We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
A Rolling Bearing Fault Diagnosis Method Based on Enhanced Integrated Filter Network.
- Authors
Wu, Kang; Tao, Jie; Yang, Dalian; Xie, Hu; Li, Zhiying
- Abstract
Aiming at the difficulty of rolling bearing fault diagnosis in a strong noise environment, this paper proposes an enhanced integrated filter network. In the method, we firstly design an enhanced integrated filter, which includes the filter enhancement module and the expression enhancement module. The filter enhancement module can not only filter the high-frequency noise to extract useful features of medium and low-frequency signals but also maintain frequency and time resolution to some extent. On this basis, the expression enhancement module analyzes fault features intercepted by the upper network at multiple scales to get deep features. Then we introduce vector neurons to integrate scalar features into vector space, which mine the correlation between features. The feature vectors are transmitted by dynamic routing to establish the relationship between low-level capsules and high-level capsules. In order to verify the diagnostic performance of the model, CWRU and IMS bearing datasets are used for experimental verification. In the strong noise environment of SNR = −4 dB, the fault diagnosis precisions of the method on CWRU and IMS reach 94.85% and 92.45%, respectively. Compared with typical bearing fault diagnosis methods, the method has higher fault diagnosis precision and better generalization ability in a strong noise environment.
- Subjects
FAULT diagnosis; ROLLER bearings; DIAGNOSIS methods; VECTOR spaces
- Publication
Machines, 2022, Vol 10, Issue 6, p481
- ISSN
2075-1702
- Publication type
Article
- DOI
10.3390/machines10060481