Machine Learning Based Prognostics for Predicting Remaining Useful Life of IGBT – NASA IGBT Accelerated Ageing Case Study

Abstract

Power electronic devices such as Insulated Gate Bipolar Transistors (IGBT) are widely used in electronic systems. The failure of IGBT may bring fatal damage to the whole system. The ability to accurately predict the remaining useful life (RUL) of IGBT can provide a basis for intelligent equipment maintenance, thereby minimizing the risk of catastrophe failure at work. The research in this field aims to achieve accurate and timely RUL prediction of equipment. This paper exploited the IGBT accelerated aging data from NASA Ames Research Center, and the monitoring data of IGBT is collected under thermal overstress load condition with square signal bias at gate. This paper developed three machine learning models to predict the RUL of IGBT, including Back Propagation Neural Network(BPNN), Random Forest and Extreme Learning Machine. These models realize real-time prediction of RUL, and the performance of these models is observed and analyzed.