Application of Pearson Diversity Entropy as Prognostic Measure of Rotating Machinery

Wang Xinyue, Khandaker Noman, Hui Li, Yinchao Chen, Chenggang Tao, Yongbo Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

As a novel nonlinear measure, diversity entropy (DE) is a promising tool for prognosis of rotating machinery health. Being a core procedure in DE algorithm, cosine similarity (CS) focuses on the difference between the direction of two vectors instead of their distance or length. However, the direction difference can be easily affected by unwanted signal noise components, which will lead to low accuracy in DE-based signal complexity estimation. As a result, under heavy noise, DE not only fails to detect the incipient fault at the earliest stage of inception but also fails to trace the development of the fault. Aiming to solve the aforementioned problems, this paper incorporates the concept of Pearson similarity (PS) into DE calculation, instead of CS. PS measures the decentralized linear correlation between two vectors. Due to the involvement of PS, the newly proposed measure is termed as Pearson diversity entropy (PDE). Bearing run-to-failure data has been utilized to verify the performance of the proposed PDE. Result shows that the proposed PDE not only overcomes the limitations of original DE but also demonstrates better performance than conventional entropy algorithms such as permutation entropy (PE) and improved version of DE, namely multiscale diversity entropy (MDE).

Original languageEnglish
Title of host publicationAdvances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
EditorsAndrew Tan, Fan Zhu, Haochuan Jiang, Kazi Mostafa, Eng Hwa Yap, Leo Chen, Lillian J. A. Olule, Hyun Myung
PublisherSpringer Science and Business Media Deutschland GmbH
Pages519-527
Number of pages9
ISBN (Print)9789819984978
DOIs
StatePublished - 2024
EventInternational Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 - Suzhou, China
Duration: 22 Aug 202323 Aug 2023

Publication series

NameLecture Notes in Networks and Systems
Volume845
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
Country/TerritoryChina
CitySuzhou
Period22/08/2323/08/23

Keywords

  • Cosine similarity
  • Diversity entropy
  • Early fault detection
  • Pearson similarity
  • Rotating machinery

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