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Article Dans Une Revue Reliability Engineering and System Safety Année : 2011

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

Résumé

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.

Dates et versions

hal-04366336 , version 1 (28-12-2023)

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Citer

Márcio das Chagas Moura, Isis Didier Lins, Enrique Droguett, Enrico Zio. Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering and System Safety, 2011, 96 (11), pp.1527-1534. ⟨10.1016/j.ress.2011.06.006⟩. ⟨hal-04366336⟩
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