Fault Diagnosis for Reducer via Improved LMD and SVM-RFE-MRMR
The vibration signals are usually characterized by nonstationary, nonlinearity, and high frequency shocks, and the redundant features degrade the performance of fault diagnosis methods. To deal with the problem, a novel fault diagnosis approach for rotating machinery is presented by combining improv...
Saved in:
Main Authors: | Xiaoguang Zhang, Zhenyue Song, Dandan Li, Wei Zhang, Zhike Zhao, Yingying Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2018-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2018/4526970 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Retracted: Research on Complex Classification Algorithm of Breast Cancer Chip Based on SVM-RFE Gene Feature Screening
by: null Complexity
Published: (2023-01-01) -
Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVM
by: Tian-bing Ma, et al.
Published: (2019-01-01) -
Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV
by: Zhou Wan, et al.
Published: (2015-01-01) -
Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing
by: Songrong Luo, et al.
Published: (2015-01-01) -
Rolling Bearing Fault Diagnosis Based on SVM Optimized with Adaptive Quantum DE Algorithm
by: Yuanyuan Li, et al.
Published: (2022-01-01)