Deep learning network based lifetime analysis of energy - fed traction power supply converter

Abstract

This paper presents a life prediction method based on the parameters of the actual operation history data collected by the existing converter power unit sensors. Firstly, the characteristics of junction temperature curves of forced air-cooled radiator and power unit are extracted, and the deep learning neural network architecture is constructed based on the characteristics. Then the thermoelectric coupling model of power unit based on thermal resistance calculation theory is established, and the cumulative loss is obtained from the measured data. The deep learning network is trained and the model prediction is verified. Finally, the power unit loss distribution under different setting temperature thresholds and the correlation analysis with radiator parameters are obtained, which provides a feasible scheme for parameter setting and life prediction.

Keywords

• inverter
• igbt
• lifetime prediction
• deep learning