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

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.

Advanced Control Design and Fault Diagnosis

“…• Remaining Useful Life Prediction of MOSFETs via the Takagi-Sugeno Framework by Marcin Witczak, Marcin Mrugalski and Bogdan Lipiec [5].…”

Health Indicator Similarity Analysis-Based Adaptive Degradation Trend Detection for Bearing Time-to-Failure Prediction

“…One problem is that random changes in current degradation trends (DTs) often decrease the accuracy of TTF prediction. Taking the most used exponential model as an example, researchers leveraged it to fit monitored data, and then future degradation status can be predicted for TTF prediction [18]. In this way, satisfactory results can be obtained if the HI trajectory or DT maintains an exponential-like degradation profile.…”

An Overview on Fault Management for Electric Vehicle Onboard Chargers

“…Power metal-oxide field-effect transistors (MOSFETs) are standard power semiconductors in various applications, including OBCs. Many methods that allow the diagnosis of both individual MOSFETs and the complete systems that comprise them can be found in the literature [3].…”