Effect of Input Data on the Neural Networks Performance Applied in Bearing Fault Diagnosis

2018 ◽  
pp. 34-43
Author(s):  
Hocine Fenineche ◽  
Ahmed Felkaoui ◽  
Ali Rezig
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Input Data ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
The Neural Networks

scholarly journals Early bearing fault diagnosis based on improved SFLA and ELM network

2018 ◽  
Vol 42 (2) ◽  
pp. 187-193 ◽  
Author(s):  
Jingwen Xu ◽  
Yunxuan Zhang ◽  
Ziqi Yin
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Single Layer ◽  
Shuffled Frog Leaping Algorithm ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Chaotic Mapping ◽  
New Type ◽  
Shuffled Frog Leaping ◽  
Learning Machine

In this paper, an extreme learning machine (ELM) network based on an improved shuffled frog leaping algorithm (CCSFLA) is applied in early bearing fault diagnosis. ELM is a new type of single layer forward network. Although the generalization is stronger compared with traditional neural networks, a random setup of initial parameters increases instability of the network. An improved SFLA based on sinusoidal chaotic mapping with infinite collapses and constriction factors (CCSFLA) is proposed in this paper to optimize the ELM and obtain a CCSFLA–ELM model. Results show that the CCSFLA–ELM model can be used for optimization and that it improved the recognition of early bearing fault diagnosis.

scholarly journals A new bearing fault diagnosis method based on modified convolutional neural networks

2020 ◽  
Vol 33 (2) ◽  
pp. 439-447 ◽  
Author(s):  
Jiangquan ZHANG ◽  
Yi SUN ◽  
Liang GUO ◽  
Hongli GAO ◽  
Xin HONG ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method
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