Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

Marcin Witczak, Marcin Mrugalski and Bogdan Lipiec
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Marcin Witczak: Institute of Control and Computation Engineering, University of Zielona Góra, 65-246 Zielona Góra, Poland
Marcin Mrugalski: Institute of Control and Computation Engineering, University of Zielona Góra, 65-246 Zielona Góra, Poland
Bogdan Lipiec: Institute of Control and Computation Engineering, University of Zielona Góra, 65-246 Zielona Góra, Poland

Energies, 2021, vol. 14, issue 8, 1-23

Abstract: The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

Keywords: remaining useful life; soft computing; Takagi–Sugeno; degradation modeling; prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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