Comparison of Data Mining and Neural Network Methods on Aero-engine Vibration Fault Diagnosis

2007 ◽  
Author(s):  
Dongxiang Jiang ◽  
Kai Xiong ◽  
Yongshan Ding ◽  
Kai Li
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Fault Diagnosis ◽  
Engine Vibration ◽  
Network Methods ◽  
Aero Engine

Comparison of RBF Neural Network and Support Vector Machine on Aero-Engine Vibration Fault Diagnosis

2007 ◽  
Vol 347 ◽  
pp. 323-328
Author(s):  
Kai Xiong ◽  
Dong Xiang Jiang ◽  
Yong Shan Ding ◽  
Kai Li
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Engine Vibration ◽  
Aero Engine ◽  
Speed Accuracy

RBF neural network and support vector machine (SVM), two Artificial Intelligent (AI) methods, have been extensively applied on machinery fault diagnosis. Aero-engine, as one kind of rotating machine with complex structure and high rotating speed, has complicated vibration faults. As one kind of AI methods, RBF neural network has the advantages of fast learning, high accuracy and strong self-adapting ability. Support vector machine, another AI method, only needs a small quantity of fault data samples to train the classifier and does not need to extract signal features. In this paper, the applications of two AI methods on aero-engine vibration fault diagnosis are introduced. Firstly, the principles and algorithm of both two methods are presented. Secondly the fundamentals of two-shaft aero-engine vibration fault diagnosis are described and gotten the standard fault samples (training samples) and simulation samples (testing samples). Third, two AI methods are applied to the vibration fault diagnosis and obtained the diagnostic results. Finally, the advantages and disadvantages of the two methods are compared such as the computing speed, accuracy of diagnosis and complexity of algorithm, and given a suggestion of selecting the diagnostic methods.

Automobile Engine Fault Diagnosis and Prediction System

2014 ◽  
Vol 1008-1009 ◽  
pp. 641-644
Author(s):  
Mei Lan Zhou ◽  
Ji Chang Wang ◽  
Yan Ping Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Fault Prediction ◽  
Prediction System ◽  
Framework Structure ◽  
Hardware Platform ◽  
Automobile Engine ◽  
Engine Vibration ◽  
Abnormal Sound

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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