Journal articles: '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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Tong, Min Yong. "Research of Automobile Engine Fault Diagnosis Based on Wavelet Packet." Advanced Materials Research 472-475 (February 2012): 795–98. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.795.

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A diagnosis method using wavelet packet, frequency band energy analysis and neural network was presented for the automobile engine fault diagnosis. Fault signal of automobile engine was decomposed at different frequency band by wavelet packet. According to the change of frequency band energy, fault frequency band of the automobile engine was found. Fault diagnosis knowledge is described by means of applying T-S model. Results from the experimental signal analysis show that the proposed method is effective in diagnosing the automobile engine faults.

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Mohammadpour, J., M. Franchek, and K. Grigoriadis. "A survey on diagnostic methods for automotive engines." International Journal of Engine Research 13, no. 1 (November 21, 2011): 41–64. http://dx.doi.org/10.1177/1468087411422851.

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Faults affecting automotive engines can potentially lead to increased emissions, increased fuel consumption, or engine damage. These negative impacts may be prevented or at least alleviated if faults can be detected and isolated in advance of a failure. United States Federal and State regulations dictate that automotive engines operate with high-precision onboard diagnosis (OBD) systems that enable the detection of faults, resulting in higher emissions that exceed standard thresholds. In this paper, we survey and discuss the different aspects of fault detection and diagnosis in automotive engine systems. The paper collects some of the efforts made in academia and industry on fault detection and isolation for a variety of component faults, actuator faults, and sensor faults using various data-driven and model-based methods.

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Wang, Meng-Hui, and Pi-Chu Wu. "Fault Diagnosis of Car Engine by Using a Novel GA-Based Extension Recognition Method." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/735485.

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Due to the passenger’s security, the recognized hidden faults in car engines are the most important work for a maintenance engineer, so they can regulate the engines to be safe and improve the reliability of automobile systems. In this paper, we will present a novel fault recognition method based on the genetic algorithm (GA) and the extension theory and also apply this method to the fault recognition of a practical car engine. The proposed recognition method has been tested on the Nissan Cefiro 2.0 engine and has also been compared to other traditional classification methods. Experimental results are of great effect regarding the hidden fault recognition of car engines, and the proposed method can also be applied to other industrial apparatus.

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Boulkroune, Boulaïd, Abdel Aitouche, Vincent Cocquempot, Li Cheng, and Zhijun Peng. "Actuator Fault Diagnosis with Application to a Diesel Engine Testbed." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/189860.

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This work addresses the issues of actuator fault detection and isolation for diesel engines. We are particularly interested in faults affecting the exhaust gas recirculation (EGR) and the variable geometry turbocharger (VGT) actuator valves. A bank of observer-based residuals is designed using a nonlinear mean value model of diesel engines. Each residual on the proposed scheme is based on a nonlinear unknown input observer and designed to be insensitive to only one fault. By using this scheme, each actuator fault can be easily isolated since only one residual goes to zero while the others do not. A decision algorithm based on multi-CUSUM is used. The performances of the proposed approach are shown through a real application to a Caterpillar 3126b engine.

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Li, Li, Ji Li, and Bao Jia Chen. "Wavelet Packet and Support Vector Machine for Engine Fault Diagnosis." Advanced Materials Research 230-232 (May 2011): 1–6. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.1.

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Aimed at the complexity of engine vibration, the paper proposed a combination method of wavelet packet and support vector machine for engine fault diagnosis based on the vibration signals. The vibration signals were collected from a gasoline engine, which type is Dongfeng EQ6100 (Chinese engine). The signals cover four working conditions, i.e. normal, piston knocking, piston pin fault, crankshaft bearing fault, under two engine conditions of on- and off-ignition, respectively. Firstly, wavelet packet was used to extract the features of the signals. Then, the off-ignition signals were selected to be the training data to construct a multi-class classifier based on support vector machine (SVM). Finally, applied the classifier to the engine diagnosis, and the faults were recognized effectively. The results demonstrate that the combined method is suitable to diagnose engine faults, especially for small signal samples.

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Zhang, Bangcheng, Jing Chen, Xiaojing Yin, and Zhi Gao. "Fault diagnosis based on grey relational analysis and synergetic pattern recognition for aero-engine gas-path systems." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 12 (February 11, 2019): 4598–605. http://dx.doi.org/10.1177/0954410019827657.

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The gas-path system is an important sub-system in aero-engines. There are various indistinguishable faults in aero-engine gas-path systems. These faults are easily misjudged because the characteristic parameters are similar. Due to the many kinds of faults, current studies have poor accuracy in distinguishing similar faults. To improve fault diagnosis accuracy for gas-path systems, a fault diagnosis method based on grey relational analysis and synergetic pattern recognition is proposed. In the proposed method, grey relational analysis is used to initially distinguish the faults into different types and obtain similar fault types. Synergetic pattern recognition contributes to accurately diagnose faults which are difficult to recognize. A case study is used to verify the effectiveness and accuracy of the proposed model. The results show that faults in common types of gas-path systems can be diagnosed accurately by the proposed method.

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Xu, Tao, Yan Jin, and Jin Xu. "Aero-Engine Vibration Fault Diagnosis Based on Harmonic Wavelet." Advanced Materials Research 490-495 (March 2012): 218–22. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.218.

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Because of the complicated structure of rollers, it is very difficult to diagnosis aero-engine vibration fault. Harmonic wavelet method is proposed target towards whole-body vibration of aero-engine in this paper. To implement vibration fault diagnosis of Aero-engine, vibration signals are presented with time-frequency and spectrum of coefficients after harmonic wavelet transform. The designed method overcome noise disturbance and energy leakage and possesses better performance for aero-engine vibration analysis compared with traditional wavelet analysis method. With the vibration data from whole-body vibration experiment, the effectiveness of the designed method is illustrated and it could identify three classical vibration faulty modes accurately, which provides a better method for whole-body vibration fault diagnosis of Aero-engine.

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Zhao, Nanyang, Zhiwei Mao, Donghai Wei, Haipeng Zhao, Jinjie Zhang, and Zhinong Jiang. "Fault Diagnosis of Diesel Engine Valve Clearance Based on Variational Mode Decomposition and Random Forest." Applied Sciences 10, no. 3 (February 7, 2020): 1124. http://dx.doi.org/10.3390/app10031124.

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Diesel engines, as power equipment, are widely used in the fields of the automobile industry, ship industry, and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in the time domain and angular domain, on which the current diagnosis methods are based, are easily affected by working conditions or are hard to extract accurate enough from, as the diesel engine keeps running in transient and non-stationary processes. This work aimed at diagnosing this fault mainly based on frequency band features, which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively, a decomposition technique based on improved variational mode decomposition was investigated in this work. As the connection between the features and the fault was fuzzy, the random forest algorithm was used to analyze the correspondence between features and faults. In addition, the feature dimension was reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition showed that the method based on variational mode decomposition and random forest was capable to detect the valve clearance fault effectively.

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Wang, Jin Ping. "Expert System for Fault Intelligence Diagnosis of Gasoline Engine." Applied Mechanics and Materials 214 (November 2012): 711–16. http://dx.doi.org/10.4028/www.scientific.net/amm.214.711.

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This paper describes the composing of Fault Diagnosis Expert System of auto engine. A model for fault diagnosis expert system, based on artificial neural network and expert system, is proposed. Firstly, we build a diagnosis tree, which is based on a fault tree to build an expert system for Diagnosis, then, get training samples from the fault tree and combine the self-study function of ANN to analyze and diagnose faults from different aspects and layers by several different ways to improve the efficiency of system diagnose, overcome the disadvantage of traditional Fault Diagnosis Expert System. This system takes the Single chip microcomputer as a developing tool. It's well operated and visible. Compared with the results obtained by BP- ANN, our method has more fast convergence rate and high computation efficiency.It is an efficient and reliable novel fault diagnosis technology.

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Jiang, Lu Lu, Yong Ni, Li Hong Tang, and Yong He. "Fault Diagnosis Expert System of Automobile Engine Based on Neural Networks." Key Engineering Materials 460-461 (January 2011): 605–10. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.605.

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his paper reports a practical approach for detecting and diagnose engine faults in real-time based on both the historical and the real-time engine operation data using a specially design neural networks-based fault diagnosis expert system. This system consisted of multiple sensors for real-time monitoring, an engine database for historic data comparison, and a neural network-bases classifier for detecting faults based on both the real-time and the historic data. This neural network-based engine fault diagnosis system was evaluated in a series of validation tests. The results indicated that the system was capable to detect the predefined faults reliably, and the diagnosis error was less than 5%.

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Xiao, Lingfei, Yanbin Du, Jixiang Hu, and Bin Jiang. "Sliding Mode Fault Tolerant Control with Adaptive Diagnosis for Aircraft Engines." International Journal of Turbo & Jet-Engines 35, no. 1 (March 26, 2018): 49–57. http://dx.doi.org/10.1515/tjj-2016-0023.

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AbstractIn this paper, a novel sliding mode fault tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors faults and actuators faults into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the fault diagnosis and fault tolerant controller design. Next, a suitable detection observer is designed to detect the faults effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode fault tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of faults converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed fault tolerant control method.

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Liu, Yu, Junhong Zhang, Kongjian Qin, and Yueyun Xu. "Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 5 (February 6, 2017): 881–94. http://dx.doi.org/10.1177/0954406217691554.

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Diesel engine is the most widely used power source of machines. However, faults occur frequently and often cause terrible accidents and serious economic losses. Therefore, diesel engine fault diagnosis is very important. Commonly, a single unitary pattern recognition method is used to diagnose the faults of diesel engine, but its performance decreases sharply when there are many fault types. Targeting this problem, a novel diesel engine fault diagnosis approach is proposed in this study. The approach is composed of four stages. Firstly, the nonstationary and nonlinear vibration signal of diesel engine is decomposed into a series of proper rotation components (PRCs) and a residual signal by the intrinsic time-scale decomposition (ITD) method. Secondly, six typical time-domain and four typical frequency-domain characteristics of the first several PRCs are extracted as fault features. Then, the modular and ensemble concepts are introduced to construct the multistage Adaboost relevance vector machine (RVM) model, in which the kernel fuzzy c-means clustering (KFCM) algorithm is used to decompose a complex classification task into several simple modules, and the Adaboost algorithm is used to improve the performance of each RVM based module. Finally, the fault diagnosis results can be obtained by inputting the fault features into the multistage Adaboost RVM model. The analysis results show that the fault diagnosis approach based on ITD and multistage Adaboost RVM performs effectively for the fault diagnosis of diesel engine, and it is better than the traditional pattern recognition methods.

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Sun, Pei Feng, and Yong Ni. "Study on CBR-Based Automobile Engine Intelligent Fault Diagnosis Technique." Key Engineering Materials 460-461 (January 2011): 637–41. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.637.

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It is difficult to do the fault diagnosis on the modern car engines which have high technology and complex structures. In this study, a case-based-reasoning (CBR) based automobile engine intelligent fault diagnosis system was proposed against this problem. The system’s structure and its mechanism of fault diagnosis were introduced. The key techniques to implement the system were analyzed, including the case establishment, the case search, the case learning and the maintenance of case library. The proposed system gave a new way to establish an efficient automobile engine fault diagnosis system.

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Vong, Chi-Man, Pak-Kin Wong, Weng-Fai Ip, and Chi-Chong Chiu. "Simultaneous-Fault Diagnosis of Automotive Engine Ignition Systems Using Prior Domain Knowledge and Relevance Vector Machine." Mathematical Problems in Engineering 2013 (2013): 1–19. http://dx.doi.org/10.1155/2013/974862.

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Engine ignition patterns can be analyzed to identify the engine fault according to both the specific prior domain knowledge and the shape features of the patterns. One of the challenges in ignition system diagnosis is that more than one fault may appear at a time. This kind of problem refers to simultaneous-fault diagnosis. Another challenge is the acquisition of a large amount of costly simultaneous-fault ignition patterns for constructing the diagnostic system because the number of the training patterns depends on the combination of different single faults. The above problems could be resolved by the proposed framework combining feature extraction, probabilistic classification, and decision threshold optimization. With the proposed framework, the features of the single faults in a simultaneous-fault pattern are extracted and then detected using a new probabilistic classifier, namely, pairwise coupling relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is not necessary. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnoses and is superior to the existing approach.

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Zeng, Xian Heng, and Li Hua Yin. "Fault Detection System of Automobile Engine Based on Correlation Dimension Feature Extraction." Applied Mechanics and Materials 380-384 (August 2013): 782–85. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.782.

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According to the high fault rate and the great difficulty of diagnosis for the automobile engine, an automobile engine faults detection system was designed. Because the vibration signal of the engine could reflect the faults types to a great extent, a fault detection method was proposed based on the extraction of the vibration signal correlation dimension. The collected vibration signal which was from different type of automobile engines was processed and analyzed. The correlation dimension was extracted and an improved correlation algorithm was proposed in the system, the computational accuracy was improved, and the standard deviation of the improved algorithm lowers about 50% in comparison with the traditional algorithm, the classification performance is raised variously, the excellent detection performance was showed in the system. The detection result shows that the correlation dimension feature extraction method that this paper proposed can detect and diagnose different types of automobile engine faults such as start subsystem fault, ignition subsystem fault, fuel supply subsystem, etc. The detection conclusion was stable and the simulation result has much great application performance.

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Merrington, G., Oh-Kyu Kwon, G. Goodwin, and B. Carlsson. "Fault Detection and Diagnosis in Gas Turbines." Journal of Engineering for Gas Turbines and Power 113, no. 2 (April 1, 1991): 276–82. http://dx.doi.org/10.1115/1.2906559.

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Modern military aircraft are fitted with Engine Monitoring Systems (EMS), which have the potential to provide maintenance personnel with valuable information for diagnosing engine faults and assessing engine condition. In this study, analytical redundancy methods have been applied to gas turbine engine transient data with the view to extracting the desired fault information. The basic idea is to use mathematical models to interrelate the measured variables and then monitor the effects of fault conditions on the new estimates of the model parameters. In most of the existing literature the models used are assumed to be perfect with the primary source of error arising from the measurement noise. In the technique to be described, a new method of quantifying the effects of changes in the operating conditions is presented when simplified models are employed. The technique accounts for undermodeling effects and errors arising from linearization of an inherently nonlinear system. Results obtained show a marked improvement over those obtained with traditional methods.

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Lu, Feng, Tie Bin Zhu, and Yi Qiu Lv. "Data-Driven Based Gas Path Fault Diagnosis for Turbo-Shaft Engine." Applied Mechanics and Materials 249-250 (December 2012): 400–404. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.400.

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In order to improve diagnostic accuracy and reduce the rate of misdiagnosis to the aircraft engine gas path faulty, the methods based on data-driven and information fusion are developed and analyzed. BP neural network (NN) and RBF neural network based on data-driven single gas path fault diagnosis method is introduced firstly. Design gas path performance estimators and the fault type classification for turbo-shaft engine. Then the gas path fused diagnostic structure based on D-S evidence theory and least squares support vector machine are developed. Comparisons of the turbo-shaft engine gas path fault diagnosis verify the feasibility and effectiveness of the gas path fault diagnosis based on information fusion.

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Yao, Zhu Ting, and Hong Xia Pan. "The Engine Fault Diagnosis Based on Time Domain and Frequency Domain." Advanced Materials Research 936 (June 2014): 2243–46. http://dx.doi.org/10.4028/www.scientific.net/amr.936.2243.

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Engine is as a power machine, the operating status is good or bad, directly affects the working status of equipment. The status monitoring and fault diagnosis is very necessary to ensure that the equipment runs in its best, and improves equipment maintenance quality and efficiency. The engine failure shows the complexity and diversity of the interaction and complex relationship between the various subsystems of the engine, that is the fault of complexity, ambiguity, correlation, relativity and multiple faults coexistence. The available information are much in the engine diagnosis, for example, the vibration signal from bearings, cylinder head or cylinder block surface; oil, cooling water, pressure of intake, exhaust and fuel; temperature signal; noise, speed or oil-sample signals. In this paper, an engine as an example, engine fault diagnosis experimental system is built, the normal state, left one and right six cylinders off the oil, air filter blockage (inlet wood blockage is 30%, the inlet has screen cloth.) in the load of 2565Nm, and the speeds of 1500r/min, 1800r/min, 2200r/min are studied. The experimental results analysis, feature extraction and fault diagnosis are finished based on the time domain and frequency domain. Keywords: engine, fault diagnosis, time domain, frequency domain.

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Liu, Huan Xue, Guang Dong Zhang, and Zhen Zhong Zhang. "Simulation for Automotive Engine Fault Diagnosis Method." Applied Mechanics and Materials 687-691 (November 2014): 882–85. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.882.

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For engine fault diagnosis problem, an engine fault diagnosis method based on particle swarm optimization algorithm is proposed. The velocity and spatial position of all the particles in the particle swarm are updated, in order to provide accurate data basis for the engine fault diagnosis. Particle swarm optimization method is utilized to process iteration for all particles, so as to determine whether failure exists in components of engine. Experimental results show that with the proposed algorithm to diagnose engine fault can effectively improve the accuracy of fault diagnosis, and achieved the desired results.

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Zhang, Xue Liang, and Yun Jie Xu. "Fault Diagnosis for Diesel Engine Cylinder Head Based on Genetic-SVM Classifier." Applied Mechanics and Materials 590 (June 2014): 390–93. http://dx.doi.org/10.4028/www.scientific.net/amm.590.390.

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Fault diagnosis of Diesel engine cylinder head is very complex, so it is difficult to use the mathematical model to describe their faults. In this study, support vector machine trained by genetic algorithm based on high frequency demodulation analysis is proposed to fault diagnosis of Diesel engine cylinder head. Genetic algorithm is used to determine training parameters of support vector machine in this model, which can optimize the support vector machine (SVM) an intelligent diagnostic model. The performance of the GSVM system proposed in this study is evaluated by Diesel engine cylinder head in the wood-wool production device. The application to fault diagnosis for diesel engine shows the effectiveness o f the method.

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Vernekar, Kiran, Hemantha Kumar, and Gangadharan K.V. "Engine gearbox fault diagnosis using machine learning approach." Journal of Quality in Maintenance Engineering 24, no. 3 (August 13, 2018): 345–57. http://dx.doi.org/10.1108/jqme-11-2015-0058.

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Purpose Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues. Design/methodology/approach This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm. Findings The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis. Originality/value This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques.

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Chen, You Peng. "Diagnostic Analysis of Ignition System Fault of Polaris Engine." Applied Mechanics and Materials 539 (July 2014): 89–92. http://dx.doi.org/10.4028/www.scientific.net/amm.539.89.

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This thesis gives an introduction of the structure, working principle, main performance and some relevant parameters of Polaris AGN engine ignition system. It illustrates the various faults, reasons, fault detection, diagnosis and removal methods of ignition system, and cites a fault diagnosis example for the reference of automobile maintenance.

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33

Feng, Donghua, and Yahong Li. "Research on Intelligent Diagnosis Method for Large-Scale Ship Engine Fault in Non-Deterministic Environment." Polish Maritime Research 24, s3 (November 27, 2017): 200–206. http://dx.doi.org/10.1515/pomr-2017-0123.

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Abstract Aiming at the problem of inaccurate and time-consuming of the fault diagnosis method for large-scale ship engine, an intelligent diagnosis method for large-scale ship engine fault in non-deterministic environment based on neural network is proposed. First, the possible fault of the engine was analyzed, and the downtime fault of large-scale ship engine and the main fault mode were identified. On this basis, the fault diagnosis model for large-scale ship engine based on neural network is established, and the intelligent diagnosis of engine fault is completed. The experiment proved that the proposed method has high diagnostic accuracy, engine fault diagnosis takes only about 3s, with a higher use value.

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Gawande, S. H., L. G. Navale, M. R. Nandgaonkar, D. S. Butala, and S. Kunamalla. "Fault Detection of Inline Reciprocating Diesel Engine: A Mass and Gas-Torque Approach." Advances in Acoustics and Vibration 2012 (September 20, 2012): 1–6. http://dx.doi.org/10.1155/2012/314706.

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Early fault detection and diagnosis for medium-speed diesel engines are important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion-related fault detection capability of crankshaft torsional vibrations. Proposed methodology state the way of early fault detection in the operating six-cylinder diesel engine. The model of six cylinders DI Diesel engine is developed appropriately. As per the earlier work by the same author the torsional vibration amplitudes are used to superimpose the mass and gas torque. Further mass and gas torque analysis is used to detect fault in the operating engine. The DFT of the measured crankshaft’s speed, under steady-state operating conditions at constant load shows significant variation of the amplitude of the lowest major harmonic order. This is valid both for uniform operating and faulty conditions and the lowest harmonic orders may be used to correlate its amplitude to the gas pressure torque and mass torque for a given engine. The amplitudes of the lowest harmonic orders (0.5, 1, and 1.5) of the gas pressure torque and mass torque are used to map the fault. A method capable to detect faulty cylinder of operating Kirloskar diesel engine of SL90 Engine-SL8800TA type is developed, based on the phases of the lowest three harmonic orders.

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Chai, Yan You, Xiu Yan Peng, and Xin Jiang Man. "Research on Fault Diagnosis of Marine Diesel Engine Based on KFDA." Advanced Materials Research 442 (January 2012): 262–66. http://dx.doi.org/10.4028/www.scientific.net/amr.442.262.

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In order to guarantee the normal operation of marine, an effective fault diagnosis model need to be established to determine the reason causing the fault of marine diesel engine. According to the problem of fault diagnosis of marine diesel engine, by using the methods of kernel fisher discriminant analysis, a method solving fault diagnosis of marine diesel engine is proposed. Firstly, kernel fisher discriminant analysis was done to the historical fault set and the parameters were determined by grid method. In this way, the fault diagnosis model of marine diesel engine was built. Then, this model was used to diagnosis the actual fault of marine diesel engine. The effect of fault diagnosis in fuel injection system of MAN B&W 10L90MC marine diesel engine verified the effectiveness of this method. Therefore, the method proposed by this paper has certain practical significance towards the fault diagnosis of marine diesel engine.

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Diao, Yixin, and Kevin M. Passino. "Fault diagnosis for a turbine engine." Control Engineering Practice 12, no. 9 (September 2004): 1151–65. http://dx.doi.org/10.1016/j.conengprac.2003.11.012.

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Zhao, Haipeng, Jinjie Zhang, Zhinong Jiang, Donghai Wei, Xudong Zhang, and Zhiwei Mao. "A New Fault Diagnosis Method for a Diesel Engine Based on an Optimized Vibration Mel Frequency under Multiple Operation Conditions." Sensors 19, no. 11 (June 6, 2019): 2590. http://dx.doi.org/10.3390/s19112590.

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The diesel engine has been a significant component of large-scale mechanical systems for the intelligent manufacturing industry. Because of its complex structure and poor working environment, it has trouble effectively acquiring the representative fault features. Further, fault diagnosis of the diesel engine faces great challenges. This paper presents a new fault diagnosis method for the detection of diesel engine faults under multiple operation conditions instead of conventional methods confined to a single condition. First, an adaptive correlation threshold process is designed as a preprocessing unit to enhance data quality by weakening non-impact region characteristics. Next, a feature extraction method for sound signals based on the Mel frequency cepstrum (MFC) is improved and introduced into the machinery fault diagnosis. Then, the combination of the improved feature and vibrational mode decomposition (VMD) is proposed to incorporate VMD into an effective adaptive decomposition of non-stationary signals to combine it with an excellent feature representation of the vibration signal. Finally, the vector quantization algorithm is adopted to reduce the feature dimensions and generate codebook model bases, which trains the K-Nearest Neighbor classifiers. Five comparative methods were carried out, and the experimental results show that the proposed method offers a good effect of the common valve clearance fault of diesel engines under different conditions.

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Zhou, Mei Lan, Ji Chang Wang, and Yan Ping Li. "Automobile Engine Fault Diagnosis and Prediction System." Advanced Materials Research 1008-1009 (August 2014): 641–44. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.641.

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Aimed at the fault diagnosis and prediction of automobile engine, firstly designed a framework structure of automobile engine fault diagnosis and prediction system, and built a hardware platform; Secondly adopted the genetic algorithm neural network to fault prediction and diagnosis reasoning; Finally after analyzing automobile exhaust components, engine vibration, engine abnormal sound parameters, inferred the appeared and impending fault of automobile then made the tips for users on the screen. The results show that the performance of system is well, the accuracy of diagnosis and prediction is 95% in different conditions of experiment and debugging.

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Cui, Jianwei, Mengxiao Shan, Ruqiang Yan, and Yahui Wu. "Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/283718.

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This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

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Romessis, C., and K. Mathioudakis. "Bayesian Network Approach for Gas Path Fault Diagnosis." Journal of Engineering for Gas Turbines and Power 128, no. 1 (March 1, 2004): 64–72. http://dx.doi.org/10.1115/1.1924536.

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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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41

Si, Chuan Sheng. "Design of Remote Fault Diagnosis System for Automobile Engine Based on Internet." Applied Mechanics and Materials 713-715 (January 2015): 456–59. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.456.

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This paper is based on the engine fault diagnosis technology and the network technology, it carries out and uses the Internet network data transmission engine to obtain technical parameters of the engine,fault diagnosis of engine remote.The system can discover the fault of automobile engine and to determine repair method, the fault and exclusion, effectively improve the flexibility and accuracy of fault diagnosis, remote resource sharing.

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42

Merrington, G. L. "Fault Diagnosis in Gas Turbines Using a Model-Based Technique." Journal of Engineering for Gas Turbines and Power 116, no. 2 (April 1, 1994): 374–80. http://dx.doi.org/10.1115/1.2906830.

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Reliable methods for diagnosing faults and detecting degraded performance in gas turbine engines are continually being sought. In this paper, a model-based technique is applied to the problem of detecting degraded performance in a military turbofan engine from take-off acceleration-type transients. In the past, difficulty has been experienced in isolating the effects of some of the physical processes involved. One such effect is the influence of the bulk metal temperature on the measured engine parameters during large power excursions. It will be shown that the model-based technique provides a simple and convenient way of separating this effect from the faster dynamic components. The important conclusion from this work is that good fault coverage can be gleaned from the resultant pseudo-steady-state gain estimates derived in this way.

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Lu, Jin Ming, Fan Lin Meng, Hua Shen, Li Bin Ding, and Jie Ma. "Fault Diagnosis for Misfire and Abnormal Clearance in a Diesel Engine Based on EEMD." Applied Mechanics and Materials 97-98 (September 2011): 702–5. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.702.

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The misfire of one or more diesel cylinder and the abnormal clearance in the intake valve train of cylinder are common faults which affect the safety and the performance of the engine seriously. A new fault diagnosis method based on EEMD and instantaneous energy density spectrum is proposed here. The IMFs generated by EEMD can alleviate the problem of mode mixing and approach the reality IMFs. The instantaneous energy density of these IMFs can distinguish the faulty impacts clearly. The effectiveness of this method was demonstrated by analysis the vibration signals of misfire fault and abnormal clearance in the intake valve train of 3110 diesel.

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Wang, Liwen, Lu Zhang, Meng Xu, and Xudong Shi. "Research on Fault Diagnosis Method of Civil Aviation Engine Variable Bleed Valve System Based on Artificial Immune Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 07 (May 25, 2016): 1659021. http://dx.doi.org/10.1142/s0218001416590217.

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Variable Bleed Valve (VBV) system is an important component of civil aviation engine which can be used to adjust the opening degree of bleed valve. By adjusting the opening degree of bleed valve, a part of outlet air from low-pressure compressor can flow into the fan so as to improve the working stability of low-pressure compressor. VBV system was chosen as the research object in this paper, which internal structure and composition were analyzed and its model was established from part to the whole at first. Then, the negative selection algorithm of variable radius detectors was researched to achieve VBV system faults diagnosis by selecting characteristic parameters and setting up multi-type fault diagnosis process. At last, electrohydraulic servo valve fault, VBV system controller fault and linear variable differential transformer fault were intentionally set up to verify the effectiveness of fault diagnosis method. Through the process of detector generation and fault recognition, the faults in VBV system can be diagnosed effectively.

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Lu, Feng, Jipeng Jiang, and Jinquan Huang. "Gas Turbine Engine Gas-path Fault Diagnosis Based on Improved SBELM Architecture." International Journal of Turbo & Jet-Engines 35, no. 4 (December 19, 2018): 351–63. http://dx.doi.org/10.1515/tjj-2016-0050.

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Abstract Various model-based methods are widely used to aircraft engine fault diagnosis, and an accurate engine model is used in these approaches. However, it is difficult to obtain general engine model with high accuracy due to engine individual difference, lifecycle performance deterioration and modeling uncertainty. Recently, data-driven diagnostic approaches for aircraft engine become more popular with the development of machine learning technologies. While these data-driven methods to engine fault diagnosis tend to ignore experimental data sparse and uncertainty, which results in hardly achieve fast fault diagnosis for multiple patterns. This paper presents a novel data-driven diagnostic approach using Sparse Bayesian Extreme Learning Machine (SBELM) for engine fault diagnosis. This methodology addresses fast fault diagnosis without relying on engine model. To enhance the reliability of fast fault diagnosis and enlarge the detectable fault number, a SBELM-based multi-output classifier framework is designed. The reduced sparse topology of ELM is presented and utilized to fault diagnosis extended from single classifier to multi-output classifier. The effects of noise and measurement uncertainty are taken into consideration. Simulation results show the SBELM-based multi-output classifier for engine fault diagnosis is superior to the existing data-driven ones with regards to accuracy and computational efforts.

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46

Twiddle, J. A., and N. B. Jones. "A high-level technique for diesel engine combustion system condition monitoring and fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 216, no. 2 (March 1, 2002): 125–34. http://dx.doi.org/10.1243/0959651021541499.

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This paper presents a technique for diagnosis of a class of engine faults, which adversely affect the combustion efficiency of a diesel generator set. The diagnosis is made by combining the evidence of two separate estimations of engine load with the outputs from a predictive fuzzy model of engine speed. Certain faults affect the periodicity of the engine speed signal. The variation in periodicity means that the load estimation from power spectral density of the speed signal is not robust. This problem is countered by implementing a reference model to predict speed fluctuations with respect to crank angle. This model has the additional benefit that its output may be used to detect periodic fault symptoms in the speed signal, thereby leading to identification of the individual cylinder affected by the fault. A fuzzy rule based system has been developed to diagnose faults based on the load estimations and the residuals obtained from the reference model. Testing the diagnostic system with data from the normal, and two other separate engine fault conditions, resulted in a classification success rate greater than 90 per cent in each case. A further benefit is reported where the combination of evidence from the three sources effectively validates the load estimation, which may then be used to infer faults in other subsystems.

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Zhang, Xiao Ming, and Dong Hai Chen. "Study on Fault Diagnosis for Turbocharging System of Diesel Engine Based on Support Vector Machine." Applied Mechanics and Materials 42 (November 2010): 371–74. http://dx.doi.org/10.4028/www.scientific.net/amm.42.371.

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The support vector machine technology is applied to study the fault diagnosis for supercharge system of diesel engine. Based on the different features under various faults of supercharge system of diesel engine and the signal variables of the fault of the system’s components, dimensionless daters can be gotten after normalization processing. The utilization of SVM with good generation ability wad adopted to establish the diagnosis model for supercharge system of diesel engine, which can make the system failure diagnosis through inputting the diagnosis data. The average accuracy is 99.8% in twenty tests. The diagnosing results are consistent with the factual results.

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Chen, Jian, Robert Randall, Ningsheng Feng, Bart Peeters, and Herman Van der Auweraer. "Modelling and diagnosis of big-end bearing knock fault in internal combustion engines." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 16 (February 24, 2014): 2973–84. http://dx.doi.org/10.1177/0954406214524743.

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Big-end bearing knock is considered to be one of the common mechanical faults in internal combustion engines (IC engines). In this paper, a model has been built to simulate the effects of oversized clearance in the big-end bearing of an engine. In order to find a relationship between the acceleration response signal and the oversized clearance, the kinematic/kinetic and lubrication characteristics of the big ending bearing were studied. By adjusting the clearance, the impact forces with different levels of bearing knock fault can be simulated. The acceleration on the surface of the engine block was calculated by multiplying the simulated force spectrum by an experimentally measured frequency response function (FRF) in the frequency domain (and then inverse transforming to the time domain). As for experimentally measured vibration signals from bearing knock faults, the signal processing approach used involved calculating the squared envelopes of the simulated acceleration signals. The comparison to the experimental results demonstrated that the simulation model can correctly simulate vibration signals with different stages of bearing knock faults.

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Wang, Yi, Ping Jiang, Yao Hui Luo, and Yan Qun Xie. "The Intelligent Fault Diagnosis of Construction Machinery Based on Multi-Agent." Applied Mechanics and Materials 48-49 (February 2011): 1265–70. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.1265.

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The number of construction machinery faults is too much and its diagnosis are fuzzy and complex. We construct the fault hierarchy model by the means of hierarchy analysis and obtain all kinds of Possibility degree of faults factors through the triangular fuzzy complementary judgment matrix .Take the fault diagnosis of diesel engine fuel system, this paper establish intelligent fault diagnosis system, which has the ability of self-learning and self-correction using multi-agent technology.

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Wang, Yi, Ping Jiang, Yao Hui Luo, and Yan Qun Xie. "The Intelligent Fault Diagnosis of Construction Machinery Based on Multi-Agent." Applied Mechanics and Materials 48-49 (February 2011): 1383–88. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.1383.

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The number of construction machinery faults is too much and its diagnosis are fuzzy and complex. We construct the fault hierarchy model by the means of hierarchy analysis and obtain all kinds of Possibility degree of faults factors through the triangular fuzzy complementary judgment matrix .Take the fault diagnosis of diesel engine fuel system, this paper establish intelligent fault diagnosis system, which has the ability of self-learning and self-correction using multi-agent technology.

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Journal articles on the topic 'Engine fault diagnosis'

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