Condition monitoring of power devices is highly critical for safety and mission-critical power electronics systems. Typically, these systems are subjected to noise in harsh operational environment contaminating the degradation measurements. In dynamic applications, the system duty cycle may not be periodic and results in aperiodic degradation measurements. Both these factors negatively affect the health assessment performance. In order to address these challenges, this article proposes a health state estimation and remaining useful life prediction method for power devices in the presence of noisy and aperiodic degradation measurements. For this purpose, three-source uncertainties in the degradation modeling, including the temporal uncertainty, measurement uncertainty, and device-to-device heterogeneity, are formulated in a Gamma state-space model to ensure health assessment accuracy. In order to learn the device degradation behavior, a model parameter estimation method is developed based on a stochastic expectation-maximization algorithm. The accuracy and robustness of the proposed method are verified by numerical analysis under various noise levels. Finally, the findings are justified using SiC metal-oxide-semiconductor field-effect transistors (MOSFETs) accelerated aging test data.
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Number of pages||16|
|Publication status||Published - 2021|
Bibliographical noteFunding Information:
Manuscript received December 16, 2020; accepted January 15, 2021. Date of publication January 25, 2021; date of current version February 12, 2021. This work was supported in part by the Villum Foundation through the Project of Light-AI for Cognitive Power Electronics, in part by the National Science Foundation under Award 1454311, and in part by the Semiconductor Research Corporation (SRC)/Texas Analog Center of Excellence (TxACE) under the Task ID 2712.026. The Associate Editor coordinating the review process was Lorenzo Ciani. (Corresponding author: Shuai Zhao.) Shuai Zhao, Yingzhou Peng, and Huai Wang are with the Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark (e-mail: email@example.com; firstname.lastname@example.org; email@example.com).
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- Degradation modeling
- gamma process
- noisy and aperiodic measurements
- particle filter (PF)
- remaining useful life (RUL) prediction
- SiC metal-oxide-semiconductor field-effect transistors (MOSFETs)