文章基本信息

标题：Remaining useful life prognostic of power metal oxide semiconductor field effect transistor based on improved particle filter algorithm:
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作者：Lifeng Wu ; Jing Yang ; Zhen Peng 等
期刊名称：Advances in Mechanical Engineering
印刷版ISSN：1687-8140
电子版ISSN：1687-8140
出版年度：2017
卷号：9
期号：12
页码：1-7
DOI：10.1177/1687814017749324
语种：English
出版社：Sage Publications Ltd.
摘要：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 non?linear 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.
关键词：Remaining useful life; metal oxide semiconductor field effect transistor; strong tracking Kalman filter; prognostic