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Dissertations / Theses on the topic 'Engine fault diagnosis'
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1
Zedda, M. "Gas turbine engine and sensor fault diagnosis." Thesis, Cranfield University, 1999. http://dspace.lib.cranfield.ac.uk/handle/1826/9117.
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Substantial economic and even safety related gains can be achieved if effective gas
turbine performance analysis is attained. During the development phase, analysis can help
understand the effect on the various components and on the overall engine performance
of the modifications applied. During usage, analysis plays a major role in the assessment
of the health status of the engine. Both condition monitoring of operating engines and
pass off tests heavily rely on the analysis.
In spite of its relevance, accurate performance analysis is still difficult to achieve. A
major cause of this is measurement uncertainty: gas turbine measurements are affected by
noise and biases. The simultaneous presence of engine and sensor faults makes it hard to
establish the actual condition of the engine components.
To date, most estimation techniques used to cope with measurement uncertainty are
based on Kalman filtering. This classic estimation technique, though, is definitely not
effective enough. Typical Kalman filter results can be strongly misleading so that even
the application of performance analysis may become questionable. The main engine
manufactures, in conjunction with research teams, have devised modified Kalman filter
based techniques to overcome the most common drawbacks. Nonetheless, the proposed
methods are not able to produce accurate and reliable performance analysis.
In the present work a different approach has been pursued and a novel method
developed, which is able to quantify the performance parameter variations expressing the
component faults in presence of noise and a significant number of sensor faults. The
statistical basis of the method is sound: the only accepted statistical assumption regards
the well known measurement noise standard deviations. The technique is based on an
optimisation procedure carried out by means of a problem specific, real coded Genetic
Algorithm. The optimisation based method enables to concentrate the steady state
analysis on the faulty engine component(s). A clear indication is given as to which
component(s) is(are) responsible for the loss of performance. The optimisation
automatically carries out multiple sensor failure detection, isolation and accommodation.
The noise and biases affecting the parameters setting the operating point of the engine
are coped with as well.
The technique has been explicitly developed for development engine test bed analysis,
where the instrumentation set is usually rather comprehensive. In other diagnostic cases
(pass off tests, ground based analysis of on wing engines), though, just few sensors may
be present. For these situations, the standard method has been modified to perform
multiple operating point analysis, whereby the amount of information is maximised by
simultaneous analysis of more than a single test point. Even in this case, the results are
very accurate.
In the quest for techniques able to cope with measurement uncertainty, Neural Networks
have been considered as well. A novel Auto-Associative Neural Network has been
devised, which is able to carry out accurate sensor failure detection and isolation.
Advantages and disadvantages of Neural Network-based gas turbine diagnostics have
been analysed.
2
Frisk, Erik. "Model-based fault diagnosis applied to an SI-Engine." Thesis, Linköpings universitet, Fordonssystem, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-141630.
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A diagnosis procedure is an algorithm to detect and locate (isolate) faulty components in a dynamic process. In 1994 the California Air Resource Board released a regulation, called OBD II, demanding a thorough diagnosis system on board automotive vehicles. These legislative demands indicate that diagnosis will become increasingly important for automotive engines in the next few years. To achieve diagnosis, redundancy has to be included in the system. This redundancy can be either hardware redundancy or analytical redundancy. Hardware redundancy, e.g. an extra sensor or extra actuator, can be space consuming or expensive. Methods based on analytical redundancy need no extra hardware, the redundancy here is generated from a process model instead. In this thesis, approaches utilizing analytical redundancy is examined. A literature study is made, surveying a number of approaches to the diagnosis problem. Three approaches, based on both linear and non-linear models, are selected and further analyzed and complete design examples are performed. A mathematical model of an SI-engine is derived to enable simulations of the designed methods.
3
Lee, Y. H. "Gas turbine engine health monitoring by fault pattern matching method." Thesis, Cranfield University, 1998. http://dspace.lib.cranfield.ac.uk/handle/1826/10714.
Full textAbstract:
The
gas turbine engine has a wide range of applications, these include industrial and
aerospace applications on locomotive, ferry, compressor and power generation, and
the most popular application will be for the air transportation. The application for air
transportation including military and commercial aircraft is highly sensitive to safety
concerns. The engine health monitoring system plays a major role for addressing this
concern, a good engine monitoring system will not only to provide immediate and
correct information to the engine user but also provide useful information for
managing the maintenance activities. Without a reliable performance diagnosis
module involved, there will be not possible to build a good health monitoring system.
There are many methodologies had been proposed and studied during past three
decades, and yet still struggling to search for some good techniques to handle
instrumentation errors. In order to develop a reliable engine performance diagnosis
technique, a fully understanding and proper handling of the instrumentation is
essential.
A engine performance fault pattern matching method has been proposed and
developed in this study, two fault libraries contains a complete defined set of 51963
faults was created by using a newly serviced fighter engine component data. This
pattern matching system had been verified by different approaches, such as compares
with linear and nonlinear diagnosis results and compares with performance sensitivity
analysis results by using LTF program engine data. The outcomes from the
verications indicate an encouraging result for further exploring this method.
In
conclusion, this research has not only propose a feasible performance diagnosis
techniques, but also developed and verified through different kind of approaches for
this techniques. In addition to that, by proper manipulating the created fault library, a
possible new tool for analyzing the application of instruments' implementation was
discovered. The author believes there will be more to study by using this created fault
pattern library. For instance, this fault pattern library can be treated as a very good
initial training sets for neural networking to develop a neural diagnosis technique.
This study has put a new milestone for further exploring gas turbine diagnosis
technique by using fault pattern related methods.
4
Pernestål, Anna. "A Bayesian approach to fault isolation with application to diesel engine diagnosis." Licentiate thesis, KTH, School of Electrical Engineering (EES), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4294.
Full textAbstract:
Users of heavy trucks, as well as legislation, put increasing demands on heavy trucks. The vehicles should be more comfortable, reliable and safe. Furthermore, they should consume less fuel and be more environmentally friendly. For example, this means that faults that cause the emissions to increase must be detected early. To meet these requirements on comfort and performance, advanced sensor-based computer control-systems are used. However, the increased complexity makes the vehicles more difficult for the workshop mechanic to maintain and repair. A diagnosis system that detects and localizes faults is thus needed, both as an aid in the repair process and for detecting and isolating (localizing) faults on-board, to guarantee that safety and environmental goals are satisfied.
Reliable fault isolation is often a challenging task. Noise, disturbances and model errors can cause problems. Also, two different faults may lead to the same observed behavior of the system under diagnosis. This means that there are several faults, which could possibly explain the observed behavior of the vehicle.
In this thesis, a Bayesian approach to fault isolation is proposed. The idea is to compute the probabilities, given ``all information at hand'', that certain faults are present in the system under diagnosis. By ``all information at hand'' we mean qualitative and quantitative information about how probable different faults are, and possibly also data which is collected during test drives with the vehicle when faults are present. The information may also include knowledge about which observed behavior that is to be expected when certain faults are present.
The advantage of the Bayesian approach is the possibility to combine information of different characteristics, and also to facilitate isolation of previously unknown faults as well as faults from which only vague information is available. Furthermore, Bayesian probability theory combined with decision theory provide methods for determining the best action to perform to reduce the effects from faults.
Using the Bayesian approach to fault isolation to diagnose large and complex systems may lead to computational and complexity problems. In this thesis, these problems are solved in three different ways. First, equivalence classes are introduced for different faults with equal probability distributions. Second, by using the structure of the computations, efficient storage methods can be used. Finally, if the previous two simplifications are not sufficient, it is shown how the problem can be approximated by partitioning it into a set of sub problems, which each can be efficiently solved using the presented methods.
The Bayesian approach to fault isolation is applied to the diagnosis of the gas flow of an automotive diesel engine. Data collected from real driving situations with implemented faults, is used in the evaluation of the methods. Furthermore, the influences of important design parameters are investigated.
The experiments show that the proposed Bayesian approach has promising potentials for vehicle diagnosis, and performs well on this real problem. Compared with more classical methods, e.g. structured residuals, the Bayesian approach used here gives higher probability of detection and isolation of the true underlying fault.
Både användare och lagstiftare ställer idag ökande krav på prestanda hos tunga lastbilar. Fordonen ska var bekväma, tillförlitliga och säkra. Dessutom ska de ha bättre bränsleekonomi vara mer miljövänliga. Detta betyder till exempel att fel som orsakar förhöjda emissioner måste upptäckas i ett tidigt stadium.
För att möta dessa krav på komfort och prestanda används avancerade sensorbaserade reglersystem.
Emellertid leder den ökade komplexiteten till att fordonen blir mer komplicerade för en mekaniker att underhålla, felsöka och reparera.
Därför krävs det ett diagnossystem som detekterar och lokaliserar felen, både som ett hjälpmedel i reparationsprocessen, och för att kunna detektera och lokalisera (isolera) felen ombord för att garantera att säkerhetskrav och miljömål är uppfyllda.
Tillförlitlig felisolering är ofta en utmanande uppgift. Brus, störningar och modellfel kan orsaka problem. Det kan också det faktum två olika fel kan leda till samma observerade beteende hos systemet som diagnosticeras. Detta betyder att det finns flera fel som möjligen skulle kunna förklara det observerade beteendet hos fordonet.
I den här avhandlingen föreslås användandet av en Bayesianska ansats till felisolering. I metoden beräknas sannolikheten för att ett visst fel är närvarande i det diagnosticerade systemet, givet ''all tillgänglig information''. Med ''all tillgänglig information'' menas både kvalitativ och kvantitativ information om hur troliga fel är och möjligen även data som samlats in under testkörningar med fordonet, då olika fel finns närvarande. Informationen kan även innehålla kunskap om vilket beteende som kan förväntas observeras då ett särskilt fel finns närvarande.
Fördelarna med den Bayesianska metoden är möjligheten att kombinera information av olika karaktär, men också att att den möjliggör isolering av tidigare okända fel och fel från vilka det endast finns vag information tillgänglig. Vidare kan Bayesiansk sannolikhetslära kombineras med beslutsteori för att erhålla metoder för att bestämma nästa bästa åtgärd för att minska effekten från fel.
Användandet av den Bayesianska metoden kan leda till beräknings- och komplexitetsproblem. I den här avhandlingen hanteras dessa problem på tre olika sätt. För det första så introduceras ekvivalensklasser för fel med likadana sannolikhetsfördelningar. För det andra, genom att använda strukturen på beräkningarna kan effektiva lagringsmetoder användas. Slutligen, om de två tidigare förenklingarna inte är tillräckliga, visas det hur problemet kan approximeras med ett antal delproblem, som vart och ett kan lösas effektivt med de presenterade metoderna.
Den Bayesianska ansatsen till felisolering har applicerats på diagnosen av gasflödet på en dieselmotor. Data som har samlats från riktiga körsituationer med fel implementerade används i evalueringen av metoderna. Vidare har påverkan av viktiga parametrar på isoleringsprestandan undersökts.
Experimenten visar att den föreslagna Bayesianska ansatsen har god potential för fordonsdiagnos, och prestandan är bra på detta reella problem. Jämfört med mer klassiska metoder baserade på strukturerade residualer ger den Bayesianska metoden högre sannolikhet för detektion och isolering av det sanna, underliggande, felet.
5
Baravdish, Ninos. "Information Fusion of Data-Driven Engine Fault Classification from Multiple Algorithms." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176508.
Full textAbstract:
As the automotive industry constantly makes technological progress, higher demands are placed on safety, environmentally friendly and durability. Modern vehicles are headed towards increasingly complex system, in terms of both hardware and software making it important to detect faults in any of the components. Monitoring the engine’s health has traditionally been done using expert knowledge and model-based techniques, where derived models of the system’s nominal state are used to detect any deviations. However, due to increased complexity of the system this approach faces limitations regarding time and knowledge to describe the engine’s states. An alternative approach is therefore data-driven methods which instead are based on historical data measured from different operating points that are used to draw conclusion about engine’s present state. In this thesis a proposed diagnostic framework is presented, consisting of a systematically approach for fault classification of known and unknown faults along with a fault size estimation. The basis for this lies in using principal component analysis to find the fault vector for each fault class and decouple one fault at the time, thus creating different subspaces. Importantly, this work investigates the efficiency of taking multiple classifiers into account in the decision making from a performance perspective. Aggregating multiple classifiers is done solving a quadratic optimization problem. To evaluate the performance, a comparison with a random forest classifier has been made. Evaluation with challenging test data show promising results where the algorithm relates well to the performance of random forest classifier.
6
Pernestål, Anna. "A Bayesian approach to fault isolation with application to diesel engine diagnosis /." Stockholm : KTH School of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4294.
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Avram, Remus C. "A UNIFIED NONLINEAR ADAPTIVE APPROACH FOR THE FAULT DIAGNOSIS OF AIRCRAFT ENGINES." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1332784433.
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Afrashteh, Reza. "Modeling, fault detection and diagnosis of an automotive engine using artificial neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0025/MQ51278.pdf.
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Bourassa, M. A. J. "Autoassociative neural networks with an application to fault diagnosis of a gas turbine engine." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0004/MQ44834.pdf.
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Nyman, David. "Injector diagnosis based on engine angular velocity pulse pattern recognition." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414967.
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In a modern diesel engine, a fuel injector is a vital component. The injectors control the fuel dosing into the combustion chambers. The accuracy in the fuel dosing is very important as inaccuracies have negative effects on engine out emissions and the controllability. Because of this, a diagnosis that can classify the conditions of the injectors with good accuracy is highly desired. A signal that contains information about the injectors condition, is the engine angular velocity. In this thesis, the classification performance of six common machine learning methods is evaluated. The input to the methods is the engine angular velocity. In addition to the classification performance, also the computational cost of the methods, in a deployed state, is analysed. The methods are evaluated on data from a Scania truck that has been run just like any similar commercial vehicle. The six methods evaluated are: logistic regression, kernel logistic regression, linear discriminant analysis, quadratic discriminant analysis, fully connected neural networks and, convolutional neural networks. The results show that the neural networks achieve the best classification performance. Furthermore, the neural networks also achieve the best classification performance from a, in a deployed state, computational cost effectiveness perspective. Results also indicate that the neural networks can avoid false alarms and maintain high sensitivity.
11
Adolfson, Magnus. "Simulation of Emission Related Faults on a Diesel Engine." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1506.
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Today's legislation on exhaust gas emissions for heavy duty diesel (HDD) vehicles is more stringent than ever and will be even more tough in the future. More over, in a few years HDD vehicles have to be equipped with OBD (On-Board Diagnostics). This place very high demands on the manufacturers to develop better engines and strategies for OBD. As an aid in the process models can be used.
This thesis presents extensions of an existing diesel engine model in Matlab/Simulink to be able to simulate emissions during standardized european test cycles. Faults in the sensor and actuator signals are implemented into the model to find out if there is an increase or decrease in the emissions. This is used to create a fault tree where it can be seen why predefined emission thresholds are exceeded. The tree is an aid when developing OBD.
The results from the simulations showed that almost no faults made the emissions cross the thresholds. The only interesting faults were faults in the ambient temperature sensor and the injection angle actuator. This means that the OBD-system only needs to monitor a few components which implies a smaller system and less work.
12
Owen, Christopher Lloyd. "Automated diesel engine condition & performance monitoring & the application of neural networks to fault diagnosis." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/2272.
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The overall aim of this research was to design, configure and validate a system which was capable of on-line performance monitoring and fault diagnosis of a diesel engine. This thesis details the development and evaluation of a comprehensive engine test facility and automated engine performance monitoring package. Results of a diesel engine fault study were used to ascertain commonly occurring faults and their realistic severities are discussed. The research shows how computer simulation and rig testing can be applied to validate the effects of faults on engine performance and quantify fault severities. A substantial amount of engine test work has been conducted to investigate the effects of various faults on high speed diesel engine performance. A detailed analysis of the engine test data has led to the development of explicit fault-symptom relationships and the identification of key sensors that may be fitted to a diesel engine for diagnostic purposes. The application of a neural network based approach to diesel engine fault diagnosis has been investigated. This work has included an assessment of neural network performance at engine torques and speeds where it was not trained, noisy engine data, faulty sensor data, varying fault severities and novel faults which were similar to those which the network had been trained on. The work has shown that diagnosis using raw neural network outputs under operational conditions would be inadequate. To overcome these inadequacies a new technique using an on-line diagnostic database incorporating 'weight adjusting' and 'confidence factor' algorithms has been developed and validated. The results show a neural network combined with an on-line diagnostic database can be successfully used for practical diesel engine fault diagnosis to offer a realistic alternative to current fault diagnosis techniques.
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Twiddle, John A. "Fuzzy systems in real-time condition monitoring and fault diagnosis, with a diesel engine case study." Thesis, University of Leicester, 2001. http://hdl.handle.net/2381/30193.
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Diesel engines have become a common source of power, both for vehicles and for static equipment because they are fuel efficient, robust and reliable. It is important that diesel engines run in their correct condition and properly controlled in order to maintain efficiency, low emissions levels and high reliability. The following thesis aims to assess the application of fuzzy systems in real-time condition monitoring and fault diagnosis. A 65kW diesel powered generator set has been purchased 'off the shelf' as an example of a typical application which may benefit from the development of CMFD techniques. As a test case, the diesel engine is appropriate as its sub-systems are complex, non-linear and subject to noise and uncertainty. A diagnostic structure comprising fuzzy systems in three distinct roles has been proposed. Fuzzy reference models, incorporating heuristics and approximate non-linear mathematical relationships, are used for the generation of residuals by comparison with signals from a small number of low cost transducers. The residuals are classified and the diagnosis is inferred from the pattern of residual classes using a fuzzy rule-base. The diagnostic results obtained for three diesel engine sub-systems, show this to be a powerful technique for CMFD system design which may generalised, both for other types of plant and other forms of reference model. This fuzzy model-based approach to fault diagnosis is shown to have benefits over other techniques by way of its transparency, ease of development, performance under variable engine load conditions, high level output and the lack of any requirement for fault data in the development process. The robustness of the fuzzy reference models to certain fault conditions remains a key issue. The fuzzy models were generally effective at generating residuals where deviations from the normal condition are small. For larger deviations, robustness of models is not guaranteed or expected. A number of techniques were successfully deployed to reduce the number of misclassifications caused by this lack of robustness.
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Sarotte, Camille. "Improvement of monitoring and reconfiguration processes for liquid propellant rocket engine." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS348/document.
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La surveillance et l'amélioration des modes de fonctionnement des systèmes propulsifs des lanceurs représentent des défis majeurs de l'industrie aérospatiale. En effet, une défaillance ou un dysfonctionnement du système propulsif peut avoir un impact significatif pour les clients institutionnels ou privés et entraîner des catastrophes environnementales ou humaines. Des systèmes de gestion de la santé (HMS) pour les moteurs fusée à ergols liquides (LPREs), ont été mis au point pour tenir compte des défis actuels en abordant les questions de sureté et de fiabilité. Leur objectif initial est de détecter les pannes ou dysfonctionnements, de les localiser et de prendre une décision à l’aide de Redlines et de systèmes experts. Cependant, ces méthodes peuvent induire de fausses alarmes ou des non-détections de pannes pouvant être critiques pour la sécurité et la fiabilité des opérations. Ainsi, les travaux actuels visent à éliminer certaines pannes critiques, mais aussi diminuer les arrêts intempestifs. Les données disponibles étant limitées, des méthodes à base de modèles sont essentiellement utilisées. La première tâche consiste à détecter les défaillances de composants et/ou d'instruments à l'aide de méthodes de détection et de localisation de fautes (FDI). Si la faute est considérée comme mineure, des actions de « non-arrêt » sont définies pour maintenir les performances de l'ensemble du système à un niveau proche de celles souhaitées et préserver les conditions de stabilité. Il est donc nécessaire d’effectuer une reconfiguration robuste (incertitudes, perturbations inconnues) du moteur. Les saturations en entrée doivent également être prises en compte dans la conception de la loi de commande, les signaux de commande étant limités en raison des caractéristiques ou performances des actionneurs physiques. Les trois objectifs de cette thèse sont donc : la modélisation des différents sous-systèmes principaux d’un LPRE, le développement d’algorithmes de FDI sur la base des modèles établis et la définition d’un système de reconfiguration du moteur en temps réel pour compenser certains types de pannes. Le système de FDI et Reconfiguration (FDIR) développé sur la base de ces trois objectifs a ensuite été validé à l’aide de simulations avec CARINS (CNES) et du banc d’essai MASCOTTE (CNES/ONERA)Monitoring and improving the operating modes of launcher propulsion systems are major challenges in the aerospace industry. A failure or malfunction of the propulsion system can have a significant impact for institutional or private customers and results in environmental or human catastrophes. Health Management Systems (HMS) for liquid propellant rocket engines (LPREs), have been developed to take into account the current challenges by addressing safety and reliability issues. Their objective was initially to detect failures or malfunctions, isolate them and take a decision using Redlines and Expert Systems. However, those methods can induce false alarms or undetected failures that can be critical for the operation safety and reliability. Hence, current works aim at eliminating some catastrophic failures but also to mitigate benign shutdowns to non-shutdown actions. Since databases are not always sufficient to use efficiently data-based analysis methods, model-based methods are essentially used. The first task is to detect component and / or instrument failures with Fault Detection and Isolation (FDI) approaches. If the failure is minor, non-shutdown actions must be defined to maintain the overall system current performances close to the desirable ones and preserve stability conditions. For this reason, it is required to perform a robust (uncertainties, unknown disturbances) reconfiguration of the engine. Input saturation should also be considered in the control law design since unlimited control signals are not available due to physical actuators characteristics or performances. The three objectives of this thesis are therefore: the modeling of the different main subsystems of a LPRE, the development of FDI algorithms from the previously developed models and the definition of a real-time engine reconfiguration system to compensate for certain types of failures. The developed FDI and Reconfiguration (FDIR) scheme based on those three objectives has then been validated with the help of simulations with CARINS (CNES) and the MASCOTTE test bench (CNES/ONERA)
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Zhang, Zai Yong. "Simultaneous fault diagnosis of automotive engine ignition systems using pairwise coupled relevance vector machine, extracted pattern features and decision threshold optimization." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493967.
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Sauer, Patrick [Verfasser], Rolf [Akademischer Betreuer] Isermann, and Ulrich [Akademischer Betreuer] Konigorski. "Model-based fault detection and diagnosis for the fuel system of a six-cylinder heavy duty diesel engine / Patrick Sauer ; Rolf Isermann, Ulrich Konigorski." Darmstadt : Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1235667855/34.
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Nebojša, Nikolić. "Razvoj metoda dijagnostike usisnog sistema motora sa unutrašnjim sagorevanjem." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2015. http://www.cris.uns.ac.rs/record.jsf?recordId=94802&source=NDLTD&language=en.
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U radu je razvijen jedan matematički model za simuliranje ponašanja nekih važnih radnih parametara motora SUS, kada u njegovom usisnom sistemu postoje neispravnosti tipa: „nepredviđeni ulaz vazduha u usisni kolektor“, „pogrešno očitavanje senzora masenog protoka vazduha“, „pogrešno očitavanje senzora pritiska u usisnom kolektoru“, „pogrešno očitavanje senzora temperature u usisnom kolektoru“ i „umanjen EGR protok“. Na osnovu rezultata ovog modela predložen je novi dijagnostički koncept, u okviru kojeg je razvijen jedan model za prepoznavanje pomenutih neispravnosti. Predloženi koncept je proveren na realnim podacima, prikupljenim ispitivanjem jednog stvarnog motora u laboratorijskim uslovima, pri čemu su dobijeni zadovoljavajući rezultati.A mathematical model capable of simulating some important IC engine operating parameters behavior when a fault in its intake air path exists. The faults considered are of the following types: „air leakage in the intake path“, „faulty mass air flow sensor“, „faulty manifold absolute pressure sensor“, „faulty intake air temperature sensor“ and „clogged EGR pipe“. Relying on the data obtained by the fault simulator, a novel diagnosis concept is proposed. A model for fault detection and diagnosis was developed in the scope of the concept. The proposed concept was tested on the real data collected from an automobile IC engine in the laboratory conditions and satisfying results were obtained.
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Hamad, Adnan. "Intelligent fault diagnosis for automotive engines." Thesis, Liverpool John Moores University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590082.
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Fault detection and isolation (FDI) has become one of the most important aspects of automobile design. In this thesis, a new fault detection and isolation approach is developed for automotive engines. The method uses an independent radial basis function neural network model to model engine dynamics, and the modelling errors are used to from the basis for residual generation. Furthermore, another radial basis function neural network is used as a fault classifier to isolate an occurred fault from other possible faults in the system by classifying fault characteristics embedded in the modelling errors. The performance of the developed scheme is assessed using an engine benchmark, the Mean Value Engine Model CMVEM), with Matlab/Simulink. Five faults have been simulated on the MVEM: three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10~20% changes superimposed on the measured outputs of manifold pressure, manifold temperature and crankshaft speed sensors; the component fault considered is air leakage in the intake manifold; and the actuator fault considered is the malfunction of the fuel injector. The simulation results show that all the simulated faults can be clearly detected and isolated in dynamic conditions throughout the engine operating range. Furthermore, in order to reflect the real state for an automotive engine, the FDI method is evaluated for the MVEM system under closed-loop control with air fuel ratio control. An independent radial basis function (RBF) neural network model is used to model engine dynamics using random amplitude signals (RAS) throttle angle as an input.
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Ngo, Caroline. "Surveillance du système de post-traitement essence et contrôle de chaîne d'air suralimentée." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT097.
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Poussés par les réglementations « anti-pollution », les industriels automobiles sont incités à développer des véhicules plus propres et sobres en consommation. Ces dernières années, la dépollution des véhicules n'a cessée d'être améliorée, en optimisant le traitement des polluants des moteurs actuels, mais aussi en développant des solutions alternatives. C'est dans ce contexte que les deux études ont étés menées. Cette étude s'est d'abord intéressée à l'optimisation du fonctionnement du catalyseur trois voies. Ce système est le principal organe de dépollution des motorisations essence, il est capable de convertir les trois principaux polluants réglementés (oxydes d'azote, monoxyde de carbone et hydrocarbures imbrûlés) en gaz non nocifs pour la santé. On s'est intéressé à la modélisation orientée contrôle de ce réacteur chimique, le modèle développé à permis d'élaborer les outils nécessaires au contrôleur pour lui permettre de prendre en compte les dynamiques internes du catalyseur trois voies. L'étude s'est ensuite focalisée sur le contrôle multivariable robuste d'une chaîne d'air turbocompressée d'un moteur essence selon les approches des systèmes linéaires à commutation et à paramètres variant. Le contrôle robuste permet au système de chaîne d'air d'admettre au plus juste la quantité d'air admis dans la chambre de combustion en prenant en compte les éventuelles perturbationsForced by more and more severe normative, automotive industrials have to develop clean and energy-efficient vehicles. During the last decades, after-treatment system has been improved and alternative solutions have been found. The two presented studies have been lead in this context. The first study has been focused on the performance optimization of the three ways catalyst. This after-treatment system is able to convert the three main pollutants limited by the normative (nitrogen oxides, carbon monoxide and unburned hydrocarbon) into harmless gases. The purpose of this study is to develop a mathematical reduced order model for the three ways catalyst focused on the oxygen storage-release dynamics. From this model, an observer for the oxygen storage rate estimation has been developed. Based on this tool, three ways catalyst converter controller will be able to take into account the internal dynamics. The second study deals with the multivariable robust control of a supercharged air path system for a spark ignition engine according to two approaches: the switched linear system and the linear parameter varying system. With robust control, the air path system will inject the right amount of air entering the combustion chamber by taking into account possible disruptions
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Zhou, Ji. "Intelligent fault diagnosis with applications to gas turbine engines." Thesis, University of Sheffield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284354.
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Morgan, Ian Macnab. "Fault detection and diagnosis for diesel engines using elemental analysis." Thesis, University of Portsmouth, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496604.
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Many systems, such as aeroplanes, military projects and cars are being built and designed around the fault diagnosis system, and in such instances the concept of fault diagnosis is provided equal importance with the system itself; for the purposes of safety, performance or for the purposes of marketing. On the other hand, there still remain a significant number of mechanisms such as large diesel engines which require an accurate and simple method for retrofitting a reduced form of a fault diagnosis system.
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Sangha, Mahavir Singh. "Intelligent fault diagnosis for automative engines and real data evaluation." Thesis, Liverpool John Moores University, 2008. http://researchonline.ljmu.ac.uk/5867/.
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Mesbahi, Ehsan. "Artificial neural networks for fault diagnosis, modelling and control of diesel engines." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323447.
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Madamedon, Misan. "The characteristics of instantaneous angular speed of diesel engines for fault diagnosis." Thesis, University of Huddersfield, 2018. http://eprints.hud.ac.uk/id/eprint/34553/.
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Early fault detection and diagnosis of diesel engines are paramount now, especially with countries like the UK and France, in-line with the 2015 Paris agreement on climate change, making plans to ban the use of an automobile with diesel and petrol engines before the year 2040. This ban could affect other sectors where diesel engines are the prime mover and result in more stringent exhaust emission regulations. The instantaneous angular speed (IAS) model based fault diagnosis has shown more prospects of fault detection and location. However, there are serious gaps in available knowledge regarding IAS model based fault diagnosis which takes into account the effect of the system’s modal properties. Hence, this research focuses on the online modal properties identification of a typical engine-load system for an improved performance of IAS based fault diagnosis. Having acknowledged the essentials of IAS based fault diagnosis techniques through a comprehensive literature study, this research firstly investigates the impact of modal properties on the IAS of a four-cylinder engine. This is achieved through a three degree of freedom (DOF) torsional vibration model of the engine-load system, which allows for the modal properties of the system to be calculated and analysed. The calculated modal properties of the system showed one rigid and two flexible modes which had a low (< 13Hz) and high (< 92Hz) frequencies. The mode shape of the low frequency resonance shows more amplitude on the flywheel-load reference point of the system while that of the high frequency resonance shows more amplitude on the engine-flywheel reference point of the system. It then simulated the IAS which represents the torsional vibration signature with altered modal properties. The simulated result demonstrated that the low frequency resonance is more sensitive to the peak and trough values of the IAS waveform. After identifying the deployment merits of operational modal analysis (OMA) techniques through a comprehensive literature study, this research then explore the prospect of an IAS based output-only modal properties identification of a typical engine-load system. This was done through both experimental and simulation evaluations, which allowed simulated and experimental IAS to be used for implementing covariance-driven reference based stochastic subspace identification (SSI). The simulated result using pseudo-random input shows that the identified resonance frequencies and mode shapes are 80% correlated with the calculated ones. The simulation results also demonstrated that the accuracy of the identified modal properties is dependent on the number of IAS responses used for implementing the covariance-driven reference based SSI technique. The experimental result using estimated IAS during engine shutdown operation showed that both high and low frequency vibration mode can be identified. The identified resonance frequencies with their mode shapes are 80% correlated with the predicted ones. Having identified the modal properties of the engine-load system online through the implementation of an IAS based covariance-driven SSI, this research then investigates the impact of misfire on the system’s modal properties especially the mode shape of the low frequency resonance. This was achieved experimentally by inducing a complete misfire in respective cylinders (1st, 3rd and 4th) and the IAS estimated during engine’s transient shutdown operation was used for implementing a covariance-driven reference based stochastic subspace modal properties identification. While the mode shape of the identified high frequency resonance (< 80Hz) showed no characteristics for cylinder misfire detection, that of the low frequency resonance (< 13Hz) did. Faults in the engine’s injection system and an abnormal clearance valve train conditions significantly affects its combustion process. The cylinder by cylinder pressure torque obtained from measured IAS through order domain deconvolution technique can be used to detect and diagnose injection faults. In the interim, this research has also recognised that the closer the low-resonance frequency of the model used for the order domain deconvolution gets to its real time value the more accurate the pressure torque becomes. The reconstructed pressure torque which takes into consideration the real time low frequency resonance can be used to detect faulty injection system with different severities and abnormal clearance valve conditions of several severities. Furthermore, the importance of an accurate modal properties utilisation in IAS model.
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Elamin, Fathi. "Fault detection and diagnosis in heavy duty diesel engines using acoustic emission." Thesis, University of Huddersfield, 2013. http://eprints.hud.ac.uk/id/eprint/19324/.
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A condition monitoring program applied to diesel engines, improves safety, productivity, increases serviceability and reduces maintenance costs. Investigation of a novel condition monitoring systems for diesel engine is attracting considerable attention due to both the increasing demands placed upon engine components and the limitations of conventional techniques. This thesis documents research conducted to assess the monitoring capabilities used acoustic emission (AE) analysis. It focuses on the possibility of using AE signals to monitor the fuel injector and oil condition. A series of experiments were performed on a JCB, four-stroke diesel engine. Tests under healthy operating conditions developed a detailed understanding of typical acoustic emission generation in terms of both the source mechanisms and the characteristics of the resulting activity. This was supplemented by specific tests to investigate possible acoustic emission generation due to the piston slap and friction. The effect of faults on the injector waveform was investigated using the injection system and at one sensor location. To overcome the reflections and injection system configuration effects the method of acoustic emission impedance was used. This enabled the injector signal to be successfully extracted and clearly shows its capability for detecting even minor combustion deviations between engine cylinders. Comparison between signals and measurement of the oil condition showed both provided useful information about the lubrication processes. Simulation and experimental work have demonstrated the capability of this technique to detect lubrication related faults and irregular lubrication variability between the engine's cylinders. A review of the AE sources in diesel engines and how to represent the AE signals generated is presented. Three analysis methods were used: time-domain analysis using parameters such as Root Mean Square (RMS), variance, mean and kurtosis; frequency-domain analysis which relied on the amplitudes of the frequency components of the measured signals; and time-frequency domain analysis extracting features so that the energy content of the signals and the frequency components were localized simultaneously. In this work, data has been obtained from tests on a diesel engine, where the engine load, speed, temperature and the oil lubrication type were changed. The monitored signal and its difference from that obtained for normal engine conditions was noted as a fault signature that could be used for fault detection and diagnosis.
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Aull, Mark J. "Comparison of Fault Detection Strategies on a Low Bypass Turbofan Engine Model." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321368833.
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Ceccarelli, Riccardo. "Model-based fault detection in diesel engines air-path." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00870762.
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Cette thèse a pour but l'étude de la détection basée sur modèle de défauts pour lesmoteurs Diesel produits en grande série. La nécessité d'une surveillance continue del'état de santé des véhicules est maintenant renforcée par la législation Euro VI sur lesémissions polluantes, qui sera probablement rendue encore plus contraignante dans sesprochaines révisions. Dans ce contexte, le développement de stratégies robustes, faciles àcalibrer et valides pour des systèmes dispersés (car produits en grande série) procureraitun avantage considérable aux constructeurs automobile. L'étude développée ici tentede répondre à ces besoins en proposant une méthodologie générique. On utilise desobservateurs adaptatifs locaux pour des systèmes scalaires non linéaires et affines parrapport à l'état, pour résoudre les problèmes de la détection de défauts, de son isolationet de son estimation d'une façon compacte. De plus, les incertitudes liées aux biais demesure et de modèle et aux dérives temporelles nécessitent d'améliorer les méthodes dedétection par l'utilisation de seuils robustes pour éviter les fausses détections. Dans cettethèse, on propose un seuil variable basé sur la condition d'observabilité du paramètreimpacté par le défaut et sur une étude de sensibilité par rapport aux incertitudes surles entrées ou sur le modèle. Cette méthode permet, entre autres, de fournir un outild'analyse pour la sélection des conditions de fonctionnement du système pour lesquelsle diagnostic est plus fiable et plus robuste par rapport aux incertitudes sur les entrées.L'approche présentée a été appliquée avec succès et validée de façon expérimentale surun moteur Diesel pour le problème de détection de fuite dans le système d'admissiond'air, puis dans un environnement de simulation pour le problème de détection dedérive d'efficacité turbine. On montre ainsi ses avantages en termes de fiabilité dedétection, d'effort de calibration, et pour l'analyse des conditions de fonctionnementmoteur adaptées au diagnostic.
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Li, Wenfei. "Fault Diagnostics Study for Linear Uncertain Systems Using Dynamic Threshold with Application to Propulsion System." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1284971383.
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Lundgren, Andreas. "Data-Driven Engine Fault Classification and Severity Estimation Using Residuals and Data." Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165736.
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Recent technological advances in the automotive industry have made vehicularsystems increasingly complex in terms of both hardware and software. As thecomplexity of the systems increase, so does the complexity of efficient monitoringof these system. With increasing computational power the field of diagnosticsis becoming evermore focused on software solutions for detecting and classifyinganomalies in the supervised systems. Model-based methods utilize knowledgeabout the physical system to device nominal models of the system to detect deviations,while data-driven methods uses historical data to come to conclusionsabout the present state of the system in question. This study proposes a combinedmodel-based and data-driven diagnostic framework for fault classification,severity estimation and novelty detection. An algorithm is presented which uses a system model to generate a candidate setof residuals for the system. A subset of the residuals are then selected for eachfault using L1-regularized logistic regression. The time series training data fromthe selected residuals is labelled with fault and severity. It is then compressedusing a Gaussian parametric representation, and data from different fault modesare modelled using 1-class support vector machines. The classification of datais performed by utilizing the support vector machine description of the data inthe residual space, and the fault severity is estimated as a convex optimizationproblem of minimizing the Kullback-Leibler divergence (kld) between the newdata and training data of different fault modes and severities. The algorithm is tested with data collected from a commercial Volvo car enginein an engine test cell and the results are presented in this report. Initial testsindicate the potential of the kld for fault severity estimation and that noveltydetection performance is closely tied to the residual selection process.
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Gubran, Ahmed. "Vibration diagnosis of blades of rotating machines." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/vibration-diagnosis-of-blades-of-rotating-machines(40f1d466-b393-42f6-a65a-e16801f06920).html.
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Rotating blades are considered to be the one of the most common cause of failures in rotating machinery. Blade failure modes normally occur as a result of cracks due to unexpected operating conditions, which are normally caused by accidents of foreign objects damage, high cycle fatigue, blade rubbing, blade root looseness, and degradation from erosion and corrosion. Thus, detection of blade faults has an important role in reducing blade related failures and allowing repairs to be scheduled for the machinery. This in turn will lead to reduction in maintenance costs and thus raise productivity and safety aspects of operation. To maintain vital components of rotating machines, such as blades, shafts, bearings and gear boxes, at optimal levels, detection of failures in such components is important, because this will prevent any serious damage that could affect performance. This research study involves laboratory tests on a small rig with a bladed disc rotor that applied vibration measurements and analysis for blade fault detection. Three measurements: shaft torsional vibration, on-bearing vibration (OBV) and on-casing vibration (OCV), are used. A small test rig of a single stage bladed disc holding 8-blades was designed and manufactured, to carry out this research study to assess the usefulness and capability of each vibration technique in detection of incipient defects within machine blades. A series of tests was conducted on a test rig for three different cases of blade health conditions: (a) healthy blade(s) with mistuned effects, (b) blade root looseness and (c) cracks in a blade on two different blade sizes (long and short blades) in order to discover changes in blades' dynamic behaviour during the machine running-up operation. The data were collected using the three measurements during machine run-up and then recorded. The measured vibration data were analysed by computing the blades' resonance at different engine orders (EOs) related to the blade(s) resonance frequencies and their higher harmonics, to understand the blade(s) dynamics behaviour for the cases of healthy and faulty blade(s). Data have been further processed using a polar plot presentation method which provides clear results that can be used for monitoring blade integrity. To validate the obtained experimental results, a simplified mathematical model was also developed. Finally, a comparative study between three methods was undertaken to understand the relative advantages and limitations in the blade heath monitoring.
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Iannetti, Alessandra. "Méthodes de diagnostic pour les moteurs de fusée à ergols liquides." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS243.
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Cette thèse a pour objectif de démontrer l'intérêt des outils de diagnostic "intelligents" pour application sur les moteurs de fusée. En Europe beaucoup d'efforts ont été faits pour développer quelques techniques innovantes comme les réseaux neuronaux, les méthodes de suivi de raie vibratoire, ou l'identification paramétrique mais peu de résultats sont disponibles quant à la comparaison des performances de différents algorithmes. Un deuxième objectif de la thèse a été celui d'améliorer le système de diagnostic du banc d'essai Mascotte (ONERA/CNES). Il s'agit d'un banc de démonstration pour les moteurs de fusée de type cryogénique représentatif des conditions d'utilisation d'un vrai moteur. Les étapes de la thèse ont été en premier lieu de choisir et d'évaluer des méthodes de diagnostic à base de modèles, en particulier l'identification paramétrique et le filtre de Kalman, et de les appliquer pour le diagnostic d'un système critique du banc Mascotte: le circuit de refroidissement. Après une première validation des nouveaux algorithmes sur des données d'essais disponibles, un benchmark fonctionnel a été mis en place pour pouvoir comparer les performances des algorithmes sur différents types de cas de panne simulés. La dernière étape consiste à intégrer les algorithmes sur les ordinateurs du banc de contrôle de Mascotte pour pouvoir effectuer une évaluation applicative des performances et de leur intégrabilité à l'environnement informatique déjà en place. Un exemple simple de boucle de régulation intégrant l’information du diagnostic est aussi étudié pour analyser l’importance de telles méthodes dans le contexte plus large d’une régulation « intelligente » du bancThe main objective of this work is to demonstrate and analyze the potential benefits of advanced real time algorithms for rocket engines monitoring and diagnosis. In the last two decades in Europe many research efforts have been devoted to the development of specific diagnostic technics such as neural networks, vibration analysis or parameter identification but few results are available concerning algorithms comparison and diagnosis performances analysis.Another major objective of this work has been the improvement of the monitoring system of the Mascotte test bench (ONERA/CNES). This is a cryogenic test facility based in ONERA Palaiseau used to perform analysis of cryogenic combustion and nozzle expansion behavior representative of real rocket engine operations.The first step of the work was the selection of a critical system of the bench, the water cooling circuit, and then the analysis of the possible model based technics for diagnostic such as parameter identification and Kalman filters.Three new algorithms were developed, after a preliminary validation based on real test data, they were thoroughly analyzed via a functional benchmark with representative failure cases.The last part of the work consisted in the integration of the diagnosis algorithms on the bench computer environment in order to prepare a set-up for a future real time application.A simple closed loop architecture based on the new diagnostic tools has been studied in order to assess the potential of the new methods for future application in the context of intelligent bench control strategies
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TEIXEIRA, JÚNIOR Adalberto Gomes. "Modelagem acústica no auxílio ao diagnóstico do funcionamento de motores de usinas termoelétricas." Universidade Federal de Campina Grande, 2015. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/549.
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Capes
O som gerado por motores em funcionamento contém informações sobre seu estado e condições, tornando-se uma fonte importante para a avaliação de seu funcionamento sem a necessidade de intervenção no equipamento. A análise do estado do equipamento muitas vezes é realizada por diagnóstico humano, a partir da experiência vivenciada no ambiente ruidoso de operação. Como o funcionamento dos motores é regido por um processo periódico, o sinal de áudio gerado segue um padrão bem definido, possibilitando, assim, a avaliação de seu estado de funcionamento por meio desse sinal. Dentro deste contexto, a pesquisa ora descrita trata da modelagem do sinal acústico gerado por motores em usinas termoelétricas, aplicando técnicas de processamento digital de sinais e inteligência artificial, com o intuito de auxiliar o diagnóstico de falhas, minimizando a presença humana no ambiente de uma sala de motores. A técnica utilizada baseia-se no estudo do funcionamento dos equipamentos e dos sinais acústicos por eles gerados por esses, para a extração de características representativas do sinal, em diferentes domínios, combinadas a métodos de aprendizagem de máquinas para a construção de um multiclassificador, responsável pela avaliação do estado de funcionamento desses motores. Para a avaliação da eficácia do método proposto, foram utilizados sinais extraídos de motores da Usina Termoelétrica Borborema Energética S.A., no âmbito do projeto REPARAI (REPair over AiR using Artificial Intelligence, código ANEEL PD6471-0002/2012). Ao final do estudo, o método proposto demonstrou acurácia próxima a 100%. A abordagem proposta caracterizou-se, portanto, como eficiente para o diagnóstico de falhas, principalmente por não ser um método invasivo, não exigindo, portanto, o contato direto do avaliador humano com o motor em funcionamento.
The sound generated by an engine during operation contains information about its conditions, becoming an important source of information to evaluate its status without requiring intervention in equipment. The fault diagnosis of the engine usually is performed by a human, based on his experience in a noisy environment. As the operation of the engine is a periodic procedure, the generated signal follows a well-defined pattern, allowing the evaluation of its operating conditions. On this context, this research deals with modeling the acoustic signal generated by engines in power plants, using techniques from digital signal processing and artificial intelligence, with the purpose of assisting the fault diagnosis, minimizing the human presence at the engine room. The technique applied is based on the study of engines operation and the acoustic signal generated by them, extracting signal representative characteristics in different domains, combined with machine learning methods, to build a multiclassifier to evaluate the engines status. Signals extracted from engines of Borborema Energética S.A. power plant, during the REPARAI Project (REPair over AiR using Artificial Intelligence), ANEEL PD-6471-0002/2012, were used in the experiments. In this research, the method proposed has demonstrated an accuracy rate of nearly 100%. The approach has proved itself to be efficient to fault diagnosis, mainly by not being an invasive method and not requiring human direct contact with the engine.
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Ko, Ching-Po, and 葛慶柏. "Ontology Construction of Automotive Engine Fault Diagnosis." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/92454733074427413223.
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博士國立臺灣師範大學
工業教育學系
99
The purpose of this study was to construct an automotive engine fault diagnosis ontology model and a semantic search system based on ontology theory, building method and knowledge transfer pattern. In order to establish an engine system fault tree analysis diagram, this study explored the literature that is related to significant phenomenons and causes of engine failures through in-depth interviews and expert consultations to excavate implicit knowledge from an automotive repair and maintenance expert system. This paper presents basic automotive engine fault diagnosis frame for building ontology, which should enable using protégé on the semantic web. Users are provided simple and convenient steps when performing a diagnosis due to structured query language which are comprised of available protégé model descriptions and the query form interface search system of web pages. The described ontology guidelines are based on user needs that can be used to set up a platform for evaluation and application in the automobile industry or a technical school. The research study is expected to help students proceed problem-based learning and enhance technicians’ problem solving abilities.
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陳聖洲. "Neuro-fuzzy Fault Diagnosis for Gasoline Engine." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/98608109698686694169.
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碩士南台科技大學
機械工程系
91
In this work, the neuro-fuzzy algorithm is applied to the fault diagnosis of gasoline engine from the sensing system. The sensing voltages are normalized before feeding into the neural network. By using steepest descent method the adaptation laws of parameters of fuzzy set and output of the inference rules are achieved. Output parameters represent the possibility of the fault source. To aim directly at the faults of air flow sensor and O2 sensor, the fault conditions of overestimated or underestimated air flow and dense O2 density are detected by the proposed diagnostic system. In this thesis, the study is divided into two parts: 1. Numerical simulation: The engine state variables are collected when air-flow sensor, intake manifold pressure sensor or mass air flow sensor has fault. The collected state variables are used to build the database for the diagnostic system. 2. Experiment: The engine state variables are collected when O2 sensor, injecting time, advanced ignition angle or intake manifold pressure sensor has fault. The collected state variables are used to build the database for the diagnostic system. Through the experimental results, the effectiveness of the proposed fault diagnosis system is verified. The above results show that the diagnostic system with neuro-fuzzy technique can exactly determine the fault of sensor. The construction of diagnostic system can be used as a reference of gasoline engine of sensor fault diagnosis.
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Chen, Kuan Lin, and 陳冠霖. "Application of Neural networks in Fault Diagnosis of Gasoline Engine－Engine Leakage and Fault Air Flow Signal." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/52038098690782568368.
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碩士南台科技大學
機械工程系
100
In this paper, neural networks are employed to construct a gasoline engine fault diagnostic system including engine-state diagnostic system, fault source diagnostic system, air-flow sensor fault diagnostic system, leaking source diagnostic system, crankcase ventilation system leaking-degree diagnostic system and fuel pressure regulator leaking-degree diagnostic system The running engine is detected using engine-state diagnostic system to identify the state first, if the engine state is abnormal, distinguish the fault source by engine fault source diagnosis system. The fault sources include fault air-flow sensor and engine leakage. If it is fault air-flow sensor, air-flow-sensor fault-degree diagnostic system is employed to indentify the fault degree, otherwise use leaking source diagnosis system to identify the leaking source. Leaking sources are divided into leakage of crankcase ventilation system and leakage of fuel pressure regulator. If crankcase ventilation system leaks, detect the leaking degree using crankcase ventilation system leaking degree diagnosis system, otherwise use fuel pressure regulator leaking degree diagnosis system to identify the leaking degree. The experiment results show that the root-mean-square errors of diagnostic results are all less than 0.1%, therefore, the proposed gasoline engine fault diagnostic structure is feasible.
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Lin, Tung-Chou, and 林通洲. "Design of a Fault Diagnosis System for Diesel Engine Generators." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/ynn4as.
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碩士國立彰化師範大學
電機工程學系
98
The diesel generator set is composed of a diesel engine, synchronous generator and a control system. Once the diesel generator set is out of order, the availability of urgent power supply system will be affected. So it is important to improve the fault diagnosis ability of maintenance workers. Because the fault diagnosis is a complex knowledge, maintenance workers need a series of experience accumulation, and integrate information with inference and verification of fault diagnosis. The demanded knowledge and experience of fault diagnosis often exceed the affordable load taken by maintenance workers, and therefore it results in longer repair time or wrong diagnosis. To decrease the repair time and avoid the wrong diagnosis, this thesis proposed a microprocessor-based system to rapidly compare the possible fault from the expert diagnosis database. This proposed system can improve the diagnosis ability of maintenance workers, decrease the repair time, avoid the wrong diagnosis, promote the maintenance efficiency, and enhance the availability of diesel generator sets.
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Chang, Zhi-Wei, and 張志瑋. "Application of Neural Network on the Fault Diagnosis of Engine." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/79266066814217930823.
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碩士國立臺北科技大學
車輛工程系碩士班
91
An engine always produces a large amount of vibration and noise when engine in ill conditions. This research uses neural network to develop a fault diagnosis method by using features collected from spectral analysis of vibration signal of engine. Generally, the engine is a complicated system and has some nonlinear factors. It is very difficult to diagnose such system by mathematical analysis. The neural network has the capabilities of solving nonlinear problems, learning and memory. Therefore, the neural network is quite suitable for the purpose of fault diagnosis of engine. The experiments are carried out on a Mitsubishi Lancer with idle speed. Vibration signals of different fault are recorded with a signal acquisition instrument. The faults include injector failure, incorrect plug gap and spark timing. As the vibration signal is related to the engine speed, a multi-network model for engine diagnosis can be constructed by using amplitudes of vibration spectrum. The diagnosis is divided into two sections. The first section is to distinguish between degrees of each fault type. The second section is to identify fault type by using only two degrees as a pattern set. The objective of the latter is mainly to verify diagnostic result of networks by reducing the number of fault degrees in order to reduce time consumption of signal measurement and network learning. Due to possessing the generalized predictability, the network can identify similar patterns. After learning the networks are verified with new measured data and the results show that it not only can identify different types of fault but also can distinguish between degrees of fault. It also indicates that for certain types of engine fault it is not necessary to measure many different degrees of fault for the network learning.
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Cholette, Michael Edward. "Precedent-free fault isolation in a diesel engine EGR valve system." Thesis, 2009. http://hdl.handle.net/2152/ETD-UT-2009-12-610.
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An application of a recently introduced framework for isolating unprecedented
faults for an automotive engine EGR valve system is presented. Using
normal behavior data generated by a high fidelity engine simulation, the Growing
Structure Multiple Model System (GSMMS) is used to construct models of normal
behavior for EGR valve system and its various subsystems. Using the GSMMS
models as a foundation, anomalous behavior of the entire system is then detected
as statistically significant departures of the most recent modeling residuals from the
modeling residuals during normal behavior. By reconnecting anomaly detectors to
the constituent subsystems, the anomaly can be isolated without the need for prior
training using faulty data. Furthermore, faults that were previously encountered
(and modeled) are recognized using the same approach as the anomaly detectors.text
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Chang, Yo-Wei, and 張祐維. "Technique Development of Vehicle Engine Fault Diagnosis Integrated with Vibration Energy Harvest." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/vek4g7.
Full textAbstract:
博士國立臺北科技大學
機電科技研究所
101
In recent years, several On-Board Diagnostics of automotive engines, such as European On-Board Diagnostics (EOBD) and On-Board Diagnostics II (OBDII stands for ''Enhanced On-Board Diagnostics, Second Generation''), have been developed to the fault diagnostics of vehicle engines because of legislative regulations. To study the dynamic characteristics of engines, the vibration patterns were obtained through signal acquisition with accelerometers and analyzed via data processing. Also, the several techniques of signal process would be adopted to investigate engine faults. After the order tracking technique could construct the original dynamic patterns for engine vibrations; therefore, the fast Fourier transform and the wavelet transform were introduced to extract the vibrating feature based on the time–frequency domain analysis. Applying the similar concept, the pressure variations on an intake manifold of an internal combustion engine should also detected as fault diagnosis. These pressure signals were decomposed and reconstructed by the discrete wavelet transform (DWT) and the engine malfunction could be recognized via various techniques of artificial neural networks. Thus, the expert detection system is developed for engine fault detection. The phenomena of a periodical impact oscillation are the energy resource through the vibration harvest with piezoelectric smart materials. The power generation is available and experimental analysis is another design point of finite element ANSYS method in this paper. The testing platform was built up and used to prove the conversion efficiency from vehicle vibration energy to electrical power output by using a lever mechanism to simulate the oscillating situation from a vehicle operation actually, called as a vibration energy harvesting system.
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Hsu, Tzu-Cheng, and 徐自珍. "ENGINE GASPATH SENSOR DATA VALIDATION AND FAULT DIAGNOSIS USING ARTIFICIAL NEURAL NETWORKS." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/h4hj8q.
Full textAbstract:
博士國立成功大學
航空太空工程學系碩博士班
91
Gaspath analysis holds a central position in the engine condition monitoring (ECM) and fault diagnostics (FD) technique. However, popularization of this approach has been impeded when practical enforcements were tried in both civil and military sectors. Artificial neural network (ANN) arises as a new approach which avoids the fundamental difficulties associated with the classical model-based methods. The objective of the present work is to develop a reliable ANN-based diagnostic system that can be enforced in the practical applications. Back-propagation, feedforward neural nets are employed for constructing the engine diagnostic networks. Noise-contained training and testing data are generated using an influence coefficient matrix and the data scatters. The results indicate that for situations where sensor scatters are comparable to those of the normal engine operation, the success rates for both 4-input and 8-input ANN diagnoses achieve high scores which satisfy the requirement. The success rate of 4-input diagnosis is almost as good as that of the 8-input. Although the ANN-based method possesses certain capability in resisting the influence of input noise, the success rate of fault diagnosis still depends mainly on the quality of the measurements obtained. A high success rate of diagnosis can only be guaranteed when a correct set of measurement deltas is available. Thus, a design of a preprocessor that can perform sensor data validation is of paramount importance. To this end, the present work proposes a genetic auto-associative neural network algorithm that can perform off-line sensor data validation simultaneously for noise-filtering and bias detection and correction. Neural network-based sensor validation procedure usually suffers from the slow convergence in network training. In addition, the trained network often fails to provide an accurate accommodation when bias error is detected. To remedy these problems, the Levenberg-Marquardt (LM) algorithm is adopted to speed up the network training and a novel two-step approach is proposed for bias accommodation problems. The first step is the construction of a noise-filtering and a self-mapping auto-associative neural network based on the LMBP algorithm. It is shown that the noise can be greatly filtered by the noise-filtering auto-associative neural network. The second step uses an optimization procedure built on top of these noise-filtering and self-mapping nets to perform bias detection and correction. Non-gradient genetic algorithm search is employed as the optimization method. It is shown in the present work that effective sensor data validation can be achieved for noise-filtering, bias correction, and missing sensor data replacement incurred in the gaspath analysis. This newly developed algorithm can also serve as an intelligent trend detector. True performance delta and trend change can be identified with no delay to assure a timely and high-quality engine fault diagnosis.
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Kuo, Ching-Hui, and 郭慶輝. "Neural Network Condition Monitoring and Fault Diagnosis of A Turbofan Engine with AfterBurner." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/10971477559654839515.
Full textAbstract:
碩士樹德科技大學
資訊管理系碩士班
98
The purpose of this thesis is to develop a Neural Network Condition Monitoring and Fault Diagnosis system of a turbofan engine with afterburner. The semi-artificial sensing engine data are normalized and then feeding into the neural network. There are two model of our purposed system: 1. limited-model in which contends 4-node input and 5-node output parameters; 2.extented-model in which contends 6-node input and 7-node output parameters. By the using of gradient method, momentum term method and Levenberg Marquardt (LM) method, the results show excellent effectiveness and accuracy. This shows that the construction of purposed system can be used as a reference of the faultier diagnosis. As a result, in the case of limited-model, it shows smaller root mean square error in the network architetecture of a 21-node hidden layer neurons using LM algorithm and achieves 90% admeasure rate. In the case of extended-model, the network architetecture of the 25-node hidden layer neurons using LM algorithm can achieve 100% admeasure rate. Finally, the system is then applied for diagnosis of the turbofan engine with hot-section. The effectiveness of the proposed system is verified.
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Lin, Chang Ting, and 林昶廷. "A PN-Based Performance Detection and Fault Diagnosis Study for Military Turbine Engine." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/72940740361047989303.
Full textAbstract:
碩士國防大學理工學院
航空太空工程碩士班
99
In viewing the influence of the aircraft engine maintenance and overhaul (EMO) on the availability and flight safety for an airplane fleet, and in responding to the urge of new techniques to facilitate the task of the engine components fault diagnosis and isolation (FDI), this thesis presents a feasibility study with the aim to develop a more practical and graphical tool for the components FDI of a military gas turbine engine, so that it may be applied to successive EMO management and relevant studies. In content, the thesis begins with a literature survey of existing FDI researches for using in gas turbines, while the present idea of choosing the Petri Net (PN) graphic method to model the FDI working procedures at flight line is also introduced. Followed is an open literature study of the PN fundamentals and applications to turbine engine FDI, together with a PN-based workflow modeling in an intermediate level turbine engine workshop. Then is a PN-based case study of the failure to start problem of a turbofan, based on the engine technical manuals, with details in how to set up the PN model for the engine starting FDI procedure, how to generate the marked matrices for different faults, and how to apply the database for doing specific fault isolation. Although the present thesis is focused on the feasibility of using PN to facilitate the FDI task of a given military turbofan engine, typical achievements through this study may still be drawn as follows: (1) the setup of an operating process to translate conventional FDI documents into graphic type PN models, (2) the setup of a systematic way to simulate all possible faults or workflow interrupts that may happen, with characterized data output, (3) the proposal of an efficient and practical method for identifying the possible causes to lead the event of failure or fault, via computer-aided data pattern comparisons. As to extensions, a further step in the real engine EMO data collection and formalization is greatly encouraged, and the treatment of workflow time lag in the PN modeling part is also suggested.
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Huang, Jian-Wei, and 黃健瑋. "Study on Condition Monitoring and Fault Diagnosis Scheme for Combustion Process of Ship’s Main Diesel Engine." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/58fr99.
Full textAbstract:
碩士國立臺灣海洋大學
輪機工程學系
106
Main Diesel engine plays an important role for Ship’s energy consumption. The build-in alarm and monitoring system can only provide basic running information, issue alarm and/or provide emergency protections for running machines. In face of more complex engines, more intelligent monitoring system is required to monitor engine’s performance, provide systematic analysis for finding root cause when fault is occurred, so that constructive decision can be made for fault accommodation. In this way, human errors can be reduced for possible wrong decision. In this study, we focus on the combustion process of the main engine, Failure Mode and Effect Analysis (FMEA) principle is applied for system monitoring and fault diagnosis design. Physical models for main components of the main engine are studied, key factors that affecting the performance of main engine are sorts out, and fault trees for major failure modes are constructed. Analysis indexes and detection methods for each failure mode are also provided that, they are helpful on tracing root cause for poor performance. When there is any incipient fault or degraded performance occurred, we can monitor each analysis index to clarify situations, trace fault developing route on the fault tree for finding possible root cause, and aware its possible effects, so that constructive decision can be made for fault accommodation, and try to restore health of the main engine. Results in this study are helpful to marine engineer on mastering the operating condition, and provide effective management suggestions on the running main engine. Key words : Main Diesel Engine, Condition Monitoring, Fault Diagnosis, FMEA, Fault Tree.
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Sauer, Patrick. "Model-based fault detection and diagnosis for the fuel system of a six-cylinder heavy duty diesel engine." Phd thesis, 2021. https://tuprints.ulb.tu-darmstadt.de/18589/1/2021-05-10_Sauer_Patrick.pdf.
Full textAbstract:
Nowadays, the fault diagnosis in modern internal combustion engines is becoming increasingly important. The constant development of engines, particularly in terms of fuel efficiency, and more stringent regulations of exhaust emissions, are leading to more complex systems. This enormous increase in complexity restricts the efficiency of conventional diagnosis systems, such as the limit checking of sensors. This thesis deals with the development of a fuel diagnosis system for a heavy duty diesel engine using advanced signal model- and process model-based methods.
The diagnosis system has been developed for serial operation, which results in various limitations. One of these limitations is the lack of integration of additional sensors to monitor intermediate states. Furthermore, the low pressure pump, as well as the rail pressure control operate in closed loop control to ensure best possible results for the controlled variable. However, closed loop controls compensate for minor faults.
In this thesis, physical models for various components in the fuel system have been developed which form the basis for model-based development. These models are used for developing algorithms to monitor the low pressure and high pressure pump, fuel filters, various leakages and the
rail flow valve. Additionally, the model-based parameters generated additional information to help characterize the faults during the fault diagnosis.
For signal model-based development, the frequency components of the periodic rail pressure and exhaust pressure signals were analyzed in high resolution. With this it is possible to extract algorithms to monitor injector flow and compression losses in internal combustion cylinders. This signal model-based methods provide additional information not only during fault detection but also during fault diagnosis, which allows the exact location of the observed fault to be determined.
Residuals have been created using the developed algorithms, which represent a deviation of the system to be monitored from the normal state. Residuals formed using process and signal models have been created, which represent the inputs of the fault diagnosis. An inference based fault diagnosis was used to determine the fault characteristics, such as the type and location of the fault, in order to isolate these faults.
The fuel diagnosis system was implemented, parameterized, tested and validated at the engine test bench. Various faults in the engine fuel system were generated as authentic as possible. These were detected by the process and signal model-based fault detection and the individual faults were identified and isolated. The tools for identification and isolation were fault-trees, which belong to the category of inference methods. Also useful for isolating individual faults are fault-symptom-tables that contain the full symptoms of all implemented faults. In particular, the method for isolating the injector flow faults provided an appropriate contribution to clearly identify the location of the fault.
Finally, a strategy was developed for the online compensation of various injectors flow faults, which allows the short-term compensation of these faults. This ensures a reliable operation of the engine until the next workshop stay. In the commercial vehicle industry this is of great importance in order to minimize the additional costs of machine downtime.
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Yang, Chi-Liang, and 楊啟良. "Diagnosis of Engine Gas-path with Multiple Faults." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/jp27kw.
Full textAbstract:
碩士國立成功大學
航空太空工程學系碩博士班
90
This research presents the utilization of a backpropagation neural network (BNN) as a fault diagnosis system for detecting multiple faults based on the measurements of gas-path variables of an engine. The influence coefficient matrix of Pratt & Whitney PW4000-94” engine was employed to generate the fault patterns for training and testing a multi-layered neural network. In each generated pattern, due to multiple simultaneous faults, some of the faults may have dominant effects on the values of input variables; that is, unless the faults are present with comparable severity which generate measurement deltas with same order of magnitude, the network may classify the minor faults as less significant noise to the major faults. Thus, those unidentifiable fault patterns were deleted from the training process to avoid incorrect classifications. Computer simulations were conducted to experiment two network structures, one with four input variables and the other one with eight input variables. Because some of the generated fault patterns with four input variables may contain contradictions in the input-output mapping relationship—similar input deltas map onto different output fault types, the network structure with eight input variables was adopted as the diagnosis system and recommended for multiple faults detection and isolation. The results of computer simulations have validated the effectiveness of the proposed diagnosis system for isolating multiple faults of engine gas-path with satisfactory accuracy.
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Mohammadi, Rasul. "Fault diagnosis of hybrid systems with applications to gas turbine engines." Thesis, 2009. http://spectrum.library.concordia.ca/976314/1/NR63443.pdf.
Full textAbstract:
Stringent reliability and maintainability requirements for modern complex systems demand the development of systematic methods for fault detection and isolation. Many of such complex systems can be modeled as hybrid automata. In this thesis, a novel framework for fault diagnosis of hybrid automata is presented. Generally, in a hybrid system, two types of sensors may be available, namely: continuous sensors supplying continuous-time readings (i.e., real numbers) and threshold sensitive (discrete) sensors supplying discrete outputs (e.g., level high and pressure low). It is assumed that a bank of residual generators (detection filters) designed based on the continuous model of the plant is available. In the proposed framework, each residual generator is modeled by a Discrete-Event System (DES). Then, these DES models are integrated with the DES model of the hybrid system to build an Extended DES model. A "hybrid" diagnoser is then constructed based on the extended DES model. The "hybrid" diagnoser effectively combines the readings of discrete sensors and the information supplied by residual generators (which is based on continuous sensors) to determine the health status of the hybrid system. The problem of diagnosability of failure modes in hybrid automata is also studied here. A notion of failure diagnosability in hybrid automata is introduced and it is shown that for the diagnosability of a failure mode in a hybrid automaton, it is sufficient that the failure mode be diagnosable in the extended DES model developed for representing the hybrid automaton and residual generators. The diagnosability of failure modes in the case that some residual generators produce unreliable outputs in the form of false alarm or false silence signals is also investigated. Moreover, the problem of isolator (residual generator) selection is examined and approaches are developed for computing a minimal set of isolators to ensure the diagnosability of failure modes. The proposed hybrid diagnosis approach is employed for investigating faults in the fuel supply system and the nozzle actuator of a single-spool turbojet engine with an afterburner. A hybrid automaton model is obtained for the engine. A bank of residual generators is also designed, and an extended DES is constructed for the engine. Based on the extended DES model, a hybrid diagnoser is constructed and developed. The faults diagnosable by a purely DES diagnoser or by methods based on residual generators alone are also diagnosable by the hybrid diagnoser. Moreover, we have shown that there are faults (or groups of faults) in the fuel supply system and the nozzle actuator that can be isolated neither by a purely DES diagnoser nor by methods based on residual generators alone. However, these faults (or groups of faults) can be isolated if the hybrid diagnoser is used.
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Abbasfard, Zahra /. ZA. "Fault Diagnosis of Gas Turbine Engines by Using Multiple Model Approach." Thesis, 2013. http://spectrum.library.concordia.ca/977061/1/Abbasfard_MASc_S2013.pdf.
Full textAbstract:
The field of fault detection and isolation (FDI) has attracted much attention in control theory during the last three decades which has resulted in development of sophisticated FDI algorithms. However, increasing the complexity of FDI algorithms is not necessarily feasible. Particularly for on-line FDI, the FDI unit must have the minimum possible computation cost to prevent any long delays in fault detection.
In this research, we try to address the FDI problem of a single spool jet engine by using a modified linear multiple model (MM). We first develop a novel symbolic computation-based method for linearization purposes such that the obtained linear models are subjected to the symbolic fault variables. By substituting certain values for these symbolic variables, one can obtain different linear models, which describe mathematically the healthy and faulty models. In order to select the operating point, we use this fact that for a given constant fuel flow (W_f), the system reaches a steady state, that is varying for different values of W_f. Therefore, the operating points for linearization can be determined by the level of the Power Level Angel (PLA) (different values of W_f). These operating points are selected such that an observer, which is designed as a candidate for the healthy mode, can accurately estimates the states of the system in healthy scenario and the number of false alarm then would be kept to minimum. If the system works at different operating points one can then discretize the W_f into different intervals such that in each interval a linear model represents the behavior of the original system. By using the obtained models for different operating points, one designs the corresponding FDI units.
Second, we provide a modified multiple model (MM) approach to investigate the FDI problem of a single spool jet engine. The main advantage of this method lies in the fact that the proposed MM consists of a certain set of linear Kalman filter banks rather than using nonlinear Kalman filters such as the Extended Kalman Filter which requires more computational cost. Moreover, a hierarchical structural multiple model is used to detect and isolate multiple faults. The simulation results show the capability of the proposed method when it is applied to a single spool jet engine model.
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KUO, TSUNG-LIEN, and 郭宗亷. "The Fault Diagnostic Applications of Marine Main Engine based on Neural Network - An Example of Yanmar 6EY22AW." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/38218896816531953959.
Full textAbstract:
碩士國立高雄海洋科技大學
輪機工程研究所
104
This research presents a kind of fault diagnosis mechanism on the basis of neural network to be an aid for marine engineers to solve the malfunction of Main Engine. The connection for condition and cause of faults provided by engine maker has been established by using the learning mechanism of traditional neural network in this research. After appropriate training, a neural network can provide rapid diagnostic analysis to assist the on-duty engineers to find possible causes as soon as possible. The time to find the cause of malfunction can be reduced especially for complicated multiple faults. It has a major improvement for the security of the ship. In the thesis, the availability of this research will be shown by some simulations.