Adaptive Remaining Useful Life Prediction for Film Capacitors in DC-Link Applications Using Degradation and Failure Data

Remaining useful life (RUL) is crucial to the condition and health monitoring. This paper proposes an adaptive RUL prediction method for DC-link film capacitors for power electronic applications. By using the proportional hazards model framework, this method integrates the degradation data and the time-to-failure data, to quantify the component failure behavior in a probabilistic way. It employs a mixed-effects model to characterize the degradation behavior of capacitors. The hazard rate is applied to characterize the likelihood of capacitor failure. The operational conditions are incorporated into the hazard model to capture the influence on the RUL. The Bayesian updating mechanism is developed to calibrate and tailor the offline model for the in-situ component, enabling the adaptive RUL prediction along with sequential monitoring data in real time. The method is experimentally verified with film capacitors in DC-link applications subjected to accelerating humidity conditions. It is accompanied by an online tool at https://rul-capacitor.streamlit.app that can interactively investigate the method details.