scholarly journals Bearing Fault Diagnosis Based on BP Neural Network and Transfer Learning

2021 ◽  
Vol 1881 (2) ◽  
pp. 022084
Author(s):  
Ningbo Zhang ◽  
Yawei Li ◽  
Xingbo Yang ◽  
Junhao Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Bp Neural Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Rolling Bearing Fault Diagnosis Method Using Glowworm Swarm Optimization and Artificial Neural Network

2013 ◽  
Vol 860-863 ◽  
pp. 1812-1815 ◽  
Author(s):  
Qiang Xu ◽  
Yong Qian Liu ◽  
De Tian ◽  
Quan Long
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Rolling Bearing ◽  
Maintenance Cost ◽  
Swarm Optimization ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Glowworm Swarm Optimization ◽  
Potential Applications

Fault diagnosis has long been recognised as one of the most effective methods of reducing operation and maintenance cost in rotating industry, especially in bearings. A method based on BP neural network modified by glowworm swarm optimization (GSO) was proposed for fault diagnosis of rolling bearings. Six fault features were selected as the input of network. GSO algorithm was applied to simultaneously optimize the initial weight and threshold values of BP neural network. The reliability of the proposed technique was confirmed by experimental data, which indicated the potential applications of this method in the field of rolling bearing fault diagnosis.

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