A Novel Weak Feature Extraction Method for Rotating Machinery: Link Dispersion Entropy

Li Ding, Jinchen Ji, Yongbo Li, Shun Wang, Khandaker Noman, Ke Feng

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

The entropy-based feature extraction is a promising tool for extracting weak features from rotating machinery. However, the existing research has paid little attention to the state transition process, which brings the problem of accuracy and comprehensiveness in complexity estimation. To address this issue, this article proposes link dispersion entropy (LDE) based on the theory of the Markov chain for weak feature extraction. By calculating the transition probability of symbol patterns, the LDE can extract the fault information contained in the transition, enabling it to capture the early weak fault. Furthermore, LDE is extended to a multiscale analysis by combining it with the coarse-gaining process for comprehensive feature extraction, termed multiscale LDE (MLDE). Finally, three simulated signals and two different experimental data are utilized to verify the advantage of MLDE in extracting the weak fault features. Results demonstrate that MLDE has the best performance in fault diagnosis of rotating machinery compared with the existing five methods, namely sample entropy (SE), fuzzy entropy (FE), permutation entropy (PE), dispersion entropy (DE), and symbolic dynamic entropy (SDE).

Original languageEnglish
Article number3532012
JournalIEEE Transactions on Instrumentation and Measurement
Volume72
DOIs
StatePublished - 2023

Keywords

  • Complexity evaluation
  • fault diagnosis
  • feature extraction
  • link dispersion entropy (LDE)
  • rotating machinery

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