use of fuel cell systems based on hydrogen is advantageous because of their high efficiency in the energy conversion and null emissions. In this thesis, an extensive study about the control and design of electrical generation systems based on fuel cells is performed. The main focus is in hybrid systems composed of fuel cells and supercapacitors as energy storage elements, oriented to automotive applications. The determination of the hybridization degree (i.e. the determination of the fuel cell size and the number of supercapacitors) is performed through a proposed methodology with the objective to fulfil the conductibility requirements and to consume the lowest amount of hydrogen. The process of design starts with the determination of the electrical structure and utilizes a detail model developed using ADVISOR, a MATLAB toolbox for modelling and studying hybrid vehicles. The energy flow between the vehicle components is analyzed when the vehicle is tested with different Standard Driving Cycles, showing how the losses in each component degrade the efficiency of the system and limit the energy recovery from braking. With regard to the energy recovery, a parameter to quantify the amount of energy that is actually reused is defined and analyzed: the braking/hydrogen ratio. To control the energy flow between the fuel cell, the energy storage system, and the electrical load in Fuel Cell Hybrid Vehicles (FCHVs), three Energy Management Strategies (EMSs) based on the fuel cell efficiency map are presented and validated through an experimental setup, which is developed to emulate the FCHV. The resulting hydrogen consumptions are compared with two references: the consumption of the pure fuel cell case, a vehicle without hybridization, and the optimal case with the minimum consumption. The optimal consumption for a given vehicle is determined through a methodology proposed that, unlike other previous methodologies, avoids the discretization of the state variables. To operate the fuel cell system efficiently, the system is controlled through a proposed control technique, which is based on Dynamic Matrix Control (DMC). This control technique utilizes the compressor voltage as control variable and also a new proposed variable: the opening area of a proportional valve at the cathode outlet. The control objectives are the control of the oxygen excess ratio at the cathode and the fuel cell voltage. The advantages of this new control variable are analyzed both in steady state and transient state. Simulation results show and adequate performance of the controller when a series of step changes in the load current is applied. On the other hand, the diagnosis and fault-tolerant control of the fuel cell-based system is approached. A diagnosis methodology based on the relative fault sensitivity is proposed. The performance of the methodology to detect and isolate a set of proposed failures is analyzed and simulation results in an environment developed to include the set of faults are given. The fault-tolerant control is approached showing that the proposed control structure with two control variables has good capability against faults in the compressor when the oxygen excess ratio in the cathode is controlled.

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Dode, Albi. "EFFECTIVENESS OF FAULT PREDICTION." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-39671.

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The research community in software engineering is trying to find a way on how to achieve the goal of having a fault-free software. The industry that will use a near fault-free software will have it easier to lower the costs of maintenance and the versions of delivered software will be more qualitative. In this case, fault prediction can be used in order to achieve the above objectives. Fully applied fault prediction is not yet achieved on an industrial scale. There is some progress attained in the field during recent years. But knowing and understanding what available tools and algorithms regarding fault prediction can give is yet a goal to be achieved by the industry. In this thesis, two fault prediction algorithms and several metrics combinations are tested in an industrial and open source project. The main goal is to understand how much fault prediction is integrated and effective in a continuous delivery environment using real case scenarios. The manually collected data, from several versions and in different time periods were applied using two already present algorithms: Naive Bayes and Clustering. As a result, while the usage of this prediction depends on the company needs, further research in the field can be extended.

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Ersanilli, V. "Automotive tyre fault detection." Thesis, Coventry University, 2015. http://curve.coventry.ac.uk/open/items/77d0e8a8-9b9d-4535-b961-cab355f7e3ff/1.

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The focus of the work in this thesis is concerned with the investigation and development of indirect measurement techniques. The methodology adopted is a combination of practical experimental, analytical deductive reasoning and simulation studies. This has led to proposals for a number of indirect tyre pressure monitoring systems, which are able to detect pressure loss under specific circumstances. The outcome overall is a proposal for a new supervisory system comprising of a modular framework, allowing various algorithms and techniques to be implemented in a complementary manner as they emerge and data sources become available. A number of contributions to the field have been made, which to the knowledge of the author, provide potential for further algorithm development and are imminently applicable given the above. The methods include a tyre pressure diagnosis via a wheel angular velocity comparator, the development of a model-based tyre pressure diagnosis via application of an unknown input observer and a parameter estimation scheme, a model-based tyre pressure diagnosis approach via an enhanced Kalman filter configured to estimate states including the input, a model-based tyre pressure diagnosis via cautious least squares, an investigation and critique of the effects of the choice of sampling interval on discrete-time models and estimation thereof. It is considered, that the extensive literature review provides a valuable historic insight into the tyre fault detection problem. It is clear, from the development and testing of the algorithms (and also the literature), that no single indirect pressure detection method is able to reliably detect changes in all driving scenarios which the regulations typically stipulate (depending on jurisdiction). In the absence of any information about the road input, the majority of the detection work must be shouldered by the wheel angular velocity comparator algorithm. As image recognition and sensor technology develops, it becomes possible to make estimates about the road surface and this removes some of the uncertainty on the input of the model-based parameter estimation approaches. Further work is detailed which goes some way towards realising the next steps in a development cycle suitable for a vehicle manufacturer to take through to the implementation stage.

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Tutivén, Gálvez Christian. "Fault detection and fault tolerant control in wind turbines." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/663289.

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Renewable energy is an important sustainable energy in the world. Up to now, as an essential part of low emissions energy in a lot of countries, renewable energy has been important to the national energy security, and played a significant role in reducing carbon emissions. It comes from natural resources, such as wind, solar, rain, tides, biomass, and geothermal heat. Among them, wind energy is rapidly emerging as a low carbon, resource efficient, cost effective sustainable technology in the world. Due to the demand of higher power production installations with less environmental impacts, the continuous increase in size of wind turbines and the recently developed offshore (floating) technologies have led to new challenges in the wind turbine systems.Wind turbines (WTs) are complex systems with large flexible structures that work under very turbulent and unpredictable environmental conditions for a variable electrical grid. The maximization of wind energy conversion systems, load reduction strategies, mechanical fatigue minimization problems, costs per kilowatt hour reduction strategies, reliability matters, stability problems, and availability (sustainability) aspects demand the use of advanced (multivariable and multiobjective) cooperative control systems to regulate variables such as pitch, torque, power, rotor speed, power factors of every wind turbine, etc. Meanwhile, with increasing demands for efficiency and product quality and progressing integration of automatic control systems in high-cost and safety-critical processes, the fields of fault detection and isolation (FDI) and fault tolerant control (FTC) play an important role. This thesis covers the theoretical development and also the implementation of different FDI and FTC techniques in WTs. The purpose of wind turbine FDI systems is to detect and locate degradations and failures in the operation of WT components as early as possible, so that maintenance operations can be performed in due time (e.g., during time periods with low wind speed). Therefore, the number of costly corrective maintenance actions can be reduced and consequently the loss of wind power production due to maintenance operations is minimized. The objective of FTC is to design appropriate controllers such that the resulting closed-loop system can tolerate abnormal operations of specific control components and retain overall system stability with acceptable system performance. Different FDI and FTC contributions are presented in this thesis and published in different JCR-indexed journals and international conference proceedings. These contributions embrace a wide range of realistic WTs faults as well as different WTs types (onshore, fixed offshore, and floating). In the first main contribution, the normalized gradient method is used to estimate the pitch actuator parameters to be able to detect faults in it. In this case, an onshore WT is used for the simulations. Second contribution involves not only to detect faults but also to isolate them in the pitch actuator system. To achieve this, a discrete-time domain disturbance compensator with a controller to detect and isolate pitch actuator faults is designed. Third main contribution designs a super-twisting controller by using feedback of the fore-aft and side-to-side acceleration signals of the WT tower to provide fault tolerance capabilities to the WT and improve the overall performance of the system. In this instance, a fixed-jacket offshore WT is used. Throughout the aforementioned research, it was observed that some faults induce to saturation of the control signal leading to system instability. To preclude that problem, the fourth contribution of this thesis designs a dynamic reference trajectory based on hysteresis. Finally, the fifth and last contribution is related to floating-barge WTs and the challenges that this WTs face. The performance of the proposed contributions are tested in simulations with the aero-elastic code FAST. La energía renovable es una energía sustentable importante en el mundo. Hasta ahora, como parte esencial de la energía de bajas emisiones en muchos países, la energía renovable ha sido importante para la seguridad energética nacional, y jugó un papel importante en la reducción de las emisiones de carbono. Proviene de recursos naturales, como el viento, la energía solar, la lluvia, las mareas, la biomasa y el calor geotérmico. Entre ellos, la energía eólica está emergiendo rápidamente como una tecnología sostenible de bajo carbono, eficiente en el uso de los recursos y rentable en el mundo. Debido a la demanda de instalaciones de producción de mayor potencia con menos impactos ambientales, el aumento continuo en el tamaño de las turbinas eólicas y las tecnologías offshore (flotantes) recientemente desarrolladas han llevado a nuevos desafíos en los sistemas de turbinas eólicas. Las turbinas eólicas son sistemas complejos con grandes estructuras flexibles que funcionan en condiciones ambientales muy turbulentas e impredecibles para una red eléctrica variable. La maximización de los sistemas de conversión de energía eólica, los problemas de minimización de la fatiga mecánica, los costos por kilovatios-hora de estrategias de reducción, cuestiones de confiabilidad, problemas de estabilidad y disponibilidad (sostenibilidad) exigen el uso de sistemas avanzados de control cooperativo (multivariable y multiobjetivo) para regular variables tales como paso, par, potencia, velocidad del rotor, factores de potencia de cada aerogenerador, etc. Mientras tanto, con las crecientes demandas de eficiencia y calidad del producto y la progresiva integración de los sistemas de control automático en los procesos de alto costo y de seguridad crítica, los campos de detección y aislamiento de fallos (FDI) y control tolerante a fallos (FTC) juegan un papel importante. Esta tesis cubre el desarrollo teórico y también la implementación de diferentes técnicas de FDI y FTC en turbinas eólicas. El propósito de los sistemas FDI es detectar y ubicar las degradaciones y fallos en la operación de los componentes tan pronto como sea posible, de modo que las operaciones de mantenimiento puedan realizarse a su debido tiempo (por ejemplo, durante periodos con baja velocidad del viento). Por lo tanto, se puede reducir el número de costosas acciones de mantenimiento correctivo y, en consecuencia, se reduce al mínimo la pérdida de producción de energía eólica debido a las operaciones de mantenimiento. El objetivo de la FTC es diseñar controladores apropiados de modo que el sistema de bucle cerrado resultante pueda tolerar operaciones anormales de componentes de control específicos y retener la estabilidad general del sistema con un rendimiento aceptable del sistema. Diferentes contribuciones de FDI y FTC se presentan en esta tesis y se publican en diferentes revistas indexadas a JCR y en congresos internacionales. Estas contribuciones abarcan una amplia gama de fallos WTs realistas, así como diferentes tipos de turbinas (en tierra, en alta mar ancladas al fondo del mar y flotantes). El rendimiento de las contribuciones propuestas se prueba en simulaciones con el código aeroelástico FAST.

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Wang, Haibo, and 王海波. "Fault detection and fault-tolerant control for dynamic systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B42576842.

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Wang, Haibo. "Fault detection and fault-tolerant control for dynamic systems." Click to view the E-thesis via HKUTO, 2002. http://sunzi.lib.hku.hk/hkuto/record/B42576842.

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Nott, Paul Jonathan King. "Bioprocess modelling and fault detection." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310022.

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NILSSON, DAVID. "Fault detection in photovoltaic systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-153945.

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This master’s thesis concerns three different areas in the field of fault detection in photovoltaic systems.Previous studies have concerned homogeneous systems with a large set of parameters being observed,while this study is focused on a more restrictive case. The first problem is to discover immediate faults occurring in solar panels. A new online algorithm is developed based on similarity measures with in a single installation. It performs reliably and is able to detect all significant faults over a certain threshold. The second problem concerns measuring degradation over time. A modified approachis taken based on repetitive conditions, and performs well given certain assumptions. Finally the third problem is to differentiate solar panel faults from partial shading. Here a clustering algorithm DBSCAN is applied on data in order to locate clusters of faults in the solar plane, demonstrating good performance in certain situations. It also demonstrates issues with misclassification of real faults due to clustering

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Christensen, Anders Lyhne. "Fault detection in autonomous robots." Doctoral thesis, Universite Libre de Bruxelles, 2008. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210508.

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In this dissertation, we study two new approaches to fault detection for autonomous robots. The first approach involves the synthesis of software components that give a robot the capacity to detect faults which occur in itself. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data in three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots both while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of the number of false positives and the time it takes to detect a fault.The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot's sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task. Finally, we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.The second approach involves the use of firefly-inspired synchronization to allow the presence of faulty robots to be determined by other non-faulty robots in a swarm robotic system. We take inspiration from the synchronized flashing behavior observed in some species of fireflies. Each robot flashes by lighting up its on-board red LEDs and neighboring robots are driven to flash in synchrony. The robots always interpret the absence of flashing by a particular robot as an indication that the robot has a fault. A faulty robot can stop flashing periodically for one of two reasons. The fault itself can render the robot unable to flash periodically.Alternatively, the faulty robot might be able to detect the fault itself using endogenous fault detection and decide to stop flashing.Thus, catastrophic faults in a robot can be directly detected by its peers, while the presence of less serious faults can be detected by the faulty robot itself, and actively communicated to neighboring robots. We explore the performance of the proposed algorithm both on a real world swarm robotic system and in simulation. We show that failed robots are detected correctly and in a timely manner, and we show that a system composed of robots with simulated self-repair capabilities can survive relatively high failure rates.We conclude that i) fault injection and learning can give robots the capacity to detect faults that occur in themselves, and that ii) firefly-inspired synchronization can enable robots in a swarm robotic system to detect and communicate faults. Doctorat en Sciences de l'ingénieur info:eu-repo/semantics/nonPublished

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Iqbal, Ammar Tanange Rakesh Virk Shafqat. "Vehicle fault prediction analysis : a health prediction tool for heavy vehicles /." Göteborg : IT-universitetet, Chalmers tekniska högskola och Göteborgs universitet, 2006. http://www.ituniv.se/w/index.php?option=com_itu_thesis&Itemid=319.

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33

Rupp, Daniel. "Fault-tolerant control and fault detection for unmanned aerial vehicles /." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Measurement and Control Laboratory, 2005. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=180.

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34

Chen, Yi-Ching. "Co-design of Fault-Tolerant Systems with Imperfect Fault Detection." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104942.

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In recent decades, transient faults have become a critical issue in modernelectronic devices. Therefore, many fault-tolerant techniques have been proposedto increase system reliability, such as active redundancy, which can beimplemented in both space and time dimensions. The main challenge of activeredundancy is to introduce the minimal overhead of redundancy and to schedulethe tasks. In many pervious works, perfect fault detectors are assumed to simplifythe problem. However, the induced resource and time overheads of suchfault detectors make them impractical to be implemented. In order to tacklethe problem, an alternative approach was proposed based on imperfect faultdetectors. So far, only software implementation is studied for the proposed imperfectfault detection approach. In this thesis, we take hardware-acceleration intoconsideration. Field-programmable gate array (FPGA) is used to accommodatetasks in hardware. In order to utilize the FPGA resources efficiently, themapping and the selection of fault detectors for each task replica have to be carefullydecided. In this work, we present two optimization approaches consideringtwo FPGA technologies, namely, statically reconfigurable FPGA and dynamicallyreconfigurable FPGA respectively. Both approaches are evaluated andcompared with the proposed software-only approach by extensive experiments.

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Brucoli, Maria. "Fault behaviour and fault detection in islanded inverter-only microgrids." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/7893.

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The increase in popularity of the microgrid concept requires the analysis and solution of the numerous technical issues arising from the operation and integration of the microgrid into the original distribution network. The work presented in this thesis is centred on the study of the fault behaviour of inverter-only microgrids and on the development of a suitable fault detection technique. This task is approached by first understanding the behaviour of a microgrid during a fault and the factors affecting it. A complete description and analysis of the key elements in the study of microgrid fault behaviour is presented. Then, three microgrid models with different inverter control methods (i.e. Synchronous Reference Frame control, Natural Reference Frame control and droop control) and with various current limiting strategies are built in PSCAD and their fault behaviour is simulated, analyzed and compared. It is found that the control of the inverter is able to shape the response of the microgrid in the event of a fault. The constraints to this capability are the inverter’s ratings (current and voltage limits) and the characteristic changes in the network introduced by faults. Moreover, it is found that the control in the Natural Reference Frame gives better fault response, in terms of voltage control and simplicity in implementation, compared with the popular control in the Synchronous Reference Frame. The behaviour of the system is then further analyzed by developing quasi steadystate inverter models suitable for numerical fault analysis. The models are developed starting from the inverter control and analyzing how it changes in the event of a fault. By combining control gains and circuit parameters, they result in being capable of capturing the key features of inverters’ fault behaviour. Depending on the control strategy, some of these models are balanced and therefore are directly applicable in numerical fault analysis based on sequence components. Others are unbalanced and therefore require a fault analysis based on a direct phase coordinates representation of the network. Examples on how to perform numerical fault analysis calculations with balanced and unbalanced models are given and the numerical results well compare with the ones obtained from time-domain simulations using PSCAD. From the knowledge of the microgrid fault behaviour developed analyzing the responses in time-domain simulations and by using the developed inverter models to numerically calculate voltages and currents in the microgrid during different faults at various locations, a fault detection strategy based on voltage sequence components is proposed. Indeed, it is the behaviour of the inverter control during faults which makes the monitoring of voltage sequence components the best discriminator between normal operation and fault operation. The three building blocks of the fault detection strategy which are capable of a fast extraction and comparison of voltage sequence components are described and then the performance of the fault detection strategy for different faults and microgrid operating conditions is tested in PSCAD and discussed. Finally, examples are given on how this voltage detection can be used in the design of a microgrid protection system.

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Lamce, Bora. "Automation and Evaluation of Software Fault Prediction." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-39995.

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Delivering a fault-free software to the client requires exhaustive testing, which in today's ever-growing software systems, can be expensive and often impossible. Software fault prediction aims to improve software quality while reducing the testing effort by identifying fault-prone modules in the early stages of development process. However, software fault prediction activities are yet to be implemented in the daily work routine of practitioners as a result of a lack of automation of this process. This thesis presents an Eclipse plug-in as a fault prediction automation tool that can predict fault-prone modules using two prediction methods, Naive Bayes and Logistic Regression, while also reflecting on the performance of these prediction methods compared to each other. Evaluating the prediction methods on open source projects concluded that Logistic Regression performed better than Naive Bayes.As part of the prediction process, this thesis also reflects on the easiest metrics to automatically gather for fault prediction concluding that LOC, McCabe Complexity and CK suite metrics are the easiest to automatically gather.

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Voronin, Artyom. "Možnosti prediktivní údržby pneumatických pístů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444967.

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Tato práce se zabývá vytvořením simulačního modelu dvojčinného pneumatického pístu s mechanickou sestavou, včetně modelů snímačů, s následujícím odhadem parametrů a aproximací chování demonstračního zařízení. Dalším cílem je prezentace různých přístupů prediktivní údržby na datové sadě měřené na demonstračním zařízení. Na měřený datový soubor se aplikovaly signal-based techniky bez použití simulačního modelu a model-based metody, které vyžadují použití simulačního modelu. Výsledkem této práce je ověření možnosti monitorování stavu zařízení pomocí nainstalovaných senzorů a vyhodnocení efektivity senzorů z hlediska přesnosti a finančních nákladů.

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Liu, Chao-Shih. "Fault detection of rolling element bearings /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/7064.

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Cheng, Qi. "Distributed fault detection for dynamic systems." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2006. http://proquest.umi.com/login?COPT=REJTPTU0NWQmSU5UPTAmVkVSPTI=&clientId=3739.

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40

Yliniemi, Kimmo. "Fault detection in district heating substations /." Luleå : Luleå tekniska universitet, 2005. http://epubl.ltu.se/1402-1757/2005/60/.

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41

Du, Suguo. "Fault detection for polynomial nonlinear systems." Thesis, Coventry University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.247208.

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Dai, Xuewu. "Observer-based parameterestimation and fault detection." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504727.

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This PhD work is motivated by on-board condition monitoring of gas turbine engines (GTEs) and presents a constructive robust fault detection procedure integrating system identification, time delay compensation, eigenstructure assignment, zero assignment and dynamic observer design techniques, to detect faults in a dynamic system corrupted by disturbances at some frequencies. The main results achieved in this PhD study are: (1) Application of nonlinear least squares to Output Error (OE) model identification. Although OE model shows better performance on long-term prediction, the challenge is the dependency within the long-term prediction errors. The dependency is tackled by an iterative calculation of the gradient, and an approximation of the Hessian matrix is adopted to accelerate the convergence. (2) Delay compensation for high-gain observer based time-varying parameter estimation. In the high-gain observer based parameter estimation, it is usually assumed that the estimation delay is zero. This assumption puts some constraints on the observer design and may not be satisfied in some situations. By examining the transfer function matrices associated with the high-gain observer, a novel time delay calculation and compensation approach is proposed. The main contribution is the proof of the fact that the estimation delay is free from the plant parameter variation. Then a nonlinear phase delay filter approximation technique is used to compensate the delay. (3) Zero assignment in dynamic fault detection observer design. It is well known in filter design that zeros have the ability to block the propagation of some input signal through the system at some frequency. In this thesis. this idea is used to assign zeros to the desired places so that the disturbance can be attenuated. In most observer design research, however, the structure is confined to the classic (static) Luenberger structure where the gain is a constant. numerical matrix. As proved in this thesis, zeros of static observers arc invariant. Hence the dynamic observer is proposed, where a dynamic system (dynamic feedback gain) substitutes for the constant numerical gain matrix. As a result, some additional zeros are introduced and can be assigned arbitrarily to the desired places. To the best of our knowledge, although the concept of zeros in multivariable systems has been proposed by Rosenbrock over thirty years, there have been no known results of utilising zero assignment to robust fault detection observer design.

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Paterson, Neil Ewing. "Fault detection using transfer function techniques." Thesis, University of Edinburgh, 1987. http://hdl.handle.net/1842/11235.

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Ford, Corey. "Lazy Fault Detection for Redundant MPI." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1561.

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As the scale of supercomputers grows, it is becoming increasingly important for software to efficiently withstand hardware and software faults. Process replication is one resilience technique, but typical implementations require replicas to stay closely synchronized with each other. We propose algorithms to lazily detect faults in replicated MPI applications, allowing for more flexibility in replica scheduling and potential power savings. Evaluation shows that, when all processes are operated at full power, this approach allows applications to complete substantially faster as compared to using a synchronized model, and often as fast as in non-replicated execution.

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Kurén, Jonathan, Simon Leijon, Petter Sigfridsson, and Hampus Widén. "Fault Detection AI For Solar Panels." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413319.

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The increased usage of solar panels worldwide highlights the importance of being able to detect faults in systems that use these panels. In this project, the historical power output (kWh) from solar panels combined with meteorological data was used to train a machine learning model to predict the expected power output of a given solar panel system. Using the expected power output, a comparison was made between the expected and the actual power output to analyze if the system was exposed to a fault. The result was that when applying the explained method an expected output could be created which closely resembled the actual output of a given solar panel system with some over- and undershooting. Consequentially, when simulating a fault (50% decrease of the power output), it was possible for the system to detect all faults if analyzed over a two-week period. These results show that it is possible to model the predicted output of a solar panel system with a machine learning model (using meteorological data) and use it to evaluate if the system is producing as much power as it should be. Improvements can be made to the system where adding additional meteorological data, increasing the precision of the meteorological data and training the machine learning model on more data are some of the options. Med en ökande användning av solpaneler runt om i världen ökar även betydelsen av att kunna upptäcka driftstörningar i panelerna. Genom att utnyttja den historiska uteffekten (kWh) från solpaneler samt meteorologisk data används maskininlärningsmodeller för att förutspå den förväntade uteffekten för ett givet solpanelssystem. Den förväntade uteffekten används sedan i en jämförelse med den faktiska uteffekten för att upptäcka om en driftstörning har uppstått i systemet. Resultatet av att använda den här metoden är att en förväntad uteffekt som efterliknar den faktiska uteffekten modelleras. Följaktligen, när ett fel simuleras (50% minskning av uteffekt), så är det möjligt för systemet att hitta alla introducerade fel vid analys över ett tidsspann på två veckor. Dessa resultat visar att det är möjligt att modellera en förväntad uteffekt av ett solpanelssystem med en maskininlärningsmodell och att använda den för att utvärdera om systemet producerar så mycket uteffekt som det bör göra. Systemet kan förbättras på några vis där tilläggandet av fler meteorologiska parametrar, öka precision av den meteorologiska datan och träna maskininlärningsmodellen på mer data är några möjligheter.

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Smith, Jason. "A Sensor Fault Detection Simulation Tool." Miami University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=miami1193282225.

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Luyt, Leslie. "Automated grid fault detection and repair." Thesis, Rhodes University, 2012. http://hdl.handle.net/10962/d1006693.

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With the rise in interest in the field of grid and cloud computing, it is becoming increasingly necessary for the grid to be easily maintainable. This maintenance of the grid and grid services can be made easier by using an automated system to monitor and repair the grid as necessary. We propose a novel system to perform automated monitoring and repair of grid systems. To the best of our knowledge, no such systems exist. The results show that certain faults can be easily detected and repaired. TeX Adobe Acrobat 9.51 Paper Capture Plug-in

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Brunson, Christopher M. "Matrix converter fault detection and diagnosis." Thesis, University of Nottingham, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.718994.

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With the increased use of power electronics in aerospace, automotive, industrial, and energy generation sectors, the demand for highly reliable and power dense solutions has increased. Taking into account the demands for high reliability and high power density, matrix converters become attractive. With their lack of large bulky DC- Link capacitors, high power densities are possible with capability to operate with high ambient temperatures [7]. Demand for high reliability under tight weight and volume constrains, often it is not possible to have an entirely redundant system. Under these conditions it is desirable that the system continue to operate even under faulty conditions, albeit with diminished performance in some regard. Research has been carried out on the continued operation of a matrix converter during an open- circuit switch failure[8][9]. These methods however assume that a fault detection and diagnosis system was already in place. The behavior of matrix converters under fault conditions are more complex than traditional inverter drive systems, as there is no decoupling through the DC-Link and the matrix converter's clamp circuit also complicates matters. This thesis describes the operation of a matrix converter and the clamp circuit during a open-circuit fault condition and presents a number of methods for fault detection and diagnosis in matrix converters.

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Thanagasundram, Suguna. "Fault detection using autoregressive modelling techniques." Thesis, University of Leicester, 2007. http://hdl.handle.net/2381/30247.

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The use of spectral analysis for fault detection and diagnostics in real-time has been conservative due to concerns over large processing requirements, especially when large sample sizes and high sampling frequencies are used. In this work, it is shown how such concerns can be allayed, to a large extent, by Autoregressive (AR) modelling, as the AR method has enhanced resolution capabilities compared to the Fast Fourier Transform (FFT) technique even when small sample sizes are used and requires a sampling rate just slightly above the Nyquist rate to give good parameter estimates. The use of a parametric method of AR modelling for fault diagnosis and prognosis is a relatively new concept in the field of condition monitoring.;In this thesis, a new methodology is proposed that combines AR modelling techniques and pole-related spectral decomposition for the detection of incipient single-point bearing defects for a vibration-based condition monitoring system. Vibration signals obtained from the ball bearings of a dry vacuum pump operating in normal and faulty conditions are used as the test signals and are modelled as time-variant AR series.;The position of the poles, which are the roots of the AR coefficient polynomial, vary for every frame of vibration data. It is a known fact that as defects such as spalls and cracks start to appear on the ball bearings, the amplitude of the vibrations of characteristic defect frequencies increases. This is seen as the poles moving closer to the unit circle as the severity of the defect increases. Simple statistical indicators such as the power and frequency of each bearing defect spectral component can be extracted from the residual and position of the AR poles. These indicators can be effectively used for fault classification to distinguish between the no-fault and defective cases as the difference between them is significant.

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Shang, Qi. "A new short circuit fault detection scheme for fault tolerant drive systems." Thesis, University of Newcastle Upon Tyne, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500891.

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The civil aerospace industry is moving towards the 'more electric aeroplane'. The benefits are reduced weight and reduced maintenance which will result in better fuel economy and lower flight cost per mile. One of the important technologies is fault tolerant yet power dense electrical drive systems which have very high reliability to meet safety-critical aircraft applications. The particular subject of this thesis is fault detection in permanent magnet synchronous machines. The specific target application is a 16kW, 15,000 rpm aircraft engine fuel pump drive that has previously been developed by the Power Electronics, Drives and Machines Research Group at the Newcastle University.

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Dissertationen zum Thema „Fault detection and prediction“

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