Remaining Useful Life Prediction of IGBT Modules Across Working Conditions Based on ProbSparse Self-Attention

Abstract

In order to improve the accuracy of remaining useful life (RUL) prediction of insulated gate bipolar transistor(IGBT) modules across working conditions to enhance their reliability, an RUL prediction method based on the ProbSparse self-attention mechanism and transfer learning was proposed based on the transient thermal resistance features of IGBT modules under different working conditions. An accelerated aging test bench of the IGBT module was designed ang built to perform power cycling experiments in different temperature ranges, and state data of full life-time under different working conditions were collected. Transient thermal resistance change data during the IGBT module degradation were calculated, and the transient thermal features that can accurately reflect the aging state of the IGBT module were extracted and selected. These features were used to predict the RUL of IGBT modules across different working conditions based on the proposed method. The experimental result shows that the accuracy of the proposed RUL prediction method of IGBT modules across working conditions outperforms other compared methods. Particularly, the RUL prediction accuracy during the early degradation stage has been significantly improved.

Keywords

• insulated gate bipolar transistor(igbt) module
• transient thermal resistance
• remaining useful life (rul) prediction
• probsparse self-attention
• transfer learning