Machinery condition prediction based on wavelet and support vector machine

Chao Li, Shujie Liu, Hongchao Zhang, Yawei Hu

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

3 Scopus citations

Abstract

This paper studies the use of wavelet and support vector machine (SVM) in machinery condition prediction. SVM is based on the VC dimension theory of statistical learning and the principle of structural risk minimization, and has shown advantages in solving the problem with limited sample, nonlinear and high dimensional pattern recognition. The soft failure of mechanical equipment makes its performance drop gradually, which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The paper models the vibration signal from the rear pad of a gas blower and analyzes the 1-step and multi-step forecasting of wavelet transformation and SVM (WT-SVM model) and SVM model.

Original languageEnglish
Title of host publicationQR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering
Pages1725-1729
Number of pages5
DOIs
StatePublished - 2013
Event2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2013 - Sichuan, China
Duration: Jul 15 2013Jul 18 2013

Publication series

NameQR2MSE 2013 - Proceedings of 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering

Conference

Conference2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2013
Country/TerritoryChina
CitySichuan
Period07/15/1307/18/13

Keywords

  • multi-step forecasting
  • support vector machine
  • vibration intensity
  • wavelet transform

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