Failure and reliability prediction by support vector machines regression of time series data |
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Authors: | Má rcio das Chagas Moura,Enrico Zio,Isis Didier Lins |
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Affiliation: | a Department of Production Engineering, Center for Risk Analysis and Environmental Modeling, Federal University of Pernambuco, Rua Acadêmico Hélio Ramos, S/N, Cidade Universitária, 50740-530 Recife, PE, Brazil b Department of Energy, Polytechnic of Milan, Via Ponzio 34/3, 20133 Milan, Italy c Ecole Centrale Paris et Supelec, France |
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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. |
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Keywords: | Time series regression Learning methods Support vector machines Time-to-failure forecasting and reliability prediction |
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