scholarly journals Application of Modified ART1 Neural Network in Aero-engine Fault Diagnosis

2015 ◽  
Author(s):  
Hong-li Wang ◽  
Bing Xu ◽  
Yuan Zheng ◽  
Xue-dong Xue ◽  
Kan Cheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
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.

Research on Fault Diagnosis for Rotating Machinery Vibration of Aero-Engine Based on Wavelet Transformation and Probabilistic Neural Network

2011 ◽  
Vol 295-297 ◽  
pp. 2272-2278 ◽  
Author(s):  
Wen Jie Wu ◽  
Da Gui Huang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Decomposition ◽  
Wavelet Transformation ◽  
Probabilistic Neural Network ◽  
Mechanical Vibration ◽  
Rotating Machinery ◽  
Aero Engine ◽  
Network Fault ◽  
Fault Feature Extraction

Fault feature extraction using wavelet decomposition and probabilistic neural network fault diagnosis technology is presented in this paper. Fault diagnosis based on wavelet transformation and neural network data fusion is studied. The fault diagnosis in rotating machinery vibration of the aero-engine is simulated in Matlab. Our recent investigations demonstrate that using wavelet decomposition extract fault characteristics of the energy vector has strong generalization ability and anti-noise ability. Integration of the wavelet and neural network application can provide a better classification of diagnosis results, reliability and accuracy. This technique is suitable for the mechanical vibration fault diagnosis applications of steam turbine and gas turbine.

scholarly journals Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

2022 ◽  
Vol 42 (1) ◽  
pp. 351-360
Author(s):  
Kexin Zhang ◽  
Bin Lin ◽  
Jixin Chen ◽  
Xinlong Wu ◽  
Chao Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Aero Engine
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