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

Author

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

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.

Suggested Citation

  • Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:11:p:1527-1534
    DOI: 10.1016/j.ress.2011.06.006
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    References listed on IDEAS

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    1. Moura, Márcio das Chagas & Droguett, Enrique López, 2009. "Mathematical formulation and numerical treatment based on transition frequency densities and quadrature methods for non-homogeneous semi-Markov processes," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 342-349.
    2. Hu, Q.P. & Xie, M. & Ng, S.H. & Levitin, G., 2007. "Robust recurrent neural network modeling for software fault detection and correction prediction," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 332-340.
    3. Chen, Kuan-Yu, 2007. "Forecasting systems reliability based on support vector regression with genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 423-432.
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