A Composite Failure Precursor for Condition Monitoring and Remaining Useful Life Prediction of Discrete Power Devices

Abstract

In order to prevent catastrophic failures in power electronic systems, multiple failure precursors have been identified to characterize the degradation of power devices. However, there are some practical challenges in determining the suitable failure precursor which supports the high-accuracy prediction of remaining useful life (RUL). This paper proposes a method to formulate a composite failure precursor (CFP) by taking full advantage of potential failure precursors, where CFP is directly optimized in terms of the degradation model to improve the prediction performance. The RUL estimations of the degradation model are explicitly derived to facilitate the precursor quality calculation. For CFP formulation, a genetic programming method is applied to integrate the potential failure precursors in a nonlinear way. As a result, a framework that can formulate a superior failure precursor for the given RUL prediction model is elaborated. The proposed method is validated with the power cycling testing results of SiC MOSFETs.

An Overview of Artificial Intelligence Applications for Power Electronics

“…In [160], a composite failure precursor of SiC MOSFETs is developed with a data fusion technique of genetic pro-gramming, which is a variant of GA. It integrates multiple degradation signals of a power semiconductor device in a nonlinear way.…”

“…Several feasible approaches include the Monte-Carlo methods [114], incorporating particle filter in the neural network [111], and Bayesian-based AI methods (e.g., Gaussian process, relevance vector machine). Another promising direction is the stochastic data-driven methods [154], [160], [168], which can intrinsically provide the PDF of the RUL for calculating the confidence interval. 2) Adaptive capability: It is concerned with the the model parameter tuning layer in Fig.…”

“…If these information sources and models are simultaneously exploited, possible biases can be mitigated to improve robustness. Multilevel information fusion can be performed at the data-level [160], [172], feature-level, decision-level [96], and their combinations, in order to exploit the insights of each information sources. For example, the well-established differential equations of power converter system can be integrated with AI as a hybrid solution for condition monitoring.…”

An overview of artificial intelligence applications for power electronics

“…In [159], a composite failure precursor of SiC MOSFETs is developed with a data fusion technique of genetic programming, which is a variant of GA. It integrates multiple degradation signals of a power semiconductor device in a nonlinear way.…”

“…Several feasible approaches include the Monte-Carlo methods [113], incorporating particle filter in the neural network [110], and Bayesian-based AI methods (e.g., Gaussian process, relevance vector machine). Another promising direction is the stochastic data-driven methods [153,159,167], which can intrinsically provide the probability density function (PDF) of the RUL for calculating the confidence interval. 2) Adaptive capability: It is concerned with the the model parameter tuning layer in Fig.…”