The power metal oxide semiconductor field effect transistor is used extensively in analog circuits and digital circuits. However, it is also the highest failure rate component in the power electronics system. In order to avoid serious failures in the electrical system, it is necessary to predict the remaining useful life of the metal oxide semiconductor field effect transistor. With the aim to accurately predict the remaining useful life, this article presents an improved particle filter method, which can estimate the metal oxide semiconductor field effect transistor degradation degree by the on-resistance (Ron) changing data. This method is based on the particle filter algorithm, which is suitable for nonlinear and non-Gaussian system; at the same time, the proposed method can avoid the degeneracy phenomenon in the particle filter. First, the strong tracking Kalman filter is used as the importance function to update the particles with the latest observation in the sample process. The Metropolis-Hastings algorithm is used to introduce some new particles to guarantee the effective particle number in the resampling process. The experimental results indicate that the improved particle filter algorithm can obtain more effective remaining useful life prognostic than that of the particle filter. KeywordsRemaining useful life, metal oxide semiconductor field effect transistor, strong tracking Kalman filter, prognostic Date

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