scholarly journals Research on intelligent fault diagnosis of mechanical equipment based on sparse deep neural networks

2017 ◽  
Vol 19 (4) ◽  
pp. 2439-2455 ◽  
Author(s):  
Fei-Wei Qin ◽  
Jing Bai ◽  
Wen-Qiang Yuan
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis

Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines

2019 ◽  
Vol 329 ◽  
pp. 53-65 ◽  
Author(s):  
Jinrui Wang ◽  
Shunming Li ◽  
Zenghui An ◽  
Xingxing Jiang ◽  
Weiwei Qian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Intelligent Fault Diagnosis

Design and Analysis of Fault Diagnosis System of Electrohydraulic Servo Valve Based on ANN

2013 ◽  
Vol 785-786 ◽  
pp. 1380-1383
Author(s):  
Yao Li ◽  
Jian Gang Yi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Servo System ◽  
Intelligent Fault Diagnosis ◽  
Diagnosis System ◽  
Servo Valve ◽  
Pressure Characteristic ◽  
Fault Diagnosis System ◽  
Electrohydraulic Servo System

It is a difficulty to combine artificial neural networks (ANN) with the fault diagnosis of electrohydraulic servo valve. To slolve this problem, the fault diagnosis mechanism of electrohydraulic servo system is analysed, the effecitveness of fault diagnosis based on ANN is verified, and the pressure characteristic data are used to construct ANN samples. Finally, the algorithms of RBF, BP and Elman are compared with the built system and sampled. The results show the RBF algorithm is more rapid and accurate and the proposed intelligent fault diagnosis system of electrohydraulic servo valve is valuable.

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