Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm
Accurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including...
Saved in:
Main Authors: | Qicai Zhou, Hehong Shen, Jiong Zhao, Xingchen Liu, Xiaolei Xiong |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2019-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2019/8471732 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Rolling Bearing Degradation State Identification Based on LPP Optimized by GA
by: He Yu, et al.
Published: (2016-01-01) -
ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN
by: CHEN DaiJun, et al.
Published: (2023-12-01) -
FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN
by: LI SiQi, et al.
Published: (2020-01-01) -
ROLLING BEARING FAULT DIAGNOSIS BASED ON FUSION CNN AND PSO-SVM
by: WANG YongDing, et al.
Published: (2021-01-01) -
A VMD and CNN Combined Fault Diagnosis Method for Rolling Bearings
by: Li Kui, et al.
Published: (2022-11-01)