Fault condition prognostic for rotating machinery based on new WEEMD and adaptive boosting regression algorithm

Pei Yao, Zhongsheng Wang, Hongkai Jiang, Zhenbao Liu

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

Abstract

This paper addresses a fault condition prognostic for sudden failure of rotating machinery. The proposed method is based on the utilization of feature extraction by using signal processing technique, and adaptive boosting (adaboost) regression algorithm. In this paper, we decompose vibration signal using wavelet packet decomposition and ensemble empirical mode decomposition (WEEMD), and successively we utilize the high order spectrum slice to describe the process of a fault evolution, and finally adaptive boosting regression algorithm is adopted for predicting the fault conditions. Experimental results of rotating machinery show that adaboost regression is pronounced comparing with other regression methods for fault condition prognostics.

Original languageEnglish
Title of host publicationProceedings of the 3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013
Pages58-62
Number of pages5
DOIs
StatePublished - 2013
Event3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013 - Phuket, Thailand
Duration: 10 Apr 201312 Apr 2013

Publication series

NameProceedings of the 3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013

Conference

Conference3rd IASTED Asian Conference on Modelling, Identification, and Control, AsiaMIC 2013
Country/TerritoryThailand
CityPhuket
Period10/04/1312/04/13

Keywords

  • Adaptive boosting regression algorithm
  • Fault condition prognostic
  • High order spectrum slice
  • WEEMD

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