A Permutation Entropy-based Importance Measure for Condition Monitoring Data Fusion in Fault Diagnosis

Jianhua Chen, Biao Ma, Shufa Yan, Changsong Zheng, Qianqian Zhang

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

3 Citations (Scopus)

Abstract

In condition monitoring and fault diagnosis, how to measure the importance degree of different condition monitoring (CM) data before data fusion is a vital issue. We propose an importance measure that can be modeled using a weighted average function. The weight is measured with the relative scale of the permutation entropy from each fault feature variable. Compared with some other importance measures in data fusion, the proposed measure focuses on the degradation trend represented by the permutation entropy, instead of the information volume represented by the Shannon entropy. Then, a multiple fault feature variable fusion method based on the proposed importance measure is further proposed in the D-S evidence theory framework. Finally, a case study involving an oil analysis-based dataset from a power-shift steering transmission is carried out to investigate the superiority of the proposed method.

Original languageEnglish
Title of host publication2019 Prognostics and System Health Management Conference, PHAI-Qingdao 2019
EditorsWei Guo, Steven Li, Qiang Miao
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728108612
DOIs
Publication statusPublished - Oct 2019
Event10th Prognostics and System Health Management Conference, PHM-Qingdao 2019 - Qingdao, China
Duration: 25 Oct 201927 Oct 2019

Publication series

Name2019 Prognostics and System Health Management Conference, PHM-Qingdao 2019

Conference

Conference10th Prognostics and System Health Management Conference, PHM-Qingdao 2019
Country/TerritoryChina
CityQingdao
Period25/10/1927/10/19

Keywords

  • Dempster-Shafter evidence theory
  • condition monitoring
  • fault diagnosis
  • importance measure
  • permutation entropy

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