Machinery condition prediction based on wavelet and support vector machine

Shujie Liu (), Yawei Hu (), Chao Li (), Huitian Lu and Hongchao Zhang
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Shujie Liu: Dalian University of Technology
Yawei Hu: Dalian University of Technology
Chao Li: Dalian University of Technology
Huitian Lu: South Dakota State University
Hongchao Zhang: Texas Tech University

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 4, No 14, 1045-1055

Abstract: 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 $$t$$ 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.

Keywords: Support vector machine; Wavelet transform; Vibration intensity; Probabilistic forecasting (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10845-015-1045-5

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