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New prognostic framework for degradation assessment and remaining useful life estimation of photovoltaic module

All industrial systems or machines are subjected to degradation processes, which can be related to the operating conditions. This degradation can cause unwanted stops at any time and major maintenance work sometimes. The prediction of an accurate Remaining useful life (RUL) of machines or industrial systems is an important challenge in condition based maintenance. Prognostic activity allows to estimate the RUL before a failure occurs and triggers actions to mitigate faults at the right time when needed. In this paper, we have developed a new smart prognostic method for photovoltaic module health degradation, which is based on two approaches; the on-line diagnosis and data-driven prognosis to achieve more accurate predictions. This framework of forecasting integrates the strengths of real time monitoring, in the first approach and Relevant Vector Machine in the second. The results show that the proposed method is plausible due to the good prediction of RUL, and can be effectively applied to many systems for monitoring and prognostics.