A novel method for predicting the remaining useful life of MOSFETs based on a linear multi-fractional Lévy stable motion driven by a GRU similarity transfer network

Metal-oxide-semiconductor field-effect transistors (MOSFETs) are the core components of electronic devices. Implementing effective and accurate remaining useful life (RUL) prediction for such electronic devices is crucial for achieving prognostics and health management (PHM). Therefore, this study establishes a power cycling accelerated aging experimental platform based on constant junction temperature fluctuation, using the change in on-state voltage as the performance degradation indicator for RUL prediction. Subsequently, to make full use of historical data for MOSFET devices RUL prediction under partial information, a novel hybrid prediction framework, combining linear multi-fractional Lévy stable motion (LMSM), gated recurrent unit (GRU), and transfer learning (TL), named GRU-TL-LMSM (GTLMSM) is proposed. In this framework, a degradation model based on LMSM is constructed to describe the long-range dependence, nonlinearity, multi-fractal, and incremental non-Gaussian distribution characteristics of the MOSFET degradation sequence. Unlike most stochastic process methods, to achieve adaptive fitting of the degradation trend and make full use of historical data under current partial information conditions, a degradation trend fitting method combining GRU network with similarity-based transfer learning is proposed. In this process, the optimal historical degradation trend similarity measurement method is constructed by combining dynamic time warping (DTW) and Wasserstein distance. By incrementally representing the degradation process of GTLMSM, the predicted RUL and corresponding probability density function (PDF) are obtained using Monte Carlo (MC) methods. The effectiveness and accuracy of the proposed prediction model for MOSFETs devices RUL prediction are validated through comparisons with existing methods and two datasets.