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

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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.
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    1. Abu-Samah, A. & Shahzad, M.K. & Zamai, E., 2017. "Bayesian based methodology for the extraction and validation of time bound failure signatures for online failure prediction," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 616-628.
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    5. Lins, Isis Didier & Droguett, Enrique López & Moura, Márcio das Chagas & Zio, Enrico & Jacinto, Carlos Magno, 2015. "Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 120-128.
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    18. Utkin, Lev V. & Coolen, Frank P.A., 2018. "A robust weighted SVR-based software reliability growth model," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 93-101.
    19. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    20. Oh, YeongGwang & Ransikarbum, Kasin & Busogi, Moise & Kwon, Daeil & Kim, Namhun, 2019. "Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 202-212.
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    24. Khatibinia, Mohsen & Javad Fadaee, Mohammad & Salajegheh, Javad & Salajegheh, Eysa, 2013. "Seismic reliability assessment of RC structures including soil–structure interaction using wavelet weighted least squares support vector machine," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 22-33.

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