Hierarchical Symbol Transition Entropy: A Novel Feature Extractor for Machinery Health Monitoring

Cheng Yang, Moncef Gabbouj, Minping Jia, Zhinong Li

Research output: Contribution to journalArticleScientificpeer-review


This paper develops a novel collaborative health monitoring framework based on hierarchical symbol transition entropy (HSTE) and 2D-extreme learning machine (2D-ELM) without fusion. In the proposed framework, a novel metric called symbol transition entropy (STE) is first presented to evaluate the dynamical complexity of time series through multi-step transition and the joint probability distribution of the symbol state and its transition state. Compared with the existing entropy algorithms, STE has better robustness and captures more detailed dynamical changes. Subsequently, a new feature representation method called hierarchical symbol transition entropy is proposed by combining STE with the hierarchical analysis. The two-order tensor features can be constructed for multichannel data by stacking HSTE values extracted from each single-channel data. Finally, 2D-ELM is incorporated to identify the extracted two-order tensor features without vectorization. The feasibility of the proposed schemes is verified through simulation and experimental studies, and the final results confirm that the developed schemes have better performance than the existing entropy-based collaborative fault diagnosis methods.

Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Issue number9
Early online date2021
Publication statusPublished - 2022
Publication typeA1 Journal article-refereed


  • Collaboration
  • collaborative fault diagnosis
  • Entropy
  • Extraterrestrial measurements
  • Fault diagnosis
  • Feature extraction
  • Hierarchical symbol transition entropy
  • Mechatronics
  • Monitoring
  • rotating machinery
  • two-dimensional extreme learning machine

Publication forum classification

  • Publication forum level 3

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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