A prognostic methodology for power MOSFETs under thermal stress using echo state network and particle filter

Reinforcing the reliability of power semiconductor devices is crucial for extending the lifetime of the power converter based electrical systems. This paper aims at developing a novel prognostics methodology for estimating the Remaining Useful Life (RUL) of the power Metal-Oxide Field-Effect Transistors (MOSFETs). The variation of on-state resistance as an important fault indicator under thermal overstress is utilized as the main database. A recently proposed neural network paradigm, namely Echo State Network (ESN) is utilized here to derive a degradation model, taking into account its high efficiency in modeling nonlinear dynamical systems. Meanwhile, a particle filter approach is developed to update the initially trained ESN model and to quantify the uncertainty of the RUL prediction online. The accuracy and efficiency of the proposed prognostic methodology has been verified based on an accelerated aging experimental dataset.