Health State Estimation and Remaining Useful Life Prediction of Power Devices Subject to Noisy and Aperiodic Condition Monitoring

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.

Automated summary

Condition monitoring of power devices is crucial for safety and mission-critical systems. Noise and aperiodic degradation measurements can negatively impact health assessment performance, but a proposed method in this article addresses these challenges by incorporating uncertainties and using a stochastic expectation-maximization algorithm for parameter estimation. Numerical analysis and testing on SiC MOSFETs validate the accuracy and robustness of the method.