Relevant bibliographies by topics / Fault detection and prediction

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Academic literature on the topic 'Fault detection and prediction'

Author: Grafiati
Published: 26 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Fault detection and prediction"

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S, Swetha, and Dr S. Venkatesh kumar. "Fault Detection and Prediction in Cloud Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 878–80. http://dx.doi.org/10.31142/ijtsrd18647.

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Basnet, Barun, Hyunjun Chun, and Junho Bang. "An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems." Journal of Sensors 2020 (June 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/6960328.

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Effective fault diagnosis in a PV system requires understanding the behavior of the current/voltage (I/V) parameters in different environmental conditions. Especially during the winter season, I/V characters of certain faulty states in a PV system closely resemble that of a normal state. Therefore, a normal fault detection model can falsely predict a well-operating PV system as a faulty state and vice versa. In this paper, an intelligent fault diagnosis model is proposed for the fault detection and classification in PV systems. For the experimental verification, various fault state and normal state datasets are collected during the winter season under wide environmental conditions. The collected datasets are normalized and preprocessed using several data-mining techniques and then fed into a probabilistic neural network (PNN). The PNN model will be trained with the historical data to predict and classify faults when new data is fetched in it. The trained model showed better performance in prediction accuracy when compared with other classification methods in machine learning.
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Biddle, Liam, and Saber Fallah. "A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM." Automotive Innovation 4, no. 3 (2021): 301–14. http://dx.doi.org/10.1007/s42154-021-00138-0.

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AbstractFaults that develop in vehicle sensors have the potential to propagate unchecked throughout control systems if undetected. Automatic fault diagnosis and health monitoring algorithms will become necessary as automotive applications become more autonomous. The current fault diagnosis systems are not effective for complex systems such as autonomous cars where the case of simultaneous faults in different sensors is highly possible. Therefore, this paper proposes a novel fault detection, isolation and identification architecture for multi-fault in multi-sensor systems with an efficient computational burden for real-time implementation. Support Vector Machine techniques are used to detect and identify faults in sensors for autonomous vehicle control systems. In addition, to identify degrading performance in a sensor and predict the time at which a fault will occur, a novel predictive algorithm is proposed. The effectiveness and accuracy of the architecture in detecting and identifying multiple faults as well as the accuracy of the proposed predictive fault detection algorithm are verified through a MATLAB/IPG CarMaker co-simulation platform. The results present detection and identification accuracies of 94.94% and 97.01%, respectively, as well as a prediction accuracy of 75.35%.
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Patan, Krzysztof, and Józef Korbicz. "Nonlinear model predictive control of a boiler unit: A fault tolerant control study." International Journal of Applied Mathematics and Computer Science 22, no. 1 (2012): 225–37. http://dx.doi.org/10.2478/v10006-012-0017-6.

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Nonlinear model predictive control of a boiler unit: A fault tolerant control studyThis paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.
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Wang, Shizhuang, Xingqun Zhan, Yawei Zhai, and Baoyu Liu. "Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction." Sensors 20, no. 3 (2020): 590. http://dx.doi.org/10.3390/s20030590.

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To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.
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Al Qasem, Osama, and Mohammed Akour. "Software Fault Prediction Using Deep Learning Algorithms." International Journal of Open Source Software and Processes 10, no. 4 (2019): 1–19. http://dx.doi.org/10.4018/ijossp.2019100101.

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Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (PC1, KC1, KC2 and CM1). The experiment results show how the CNN algorithm achieves prediction superiority of the MLP algorithm. The accuracy and detection rate measurements when using CNN has reached the standard ratio respectively as follows: PC1 97.7% - 73.9%, KC1 100% - 100%, KC2 99.3% - 99.2% and CM1 97.3% - 82.3%. This study provides promising results in using the deep learning for software fault prediction research.
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Li, Qiuying, and Hoang Pham. "Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts." Applied Sciences 11, no. 15 (2021): 6998. http://dx.doi.org/10.3390/app11156998.

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Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.
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Ma, Jie, and Jianan Xu. "Fault Prediction Algorithm for Multiple Mode Process Based on Reconstruction Technique." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/348729.

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In the framework of fault reconstruction technique, this paper studies the problems of multiple mode process fault detection, fault estimation, and fault prediction systematically based on multi-PCA model. First, a multi-PCA model is used for fault detection in steady state process under different conditions, while a weighted algorithm is applied to transition process. Then, describe the faults quantitatively and use the optimization method to derive the fault amplitude under the sense of fault reconstruction. Fault amplitude drifts under different conditions even if the same fault occurs. To solve the above problem, consistent estimation algorithm of fault amplitude under different conditions has been studied. Last, employ the support vector machine (SVM) to predict the trend of the fault amplitude. Effectiveness of the algorithms proposed in this paper has been verified using Tennessee Eastman process as the study object.
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Shin, Donghoon, Kang-moon Park, and Manbok Park. "Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles." Electronics 9, no. 11 (2020): 1774. http://dx.doi.org/10.3390/electronics9111774.

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This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main ideas are the usage of redundant external sensor pairs, which estimate the same target, whose results are compared to detect the fault by a modified duplication-comparison method and the novel fault isolation method using target predictions. By comparing the estimations of surrounding vehicles and the raw measurement data, the location of faults can be determined whether they are from sensors themselves or a software error. In addition, faults were isolated by defining possible areas where existing sensor coordinates could be measured, which can be predicted by using previous estimation results. The performance of the algorithm has been tested by using offline vehicle data analysis via MATLAB. Various fault injection experiments were conducted and the performance of the suggested algorithm was evaluated based on the time interval between injection and the detection of faults.
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Encalada-Dávila, Á., C. Tutivén, B. Puruncajas, and Y. Vidal. "Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder." Renewable Energy and Power Quality Journal 19 (September 2021): 487–92. http://dx.doi.org/10.24084/repqj19.325.

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Nowadays, wind turbine fault detection strategies are settled as a meaningful pipeline to achieve required levels of efficiency, availability, and reliability, considering there is an increasing installation of this kind of machinery, both in onshore and offshore configuration. In this work, it has been applied a strategy that makes use of SCADA data with an increased sampling rate. The employed wind turbine in this study is based on an advanced benchmark, established by the National Renewable Energy Laboratory (NREL) of USA. Different types of faults on several actuators and sensed by certain installed sensors have been studied. The proposed strategy is based on a normality model by means of an autoencoder. As of this, faulty data are used for testing from which prediction errors were computed to detect if those raise a fault alert according to a defined metric which establishes a threshold on which a wind turbine works securely. The obtained results determine that the proposed strategy is successful since the model detects the considered three types of faults. Finally, even when prediction errors are small, the model is able to detect the faults without problems.
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Dissertations / Theses on the topic "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.<br>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.<br>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.<br>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<br>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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Books on the topic "Fault detection and prediction"

1

Zhou, Chengke. Novel approaches to alternator transient response prediction and rotor interturn fault detection. University of Manchester, 1994.

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Pakanen, Jouko. Prediction and fault detection of building energy consumption using multi-input, single-output dynamic model. Technical Research Centre of Finland, 1992.

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Supervision and control for industrial processes: Using grey box models, predictive control, and fault detection methods. Springer, 1998.

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Sohlberg, Björn. Supervision and Control for Industrial Processes: Using Grey Box Models, Predictive Control and Fault Detection Methods. Springer London, 1998.

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Kumar, Sandeep, and Santosh Singh Rathore. Software Fault Prediction. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8715-8.

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Meskin, Nader, and Khashayar Khorasani. Fault Detection and Isolation. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8393-0.

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Alwi, Halim. Fault Detection and Fault-Tolerant Control Using Sliding Modes. Springer-Verlag London Limited, 2011.

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Li, Linlin. Fault Detection and Fault-Tolerant Control for Nonlinear Systems. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13020-6.

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Alwi, Halim, Christopher Edwards, and Chee Pin Tan. Fault Detection and Fault-Tolerant Control Using Sliding Modes. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-650-4.

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Jalel, N. A. Fault detection using optimal control techniques. University of Sheffield, Dept. of Control Engineering, 1990.

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Book chapters on the topic "Fault detection and prediction"

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Andonovski, Goran, Sašo Blažič, and Igor Škrjanc. "Evolving Fuzzy Model for Fault Detection and Fault Identification of Dynamic Processes." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_9.

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Piccoli, L. B., R. V. B. Henriques, E. Fabres, E. L. Schneider, and C. E. Pereira. "Embedded Systems Solutions for Fault Detection and Prediction in Electrical Valves." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06966-1_44.

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Mesquita, Acélio L., Vandilberto P. Pinto, and Leonardo R. Rodrigues. "Detection and Fault Prediction in Electrolytic Capacitors Using Artificial Neural Networks." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71503-8_22.

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Kabasakal, İnanç, Fatma Demircan Keskin, Aydin Koçak, and Haluk Soyuer. "A Prediction Model for Fault Detection in Molding Process Based on Logistic Regression Technique." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31343-2_31.

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Yalowitz, Jeffrey S., Roger K. Youree, Aaron Corder, and Teng K. Ooi. "Predictive Fault Detection for Missile Defense Mission Equipment and Structures." In Experimental and Applied Mechanics, Volume 6. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9792-0_107.

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Mondal, Kartick Chandra, and Hrishav Bakul Barua. "Fault Analysis and Trend Prediction in Telecommunication Using Pattern Detection: Architecture, Case Study and Experimentation." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8578-0_24.

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Osman, Shazali, and Wilson Wang. "A New Hilbert-Huang Transform Technique for Fault Detection in Rolling Element Bearings." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_7.

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Chen, S. Y., C. Y. Yao, G. Xiao, Y. S. Ying, and W. L. Wang. "Fault Detection and Prediction of Clocks and Timers Based on Computer Audition and Probabilistic Neural Networks." In Computational Intelligence and Bioinspired Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494669_117.

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Pichler, Kurt. "Early Fault Detection in Reciprocating Compressor Valves by Means of Vibration and pV Diagram Analysis." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_6.

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Leite, Daniel. "Comparison of Genetic and Incremental Learning Methods for Neural Network-Based Electrical Machine Fault Detection." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_8.

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Conference papers on the topic "Fault detection and prediction"

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DePold, Hans R., Ravi Rajamani, William H. Morrison, and Krishna R. Pattipati. "A Unified Metric for Fault Detection and Isolation in Engines." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-91095.

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In this paper we make two key contributions. First, we formalize the effectiveness of fault detection and isolation (FDI) with a metric that globally considers the following: variance in engine parameter estimate residuals under normal conditions, costs of missed detections and false alarms, costs associated with misclassification of faults, fault frequencies and fault severities. Reducing the error variance increases the signal-to-noise ratio, thereby increasing the reliability and speed of fault-detection algorithms. Minimizing missed detections has enormous implications on operational safety, while minimizing false alarms and fault misclassifications has implications on downtime, asset management, cannot duplicates, and operational costs. This metric measures the trade off between reducing data error variances, between false and missed detects, and misclassification of faults. As a second contribution, we embed this metric in a systematic data-driven diagnostic optimization process for normative decisions on input parameter selection for residual generation, FDI methods and prediction/classification fusion techniques.
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Papakonstantinou, Nikolaos, Scott Proper, Douglas L. Van Bossuyt, Bryan O’Halloran, and Irem Y. Tumer. "A Functional Modelling Based Methodology for Testing the Predictions of Fault Detection and Identification Systems." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59916.

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Fault detection and identification (FDI) systems, which are based on data mining and artificial intelligence techniques, cannot guarantee a perfect success rate or provide analytical proof for their predictions. This characteristic is problematic when such an FDI system is monitoring a safety-critical process. In these cases, the predictions of the FDI system need to be verified by other means, such as tests on the process, to increase trust in the diagnosis. This paper contributes an extension of the Hierarchical Functional Fault Detection and Identification (HFFDI) system, a combination of a plant-wide and multiple function-specific FDI modules, developed in past research. A test preparation and test-based verification phase is added to the HFFDI methodology. The functional decomposition of the process and the type of the faulty components guides the preparation of specific tests for every fault to be identifiable by the HFFDI system. These tests have the potential to confirm or disprove the existence of the fault(s) in the target process. The target is minor automation faults in redundant systems of the monitored process. The proposed extension of the HFFDI system is applied to a case study of a generic Nuclear Power Plant model. Two HFFDI predictions are tested (a successful and an incorrect prediction) in single fault scenarios and one prediction is tested in a in a two fault scenario. The results of the case study show that the testing phase introduced in this paper is able to confirm correct fault predictions and reject incorrect fault predictions, thus the HFFDI extension presented here improves the confidence of the HFFDI output.
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Li, Mengyan, Junshan Li, Shuangshuang Li, Wenqing Wang, and Fen Li. "TWT transmitter fault prediction based on ANFIS." In LIDAR Imaging Detection and Target Recognition 2017, edited by Yueguang Lv, Jianzhong Su, Wei Gong, et al. SPIE, 2017. http://dx.doi.org/10.1117/12.2296313.

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Andresen, Christian Andre, Bendik Nybakk Torsæter, Hallvar Haugdal, and Kjetil Uhlen. "Fault Detection and Prediction in Smart Grids." In 2018 IEEE 9th International Workshop on Applied Measurements for Power Systems (AMPS). IEEE, 2018. http://dx.doi.org/10.1109/amps.2018.8494849.

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Rogers, Austin, Fangzhou Guo, and Bryan Rasmussen. "Applying Static Fault Detection and Diagnosis Methods to Transient Air Conditioning Data Using an Equilibrium Prediction." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11579.

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Abstract Fault detection and diagnosis methods for air conditioning systems typically apply static models after filtering out transient data using a steady state filter. However, air conditioning systems operating in the field often do not achieve a meaningful steady state and therefore the models cannot be applied because only transient data is available. This paper proposes a solution to this problem by predicting the equilibrium point using an exponential regression. The transient response of many system parameters such as cooling capacity, airflow, and refrigerant mass flow may be approximated as a first order dynamic response because the thermal mass in the system dominates other higher order dynamics. The best-fit for a decaying exponential will therefore yield a prediction for the equilibrium point, and static models may then be applied, thus enabling the use of static models with transient data. The method’s performance is quantified using both randomly generated data (Monte Carlo simulations) and the measured response of a field-operating system during both fault-free and faulty operation.
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Chen, Yu, and Jiyu Chen. "Big Data Platform for Faults Prediction Diagnosis of CBI." In 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). IEEE, 2019. http://dx.doi.org/10.1109/safeprocess45799.2019.9213406.

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Courdier, A., and Y. G. Li. "Power Setting Sensor Fault Detection and Accommodation for Gas Turbine Engines Using Artificial Neural Networks." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-56304.

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Sensors installed on gas turbine gas path are used to obtain gas path measurement parameters for control and condition monitoring purpose. These sensors are prone to degradation or failure due to hostile working environment around them. Most gas path sensor diagnostic research is based on an assumption that the power setting sensor, a sensor used by engine control system to control engine power output, has no fault so engine measurement data can always be obtained at desired operating conditions. However in practice, power setting sensor may also be faulty, which may result in misleading measurement data and diagnostic results. In this paper, an artificial neural network based gas path diagnostic approach for engine power setting sensor fault detection and quantification has been introduced. Nested artificial neural networks (ANN) are used to detect power setting sensor fault and ensure prediction accuracy. Measurement noise is also considered in the training and testing samples to ensure the robustness of the diagnostic system. The developed power setting sensor diagnostic approach has been applied to a model 2-shafts industrial gas turbine engine similar to a GE LM2500+G4 engine to test the effectiveness of the approach. The selected power setting parameter is the shaft power output measured by a power setting sensor. An engine performance model is produced using Cranfield University’s gas turbine performance and diagnostics software, Pythia. Training samples with the consideration of sensor faults were simulated with the engine model assuming one of the sensors, either the power setting sensor or other gas path sensors may be faulty. In the nested neural network for sensor fault diagnostics, the system separately performs sensor fault detection, sensor fault identification and sensor fault quantification. Results show that the developed nested neural network diagnostic system is able to identify the power setting sensor fault and correctly predict the magnitude of the fault. This would allow the engine control system correct its control schedule and accommodate the power setting sensor fault.
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Jia, Chao, and Hanwen Zhang. "RUL Prediction: Reducing Statistical Model Uncertainty Via Bayesian Model Aggregation." In 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). IEEE, 2019. http://dx.doi.org/10.1109/safeprocess45799.2019.9213433.

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Liu, Y., C. Liu, D. Wang, X. Feng, and Q. Lu. "3D Fault Detection Using Structure Prediction and Nonstationary Similarity." In 74th EAGE Conference and Exhibition incorporating EUROPEC 2012. EAGE Publications BV, 2012. http://dx.doi.org/10.3997/2214-4609.20148245.

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Upadhyaya, B. R., G. Mathai, and J. D. Green. "Data Clustering and Prediction for Fault Detection and Diagnostics." In 1988 American Control Conference. IEEE, 1988. http://dx.doi.org/10.23919/acc.1988.4789798.

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Reports on the topic "Fault detection and prediction"

1

Ingle, Richard M., John H. Bordelon, Michael J. Willis, and C. D. Stokes. Analog Microcircuit Fault Prediction. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada281958.

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Yinger, Robert, J., Venkata, S., S., and Virgilio Centeno. Fault Locating, Prediction and Protection (FLPPS). Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/989414.

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Shabalina, A., A. Carpenter, M. Rahman, C. Tennant, and L. Vidyaratne. Machine Learning Based Cavity Fault Classification and Prediction. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1735851.

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King, Bruce Hardison, and Christian Birk Jones. Final Technical Report: PV Fault Detection Tool. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1233822.

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ORINCON CORP LA JOLLA CA. Conditioned Based Machinery Maintenance (Helicopter Fault Detection). Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada252822.

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ORINCON CORP LA JOLLA CA. Conditioned Based Machinery Maintenance (Helicopter Fault Detection). Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada255796.

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Butzbaugh, Joshua, Abraham SD Tidwell, and Chrissi Antonopoulos. Automatic Fault Detection & Diagnostics: Residential Market Analysis. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1670423.

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Heo, Jaehyeok, W. Vance Payne, Piotr A. Domanski, and Zhimin Du. Self-training of a fault-free model for residential air conditioner fault detection and diagnostics. National Institute of Standards and Technology, 2015. http://dx.doi.org/10.6028/nist.tn.1881.

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Lavrova, Olga, Jack David Flicker, and Jay Johnson. PV Systems Reliability Final Technical Report: Ground Fault Detection. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1234818.

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Brotherton, T. W., and T. G. Pollard. Condition Based Machinery Maintenance (Helicopter Fault Detection). Phase I. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada259774.

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