Weak fault detection with a two-stage key frequency focusing model

Dawei GAO, Yongsheng ZHU, Wei KANG, Hong FU, Ke YAN, Zhijun REN

Research output: Contribution to journalArticlespeer-review

1 Citation (Scopus)

Abstract

The fault diagnosis of early failure bearing can discover the potential danger in the mechanical equipment in time. It remains a great challenge due to the noise interference. Although many diagnosis methods have been proposed, the characteristics of signal have not yet been fully investigated, which leads to unsatisfactory diagnosis results. To solve this problem, a weak fault detection method with a two-stage key frequency focusing model is designed. Translation invariance in time domain and translation variance in frequency domain are systematically considered. The effectiveness is verified on four constructed datasets. The results show that the designed network has the best comprehensive performance comparing to the state-of-the-art methods. Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.
Original languageEnglish
JournalISA Transactions
Early online dateJun 2021
DOIs
Publication statusE-pub ahead of print - Jun 2021

Citation

Gao, D., Zhu, Y., Kang, W., Fu, H., Yan, K., & Ren, Z. (2021). Weak fault detection with a two-stage key frequency focusing model. ISA transactions. Advance online publication. doi: 10.1016/j.isatra.2021.06.014

Keywords

  • Weak-fault related features
  • Frequency focusing
  • Translation variance
  • Translation invariance
  • Fault diagnosis

Fingerprint

Dive into the research topics of 'Weak fault detection with a two-stage key frequency focusing model'. Together they form a unique fingerprint.