Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine
ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extre...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2024-01-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails?columnId=78737352&Fpath=home&index=0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extreme learning machine(D-MKELM) theory.MethodsFirstly, sparse signals were obtained through threshold processing of transformed domain signals. A Gaussian random matrix was employed as the measurement matrix to compress the processed data. Subsequently, the compressed data was used as the input signal for the D-MKELM. Particle swarm optimization(PSO) algorithm was applied to optimize critical parameters, enabling intelligent fault diagnosis.ResultsResults demonstrate that the proposed method, using only a small amount of bearing diagnostic data, automatically extracts feature information of bearings from a limited number of measurement signals through the D-MKELM. The proposed method enables rapid fault diagnosis of bearings. With a diagnostic time of 0.55 s, a final recognition accuracy of 99.29% was achieved. The proposed method reduces diagnostic time and exhibits high diagnostic accuracy, providing a new approach for handling massive bearing data in fault diagnosis. |
|---|---|
| ISSN: | 1001-9669 |