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 article proposes a method to formulate a composite failure precursor (CFP) by taking full advantage of potential failure precursors (PFPs), 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 PFPs 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.
Bibliographical noteFunding Information:
Manuscript received January 27, 2020; revised April 11, 2020; accepted April 23, 2020. Date of publication April 30, 2020; date of current version October 23, 2020. This work was supported in part by the Innovation Fund Denmark through the project of Advanced Power Electronic Technology and Tools (APETT), in part by the National Science Foundation under the Award Number 1454311, and in part by the Semiconductor Research Corporation (SRC)/Texas Analog Center of Excellence (TxACE) under the Task ID 2712.026. Paper no. TII-20-0370. (Corresponding author: Shuai Zhao.) Shuai Zhao and Huai Wang are with the Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark (e-mail: firstname.lastname@example.org; email@example.com).
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- composite failure precursor (CFP)
- Condition monitoring (CM)
- genetic programming (GP)
- power devices
- remaining useful life (RUL)
- SiC MOSFETs