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Mohamed El Hail
The Laboratory of Industrial Engineering and Computer Science (LGII), National School of Applied Sciences Ibn Zohr University Agadir, Morocco
Engineering

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Intelligent Prognostic Framework for Degradation Assessment and Remaining Useful Life Estimation of Photovoltaic Module Nabil Laayouj; Hicham Jamouli; Mohamed El Hail
Journal of Engineering and Technological Sciences Vol. 48 No. 6 (2016)
Publisher : Institute for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2016.48.6.9

Abstract

All industrial systems and 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 accurate prediction of the remaining useful life (RUL) is an important challenge in condition-based maintenance. Prognostic activity allows estimating the RUL before failure occurs and triggering actions to mitigate faults in time when needed. In this study, a new smart prognostic method for photovoltaic module health degradation was developed based on two approaches to achieve more accurate predictions: online diagnosis and data-driven prognosis. 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 its good prediction of RUL and can be effectively applied to many systems for monitoring and prognostics.
Co-Authors Hicham Jamouli Nabil Laayouj