Machinery condition prediction based on wavelet and support vector machine

Shujie Liu, Yawei Hu, Chao Li, Huitian Lu, Hongchao Zhang

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

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. In this paper, the support vector machine (SVM), a novel learning machine based on the VC dimension theory of statistical learning theory, is described and applied in machinery condition prediction. To improve the modeling capability, wavelet transform (WT) is introduced into the SVM model to reduce the influence of irregular characteristics and simultaneously simplify the complexity of the original signal. The paper models the vibration signal from the double row bearing and wavelet transformation and SVM model (WT–SVM model) is constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships is applied to describe the degradation trend distribution and a 95 % confidence level based on t-distribution is given. The single SVM model and neural network (NN) approach is also investigated as a comparison. The modeling results indicate that the WT–SVM model outperforms the NN and single SVM models, and is feasible and effective in machinery condition prediction.

Original languageEnglish
Pages (from-to)1045-1055
Number of pages11
JournalJournal of Intelligent Manufacturing
Volume28
Issue number4
DOIs
StatePublished - Apr 1 2017

Keywords

  • Probabilistic forecasting
  • Support vector machine
  • Vibration intensity
  • Wavelet transform

Fingerprint Dive into the research topics of 'Machinery condition prediction based on wavelet and support vector machine'. Together they form a unique fingerprint.

Cite this