Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression

Isis Didier Lins, Droguett, Enrique López, Moura, Márcio das Chagas, Enrico Zio and Carlos Magno Jacinto

Reliability Engineering and System Safety, 2015, vol. 137, issue C, 120-128

Abstract: Data-driven learning methods for predicting the evolution of the degradation processes affecting equipment are becoming increasingly attractive in reliability and prognostics applications. Among these, we consider here Support Vector Regression (SVR), which has provided promising results in various applications. Nevertheless, the predictions provided by SVR are point estimates whereas in order to take better informed decisions, an uncertainty assessment should be also carried out. For this, we apply bootstrap to SVR so as to obtain confidence and prediction intervals, without having to make any assumption about probability distributions and with good performance even when only a small data set is available. The bootstrapped SVR is first verified on Monte Carlo experiments and then is applied to a real case study concerning the prediction of degradation of a component from the offshore oil industry. The results obtained indicate that the bootstrapped SVR is a promising tool for providing reliable point and interval estimates, which can inform maintenance-related decisions on degrading components.

Keywords: Degradation; Prognostics; Uncertainty; Confidence and prediction intervals; Support vector regression; Bootstrap; Scale growth rate (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:137:y:2015:i:c:p:120-128

DOI: 10.1016/j.ress.2015.01.007

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