Hierarchical diagnosis network based on sparse deep neural networks and its application in bearing fault diagnosis

2017 ◽  
Author(s):  
Yumei Qi ◽  
Wei You ◽  
Changqing Shen ◽  
Xingxing Jiang ◽  
Weiguo Huang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Deep neural networks-based rolling bearing fault diagnosis

2017 ◽  
Vol 75 ◽  
pp. 327-333 ◽  
Author(s):  
Zhiqiang Chen ◽  
Shengcai Deng ◽  
Xudong Chen ◽  
Chuan Li ◽  
René-Vinicio Sanchez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Deep Neural Networks ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

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.

Sign in / Sign up

Export Citation Format

Share Document