Rolling Bearing Fault Diagnosis Based on SVM Optimized with Adaptive Quantum DE Algorithm
In order to optimize traditional fault diagnosis models for practical applications, a fault diagnosis model based on support vector machines optimized with the adaptive quantum differential evolution of (AQDE-SVM) is proposed in this study. First, the traditional differential evolution is rewritten...
Saved in:
Main Authors: | Yuanyuan Li, Qichun Sun, Hua Xu, Xiaogang Li, Zhijun Fang, Wei Yao |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2022/8126464 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest
by: Xiwen Qin, et al.
Published: (2021-01-01) -
Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals
by: Yong Chang, et al.
Published: (2021-06-01) -
Rolling Bearing Fault Diagnosis Method Based on MCMF and SAIMFE
by: Dejun Meng, et al.
Published: (2022-01-01) -
Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions
by: Xiao Yu, et al.
Published: (2021-01-01) -
A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM
by: HungLinh Ao, et al.
Published: (2014-01-01)