A Hybrid Domain Degradation Feature Extraction Method for Motor Bearing Based on Distance Evaluation Technique
The vibration signal of the motor bearing has strong nonstationary and nonlinear characteristics, and it is arduous to accurately recognize the degradation state of the motor bearing with traditional single time or frequency domain indexes. A hybrid domain feature extraction method based on distance...
Saved in:
Main Authors: | Baiyan Chen, Hongru Li, He Yu, Yukui Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2017-01-01
|
Series: | International Journal of Rotating Machinery |
Online Access: | http://dx.doi.org/10.1155/2017/2607254 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Degradation State Identification of Cracked Ultrasonic Motor by Means of Fault Feature Extraction Method
by: Guoqing An, et al.
Published: (2019-01-01) -
Research on the Feature Selection of Rolling Bearings’ Degradation Features
by: Yaolong Li, et al.
Published: (2019-01-01) -
Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning
by: Jing An, et al.
Published: (2020-01-01) -
The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification
by: Jiyong Li, et al.
Published: (2015-01-01) -
An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics
by: Lixiang Duan, et al.
Published: (2018-01-01)