Relevant bibliographies by topics / Engine fault diagnosis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Engine fault diagnosis'

Author: Grafiati
Published: 2 July 2021
Last updated: 4 February 2022

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Journal articles on the topic "Engine fault diagnosis"

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Cheng, Jian, Chuan Mei Bao, Yi Su Huang, Ye Sun, and Zhe Jing Yi. "Fuzzy Diagnosis Method of Aero-Engine Fault." Advanced Materials Research 1037 (October 2014): 147–51. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.147.

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A diagnosis method of aero-engine faults based on Mamdani fuzzy inference is proposed in this paper. Regarding the fault symptoms of aero-engines as input of fuzzy inference, the proposed method establishes rules of inference from experts’ experience and distills the implication relationships. On this basis, the fault symptoms are combined with the implication relationships to obtain the probability of fault causes, so as to achieve the diagnosis of aero-engine faults. The results of experiments showed that the method is feasible and effective.
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Adaileh, Wail M. "Engine Fault Diagnosis Using Acoustic Signals." Applied Mechanics and Materials 295-298 (February 2013): 2013–20. http://dx.doi.org/10.4028/www.scientific.net/amm.295-298.2013.

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This work presents an experimental study to detecting the faults of engine using its noise. The noises produced by the engine and its accessory systems are numerous: whines, squeals, knock, rattles, and many other sounds. Faults diagnosis for Mitsubishis car engine model 2006 has been conducted and this diagnosis includes normal operating conditions for the engine (without malfunction) and for malfunctions situations at variable engine speed 1000,2000, 3000 and 4000 rpm respectively The engine data is acquired from a four cylinder one- petrol engine test bed under consideration at different operating states, and then simulated. Most of the conventional fault diagnosis techniques using sound emission and vibration signals are based on analyzing the signal amplitude in the time or frequency domain. For engine under fired and misfires spark the all the domain parameters (RMS amplitude, peak amplitude and energy) was processed using MATLAB software.It was found that fault detection and diagnosis for internal combustion engines is complicated by the presence of engine noise during normal operation. The average of amplitude found to be 450 x10-3m for normal engine working without any malfunction and 458x10-3m for misfire of one spark plug and for misfire of two spark plugs 457.8 x10-3m. In this study, some of the engine malfunction such as failure spark plug has been recorded, but we can generalize it to include all engine breakdown. Generally, sound emission signal serves as a promising alternative to the condition monitoring and fault diagnosis in rotating machinery when the vibration signal is not available. This research paper explores that automobiles could be major sources of noise pollution. Condition monitoring and fault diagnosis of IC engine through acoustic signal analysis is an established technique for detecting early stages of component degradation.
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Antory, D., U. Kruger, G. Irwin, and G. McCullough. "Fault diagnosis in internal combustion engines using non-linear multivariate statistics." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 219, no. 4 (June 1, 2005): 243–58. http://dx.doi.org/10.1243/095965105x9614.

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This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce non-linear relationships between the recorded engine variables, the paper proposes the use of non-linear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new non-linear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady state operating conditions. More precisely, non-linear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (a) an enhanced identification of potential root causes of abnormal events and (b) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA-based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, while the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 litre diesel engine mounted on an experimental engine test bench facility.
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Chen, Jian, Robert Randall, Bart Peeters, Wim Desmet, and Herman Van der Auweraer. "Artificial Neural Network Based Fault Diagnosis of IC Engines." Key Engineering Materials 518 (July 2012): 47–56. http://dx.doi.org/10.4028/www.scientific.net/kem.518.47.

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Fault diagnosis is important to avoid unforeseen failures of IC engines, but normally requires an expert to interpret analysis results. Artificial Neural Networks are potential tools for the automated fault diagnosis of IC engines, as they can learn the patterns corresponding to various faults. Most engine faults can be classified into two categories: combustion faults and mechanical faults. Misfire is a typical combustion fault; piston slap and big end bearing knock are common mechanical faults. The automated diagnostic system proposed in this paper has three main stages, each stage including three neural networks. The first stage is the fault detection stage, where the neural networks detect whether there are faults in the engine and if so which kind. In the second stage, based on the detection results, the severity of the faults was identified. In the third stage, the neural networks localize which cylinder has a fault. The critical thing for a neural network is its input feature vector, and a previous study had indicated a number of features that should differentiate between the different faults and their location, based on advanced signal processing of the vibration signals measured for different normal and fault conditions. In this study, an advanced feature selection technology was employed to select the significant features as the inputs to networks. The input vectors were separated into two groups, one for training the network, and the other for its validation. Finally it has been demonstrated that the neural network based system can automatically differentiate and diagnose a number of engine faults, including location and severity.
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Zabihi-Hesari, Alireza, Saeed Ansari-Rad, Farzad A. Shirazi, and Moosa Ayati. "Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 6 (June 3, 2018): 1910–23. http://dx.doi.org/10.1177/0954406218778313.

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This paper presents a condition monitoring and combustion fault detection technique for a 12-cylinder 588 kW trainset diesel engine based on vibration signature analysis using fast Fourier transform, discrete wavelet transform, and artificial neural network. Most of the conventional fault diagnosis techniques in diesel engines are mainly based on analyzing the difference of vibration signals amplitude in the time domain or frequency spectrum. Unfortunately, for complex engines, the time- or frequency-domain approaches do not provide appropriate features solely. In the present study, vibration signals are captured from both intake manifold and cylinder heads of the engine and were analyzed in time-, frequency-, and time–frequency domains. In addition, experimental data of a 12-cylinder 588 kW diesel engine (of a trainset) are captured and the proposed method is verified via these data. Results show that power spectra of vibration signals in the low-frequency range reliably distinguish between normal and faulty conditions. However, they cannot identify the fault location. Hence, a feature extraction method based on discrete wavelet transform and energy spectrum is proposed. The extracted features from discrete wavelet transform are used as inputs in a neural network for classification purposes according to the location of sensors and faults. The experimental results verified that vibration signals acquired from intake manifold have more potential in fault detection. In addition, the capacity of discrete wavelet transform and artificial neural network in detection and diagnosis of faulty cylinders subjected to the abnormal fuel injection was revealed in a complex diesel engine. Beside condition monitoring of the engine, a two-step fault detection method is proposed, which is more reliable than other one-step methods for complex engines. The average condition monitoring performance is from 93.89% up to 99.17%, based on fault location and sensor placement, and the minimum classification performance is 98.34%.
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Tian, Feng, Wen Jie Li, Zhi Gang Feng, and Rui Zhang. "Fault Diagnosis for Aircraft Engine Based on SVM Multiple Classifiers Fusion." Applied Mechanics and Materials 433-435 (October 2013): 607–11. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.607.

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Support vector machine (SVM) could well solve the over-learning and the low generalization ability of the neural network. But the single classifier cannot achieve satisfactory recognition rate and anti-interference ability. An aircraft engine fault diagnosis method based on support vector machine multiple classifiers is proposed in this paper. Firstly, sample characteristic information which constitutes the fault feature vectors obtained from the existing engine fault. Then, after training the SVM multiple classifier by faulty feature vectors, the SVM model of the fault diagnosis system is established; Finally, the trained SVM multiple classifier is used to recognize and classify the test faults. Applying the noise on the test samples, SVM multiple classifiers can still get a good diagnosis effect. It shows that the fault diagnosis algorithm has good robustness and can be applied to the study of aero engine fault diagnosis.
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Sun, Zhao Rong, Yi Gang Sun, and Chun Lin Sun Sun. "Research of Hard Fault Diagnosis Simulation Platform of Aero-Engine's Key Sensors Based on Neural Network." Applied Mechanics and Materials 391 (September 2013): 150–54. http://dx.doi.org/10.4028/www.scientific.net/amm.391.150.

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The purpose of the research is to establish a fault diagnosis model of the aero-engines key sensors using the artificial neural networks to replace the engines mathematical model, so as to establish a hard fault diagnosis simulation platform to monitor the performances of the engine sensors on real-time, to judge the engine failure mode timely, and to locate the fault type of sensors accurately. By analyzing the correlations of the parameters that affect the conditions of the engine, a three-layer BP network model is established. The related QAR (Quick Access Recorder) data are used to simulate and analyze the models using the MATLAB. Combined with the characteristics of the hard failure of the critical engine sensors and the correlation of the parameters, the fault diagnosis simulation platform is established. Then, the parameters of the normal engine and the failure engine are used respectively to evaluate and validate the platform. The simulation results show that the platform can judge the critical sensors faults of the engine accurately, and can locate the type of sensors reliably.
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Skliros, Christos. "A CASE STUDY OF VIBRATION FAULT DIAGNOSIS APPLIED AT ROLLS-ROYCE T-56 TURBOPROP ENGINE." Aviation 23, no. 3 (January 17, 2020): 78–82. http://dx.doi.org/10.3846/aviation.2019.11900.

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Gas turbine engines include a plethora of rotating modules, and each module consists of numerous components. A component’s mechanical fault can result in excessive engine vibrations. Identification of the root cause of a vibration fault is a significant challenge for both engine manufacturers and operators. This paper presents a case study of vibration fault detection and isolation applied at a Rolls-Royce T-56 turboprop engine. In this paper, the end-to-end fault diagnosis process from starting system faults to the isolation of the engine’s shaft that caused excessive vibrations is described. This work contributes to enhancing the understanding of turboprop engine behaviour under vibration conditions and highlights the merit of combing information from technical logs, maintenance manuals and engineering judgment in successful fault diagnosis.
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Aretakis, N., K. Mathioudakis, and A. Stamatis. "Nonlinear Engine Component Fault Diagnosis From a Limited Number of Measurements Using a Combinatorial Approach." Journal of Engineering for Gas Turbines and Power 125, no. 3 (July 1, 2003): 642–50. http://dx.doi.org/10.1115/1.1582494.

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A method for diagnosing component faults of jet engines is presented. It uses nonlinear gas path analysis techniques to determine the values of health parameters, with the help of a suitably formulated engine simulation model. The incentive of the method is to achieve the determination of the values of component health indices when a limited number of measured quantities is available, which do not allow the determination of all the fault indices simultaneously. A combinatorial approach is introduced, in order to circumvent the problem of the insufficient information for determining a full set of indices. After obtaining a set of possible solutions, a selection procedure is applied to isolate the ones that can give the actual fault identity. Quantification of the fault comes at a final step, when the faulty component has been identified. Different scenarios of faults on a twin spool partially mixed turbofan engine are considered in order to demonstrate the effectiveness of the method. The limitations of the method are also discussed.
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Wang, Bo, Hongwei Ke, Xiaodong Ma, and Bing Yu. "Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine." Applied Sciences 9, no. 19 (October 2, 2019): 4122. http://dx.doi.org/10.3390/app9194122.

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Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data sample for engine fault diagnosis is established. Finally, the important parameters of PNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and compared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively diagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston engine control system.
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Dissertations / Theses on the topic "Engine fault diagnosis"

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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.
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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.
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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.

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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.
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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.

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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.

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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.

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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.
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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.
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Books on the topic "Engine fault diagnosis"

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Litt, John. Sensor fault detection and diagnosis simulation of a helicopter engine in an intelligent control framework. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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author, Tian Gan, Xu Zhigao author, and Yang Zhengwei author, eds. Ye ti dao dan fa dong ji gu zhang te xing fen xi yu zhen duan: Failure Characteristics Analysis and Fault diagnosis for Liquid Missile Engine. Beijing Shi: Guo fang gong ye chu ban she, 2014.

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Green, Michael D. Model-based fault diagnosis for turboshaft engines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Green, Michael D. Model-based fault diagnosis for turboshaft engines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Green, Michael D. Model-based fault diagnosis for turboshaft engines. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Ferguson, David. Diesel engines fault finding & diagnostics manual. Hereford: Porter Publishing, 1998.

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Zhang, Wei. Failure Characteristics Analysis and Fault Diagnosis for Liquid Rocket Engines. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49254-3.

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Bickmore, Timothy W. SSME HPOTP post-test diagnostic system enhancement project. Cleveland, Ohio: Lewis Research Center, 1995.

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Ye ti huo jian fa dong ji gu zhang jian ce zhen duan li lun yu fang fa: Theory an Method of Fault Dection and Diagnosis for Liquid- propellant Rocket Engines. Beijing: Guo fang gong ye chu ban she, 2013.

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A, Duyar, and United States. National Aeronautics and Space Administration., eds. Implementation of a model based fault detection and diagnosis for actuation faults of the space shuttle main engine. [Washington, DC: National Aeronautics and Space Administration, 1991.

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Book chapters on the topic "Engine fault diagnosis"

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Isermann, Rolf. "Fault-tolerant components." In Combustion Engine Diagnosis, 269–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-49467-7_8.

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Denton, Tom. "Engine systems." In Advanced Automotive Fault Diagnosis, 143–226. Fifth edition. | Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.1201/9780429317781-6.

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Isermann, Rolf. "Supervision, fault-detection and fault-diagnosis methods – a short introduction." In Combustion Engine Diagnosis, 25–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-49467-7_2.

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Isermann, Rolf. "Terminology in fault detection and diagnosis." In Combustion Engine Diagnosis, 295–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-49467-7_9.

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Wu, Xiaobing, Xueshan Gao, and Dharmendra Sharma. "A Multiagent Based Vehicle Engine Fault Diagnosis." In Lecture Notes in Computer Science, 541–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74827-4_68.

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Zhu, Feixiang, Benwei Li, Zhao Li, and Yun Zhang. "Sensor Fault Diagnosis and Classification in Aero-engine." In Proceedings of the First Symposium on Aviation Maintenance and Management-Volume I, 397–411. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54236-7_45.

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Singh, Vrijendra, and Narendra Meena. "Engine Fault Diagnosis using DTW, MFCC and FFT." In Proceedings of the First International Conference on Intelligent Human Computer Interaction, 83–94. New Delhi: Springer India, 2009. http://dx.doi.org/10.1007/978-81-8489-203-1_6.

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Wang, Fengli, and Shulin Duan. "Fault Diagnosis of Diesel Engine Using Vibration Signals." In Communications in Computer and Information Science, 285–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18134-4_46.

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Zhang, Wei. "Fault Prediction Methods of Liquid Rocket Engine (LRE)." In Failure Characteristics Analysis and Fault Diagnosis for Liquid Rocket Engines, 307–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49254-3_11.

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Cheng, Rui, and Jiayuan Dan. "Missile Turbofan Engine Fault Diagnosis Technology and Its Application." In Advances in Intelligent Systems and Computing, 751–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37835-5_65.

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Conference papers on the topic "Engine fault diagnosis"

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Loboda, Igor, Sergey Yepifanov, and Yakov Feldshteyn. "An Integrated Approach to Gas Turbine Monitoring and Diagnostics." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51449.

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This paper presents an investigation of a conventional gas turbine diagnostic process and its generalization. A usual sequence of diagnostic actions consists of two stages: monitoring (fault detection) and subsequent proper diagnosis (fault identification). Such an approach neither implies fault identification nor uses the information about incipient faults unless the engine is recognized as faulty. In previous investigations for engine steady state operation conditions we addressed diagnostics problems without their relation with the monitoring process. Fault classes were given by samples of patterns generated by a static gas turbine performance model. This fault simulation took into account faults of varying severity including incipient ones. A diagnostic algorithm employed artificial neural networks to identify an actual fault. In the present paper we consider the monitoring and diagnosis as joint processes extending our previous approach over both of them. It is proposed to form two classes for the monitoring using the above-mentioned classes constructed for the diagnosis. A two-shaft industrial gas turbine has been chosen to test the proposed integrated approach to monitoring and diagnosis. A general recommendation following from the presented investigation is to identify faults simultaneously with fault detection. This permits accumulating preliminary diagnoses before the engine faulty condition is detected and a rapid final diagnosis after the fault detection.
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Davison, Craig R., and A. M. Birk. "Automated Fault Diagnosis for Small Gas Turbine Engines." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30029.

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In one possible model of distributed power generation a large number of users will operate individual, gas turbine powered, cogeneration systems. These systems will be small, relatively inexpensive, and installed in locations without ready access to gas turbine maintenance experts. Consequently an automated method to monitor the engine and diagnose its health is required. To remain compatible with the low cost of the power system the diagnostics must also be relatively inexpensive to install and operate. Accordingly a minimum number of extra sensors should be used and the analysis performed by a common personal computer system. The current work automates the diagnosis of component faults by comparing the engine’s operating trends to the trends for known faults. This allows the relative percentage chance of each fault occurring to be determined. The likelihood of each fault is then compared, to determine which component is degrading. The technique can be adapted to compare the engines historic operating trend or a single operating point. In this initial work a computer model was used as a test bed and 5 faults were introduced individually. The technique successfully diagnosed the faulty component using either the operating trend or a single operating point.
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Aretakis, N., I. Roumeliotis, and K. Mathioudakis. "Performance Model “Zooming” for In-Depth Component Fault Diagnosis." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23262.

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A method giving the possibility for a more detailed gas path component fault diagnosis, by exploiting the “zooming” feature of current performance modelling techniques, is presented. A diagnostic engine performance model is the main tool that points to the faulty engine component. A diagnostic component model is then used to identify the fault. The method is demonstrated on the case of compressor faults. A 1-D model based on the “stage stacking” approach is used to “zoom” into the compressors, supporting a 0-D engine model. A first level diagnosis determines the deviation of overall compressor performance parameters, while “zooming” calculations allow a localization of the faulty stages of a multistage compressor. The possibility to derive more detailed information with no additional measurement data is established, by incorporation of empirical knowledge on the type of faults that are usually encountered in practice. Although the approach is based on known individual diagnostic methods, it is demonstrated that the integrated formulation provides not only higher effectiveness but also additional fault identification capabilities.
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Ogaji, S. O. T., Y. G. Li, S. Sampath, and R. Singh. "Gas Path Fault Diagnosis of a Turbofan Engine From Transient Data Using Artificial Neural Networks." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38423.

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Transient and steady state data may contain the same essential fault information but some faults have been shown to be more easily detectable from transient data because the transient records provide significant diagnostic content especially as the fault effects are magnified under transient. Various traditional and conventional techniques such as fault trees, fault matrixes, gas path analysis and its variants have been applied to gas path fault diagnosis of gas turbines. Recently, artificial intelligence techniques such as artificial neural networks (ANN) as well as optimization techniques such as genetic algorithm (GA) are being explored for fault diagnosis activities. In this paper, a novel approach to gas path fault diagnosis is proposed. The method involves the use of ANN with engine transient data. A set of nested neural networks designed to estimate independent parameter (efficiencies and flow capacities) changes due to faults within single or multiple components of a turbofan engine are presented. The approach involves classification and approximation type networks. Measurements from the engine are first assessed by a trained network and if a fault is diagnosed, are then classified into two groups — those originating from sensor faults and those from component faults, by another trained network. Other trained networks continue the fault isolation process and finally the magnitude of the fault(s) is quantified. A computer simulation of the process shows that results from a batched process of these networks can be obtained in less than three seconds. Four of the gas path components — intermediate pressure compressor (IPC), high pressure compressor (HPC), high pressure turbine (HPT) and low pressure turbine (LPT) — and measurements from eight sensors are considered. Sensor noise and bias are also considered in this analysis. The comparison of fault signatures from a steady state and transient process show that diagnosis with transient data can improve the accuracy of gas turbine fault diagnosis.
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Tan, Daoliang, Ai He, Xiangxing Kong, and Xi Wang. "Integration of Unknown Input Observers and Classification for Turbofan Engine Diagnosis." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46429.

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A great deal of attention has been attracted in the analytical model-based engine diagnostics over the past years. Meanwhile, an increasing number of researchers and practitioners make an attempt to gain an intelligent diagnoser in a pattern recognition way. A question naturally emerges of how to combine the two techniques to improve the robustness of an on-board diagnostic system. In this context, this paper suggests an integrated approach that combines the unknown input observer (UIO) with the support vector machine (SVM) technique to aircraft engine fault diagnosis. Sensor faults and actuator faults are separately considered. To reduce the effect of engine disturbances on diagnostic performance, we first design a bank of UIOs, each of which is sensitive to all sensor and actuator faults but only one signal. Then, the magnitudes of a set of residuals between the UIO-based estimations and the engine measurements are fed into an SVM classifier to detect and isolate engine faults. Experimental results demonstrate an encouraging potential of the suggested method and that the UIO-oriented approach is superior or competitive to the Kalman-based algorithm.
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Li, Wenfei, and Rama K. Yedavalli. "Dynamic Threshold Method Based Aircraft Engine Sensor Fault Diagnosis." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2262.

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It is challenging to have a good fault diagnostic scheme that can distinguish between model uncertainties and occurrence of faults, which helps in reducing false alarms and missed detections. In this paper, a dynamic threshold algorithm is developed for aircraft engine sensor fault diagnosis that accommodates parametric uncertainties. Using the robustness analysis of parametric uncertain systems, we generate upper-and-lower bound trajectories for the dynamic threshold. The extent of parametric uncertainties is assumed to be such that the perturbed eigenvalues reside in a set of distinct circular regions. Dedicated observer scheme is used for engine sensor fault diagnosis design. The residuals are errors between estimated state variables from a bank of Kalman filters. With this design approach, the residual crossing the upper-and-lower bounds of the dynamic threshold indicates the occurrence of fault. Application to an aircraft gas turbine engine Component Level Model clearly illustrates the improved performance of the proposed method.
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Mohammadi, Rasul, Shahin Hashtrudi-Zad, and Khashayar Khorasani. "Hybrid Fault Diagnosis: Application to a Gas Turbine Engine." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-60075.

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This paper presents a hybrid framework for fault diagnosis of complex systems that are modeled by hybrid automata. A bank of residual generators is constructed based on the continuous models of the system. Each residual generator is modeled by a discrete-event system (DES). Next, the DES models of the residual generators and the DES model of the hybrid plant are combined to build an “extended DES” model. A hybrid diagnoser is constructed based on the extended DES model. The hybrid diagnoser effectively combines the readings of discrete sensors and the information supplied by the residual generators (which is based on continuous sensors) to determine the health status of the hybrid plant. The hybrid diagnosis approach is employed to investigate faults in the fuel supply system and the nozzle actuator of a single-spool turbojet engine with an afterburner.
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Zhang, Xiaodong, Remus C. Avram, Liang Tang, and Michael J. Roemer. "A Unified Nonlinear Approach to Fault Diagnosis of Aircraft Engines." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-95803.

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Many existing aircraft engine diagnostic methods are based on linearized engine models. However, the dynamics of aircraft engines are highly nonlinear and rapidly changing. Future engine health management designs will benefit from new methods that are directly based on intrinsic nonlinearities of the engine dynamics. In this paper, a fault detection and isolation (FDI) method is developed for aircraft engines by utilizing nonlinear adaptive estimation and nonlinear observer techniques. Engine sensor faults, actuator faults and component faults are considered under one unified nonlinear framework. The fault diagnosis architecture consists of a fault detection estimator and a bank of nonlinear fault isolation estimators. The fault detection estimator is used for detecting the occurrence of a fault, while the bank of fault isolation estimators is employed to determine the particular fault type or location after fault detection. Each isolation estimator is designed based on the functional structure of a particular fault type under consideration. Specifically, adaptive estimation techniques are used for designing the isolation estimators for engine component faults and actuator faults, while nonlinear observer techniques are used for designing the isolation estimators for sensor faults. The FDI architecture has been integrated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) engine model developed by NASA researchers in recent years. The engine model is a realistic representation of the nonlinear aero thermal dynamics of a 90,000-pound thrust class turbofan engine with high-bypass ratio and a two-spool configuration. Representative simulation results and comparative studies are shown to verify the effectiveness of the nonlinear FDI method.
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Romessis, C., and K. Mathioudakis. "Bayesian Network Approach for Gas Path Fault Diagnosis." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53801.

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A method for solving the gas path analysis problem of jet engine diagnostics based on a probabilistic approach is presented. The method is materialized through the use of a Bayesian Belief Network (BBN). Building a BBN for gas turbine performance fault diagnosis requires information of a stochastic nature expressing the probability of whether a series of events occurred or not. This information can be extracted by a deterministic model and does not depend on hard to find flight data of different faulty operations of the engine. The diagnostic problem and the overall diagnostic procedure are first described. A detailed description of the way the diagnostic procedure is set-up, with focus on building the BBN from an engine performance model, follows. The case of a turbofan engine is used to evaluate the effectiveness of the method. Several simulated and benchmark fault case scenarios have been considered for this reason. The examined cases demonstrate that the proposed BBN-based diagnostic method composes a powerful tool. This work also shows that building a diagnostic tool, based on information provided by an engine performance model, is feasible and can be efficient as well.
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Hu, Xiao, Neil Eklund, and Kai Goebel. "A Data Fusion Approach for Aircraft Engine Fault Diagnostics." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27941.

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Accurate and timely detection and identification of aircraft engine faults is critical to keeping the engine and aircraft in a healthy operating state. Early detection of faults increases the window of opportunity to schedule maintenance actions both at a convenient time and before the fault progresses and causes equipment downtime and secondary damage to the system. Typically, diagnostic models are built using parametric sensor data to infer the state of the system. However, recording and collecting this data is costly, and it is generally limited to a few snapshots over the course of a flight for commercial aircraft. Another way to recognize faults is through the use of built-in tests that produce error log messages. These tests produce data that is less information rich, but provide insight over the course of the entire flight. Each data source provides a different perspective of the state of the system. Therefore, it may be advantageous to combine information from parametric and nonparametric sources to improve fault diagnosis in terms of accuracy and timeliness of diagnosis. In this paper, we investigate integrating parametric sensor data and nonparametric information in fault diagnosis, specifically the way to parameterize nonparametric information for use in diagnostic models that accept only parametric data (e.g., most machine learning techniques). Results from high bypass commercial engines are presented.
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