Feature extraction for bearing fault diagnosis using composite multiscale entropy

Shuen De Wu, Chiu Wen Wu, Shiou Gwo Lin, Chun Chieh Wang, Kung Yen Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Multiscale entropy (MSE) is a popular algorithm to measure the complexity of a time series for multiple scales. However, the conventional MSE algorithm yields imprecise estimation of entropy for a time series with large time scale factors. In this paper, a composite multiscale entropy (CMSE) method is proposed to overcome this drawback. In the CMSE algorithm, with scale factors of τ, we calculate the sample entropies (SampEns) of all coarse-grained series and then define the mean of τ SampEns as the entropy values. This proposed algorithm is then applied to two different kinds of simulated noise signals and a set of real vibration data. These results demonstrate that the proposed CMSE provides more precise entropy calculation than the convectional MSE. Furthermore, as a feature extractor for a bearing faulty signal, CMSE provides a higher distinguishability, compared with MSE.

Original languageEnglish
Title of host publication2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
Subtitle of host publicationMechatronics for Human Wellbeing, AIM 2013
Pages1615-1618
Number of pages4
DOIs
Publication statusPublished - 2013 Sep 16
Event2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013 - Wollongong, NSW, Australia
Duration: 2013 Jul 92013 Jul 12

Publication series

Name2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013

Other

Other2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013
CountryAustralia
CityWollongong, NSW
Period13/7/913/7/12

    Fingerprint

ASJC Scopus subject areas

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Mechanical Engineering

Cite this

Wu, S. D., Wu, C. W., Lin, S. G., Wang, C. C., & Lee, K. Y. (2013). Feature extraction for bearing fault diagnosis using composite multiscale entropy. In 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013 (pp. 1615-1618). [6584327] (2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013). https://doi.org/10.1109/AIM.2013.6584327