Machine learning- assisted remaining useful lifetime prediction of power electronic converters

This paper presents a methodology for the remaining useful life (RUL) prediction in power electronic converters based on the health monitoring of semiconductor devices and DC capacitor(s). The primary components considered are Gallium Nitride High Electron Mobility Transistors (GaN HEMTs) and aluminum electrolytic capacitors (AECs). The proposed methodology leverages long-term component characterization under accelerated aging testing in a laboratory setup. A statistical approach based on uniform probability density functions (PDFs) is utilized to estimate the system-level probability of survival under the desired operating conditions. Given that the PDFs and system-level probability calculations involve taking integrals and other complex operations, a machine learning (ML)-assisted model is utilized to reduce the computational burden on the controller units in power electronics converters. A neural network (NN) is used to process the experimentally derived degradation data and the extracted PDFs for arriving at a simple model presented by a few matrices that can even be deployed on modern microcontrollers or field-programmable gate arrays (FPGAs) for in-situ implementation. Experimental results obtained using a laboratory-scale prototype show that the proposed data-driven approach can achieve accuracy levels higher than 99% in predicting the time evolution between degradation checkpoints under accelerated thermal cycling conditions. This validation confirms the model's consistency with established statistical approaches across the full reliability range (T99-T01). Hence, this approach can identify aged or potentially failing converters in-situ and avoid early decommissioning, thereby extending the operational life.