Failure and reliability prediction by support vector machines regression of time series data

Moura, Márcio das Chagas, Enrico Zio, Isis Didier Lins and Enrique Droguett

Reliability Engineering and System Safety, 2011, vol. 96, issue 11, 1527-1534

Abstract: Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.

Keywords: Time series regression; Learning methods; Support vector machines; Time-to-failure forecasting and reliability prediction (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:96:y:2011:i:11:p:1527-1534

DOI: 10.1016/j.ress.2011.06.006

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