Novel method for failure prognostics of power MOSFET

Zhao, Min; Zhang, Donglai; Zhou, Zhicheng; Li, Tiecai; Zi-cai, Wang

doi:10.1109/civemsa.2015.7158628

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Xuerong Ye et al used a simulation-based health assessment methodology for SMPS [28]. Min Zhao et al made a proposal for failure prediction methodology for SMPS power MOSFET using an equivalent circuit model [29]. A major part of SMPS studies is based on a simulated model of the equivalent SMPS circuit, which requires a physics-of-failure modeling and mathematical knowledge of the SMPS.…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

et al. 2021

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Switched-mode power supply (SMPS) has been of vital importance majorly in power management of industrial equipment with much-improved efficiency and reliability. Given the diverse range on loading and operating conditions of SMPS, several anomalies can occur in the device resulting to over-voltage, overloading, erratic atmospheric conditions, etc. Electrical over-stress (EOS) is one of the commonly used causes of failure among power electronic devices. Since there is a limitation for the SMPS in terms of input voltage and current (two methods of controlling an SMPS), the device has been subjected to an accelerated aging test using EOS. This study presents a two-fold approach to evaluate the overall state of health of SMPS using an integration of extended Kalman filter (EKF) and deep neural network. Firstly, the EKF algorithm would assist in fusing fault features to acquire an comprehensive degradation trend. Secondly, the degradation pattern of the SMPS has been monitored for four different electrical loadings, and a bi-directional long short-term memory (BiLSTM) deep neural network is trained for future predictions. The proposed model provides a unique approach and accuracy in SMPS fault indication with the aid of electrical parameters.

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Section: Motivation and Literature Reviewmentioning

confidence: 99%