Machinery condition prediction based on support vector machine model with wavelet transform

Shu Jie Liu, Hui Tian Lu, Chao Li, Ya Wei Hu, Hong Chao Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

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 vibration signal was modeled from the double row bearing, and wavelet transform and support vector machine model (WT-SVM model) was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95% confidence level based on t-distribution was given. The single SVM model and neural network (NN) approach were also investigated as a comparison. 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)831-834
Number of pages4
JournalJournal of Donghua University (English Edition)
Volume31
Issue number6
StatePublished - Dec 31 2014

Keywords

  • Probabilistic forecasting
  • Support vector machine (SVM)
  • Vibration intensity
  • Wavelet transform (WT)

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