A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

Tao Tao, Enrico Zio and Wei Zhao

Reliability Engineering and System Safety, 2018, vol. 177, issue C, 35-49

Abstract: Modeling the evolution of system reliability in the presence of Condition Monitoring (CM) signals is an important issue for improved reliability assessment and system lifetime prediction. In practice, during its lifetime, a system usually works under varying operating conditions due to internal or external factors such as the ambient environments, operational profiles or workloads. In this context, the system reliability can show varying evolution behaviors (follow changing underlying trajectories), which presents new challenges to describe precisely the dynamics of system reliability. Thus, this paper proposes a novel data-driven approach to address the problems including the identification of varying operating conditions, the construction and dynamical updating of evolution model, and finally the online prediction of system reliability, focusing on systems under one common and typical case of varying operating conditions, the multi-state operating condition. Experiments based on artificial data and some widely studied real reliability cases reveal that the proposed method has superior performance compared with some existing benchmark approaches, in the case under consideration. This improved reliability prediction provides fundamental basis for advanced prognostics such as the Remaining Useful Life (RUL) estimation.

Keywords: Reliability prediction; Multi-state varying conditions; Support vector regression; Particle filter; Possibilistic clustering classification (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:177:y:2018:i:c:p:35-49

DOI: 10.1016/j.ress.2018.04.027

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