Remaining Useful Lifetime Prediction of Discrete Power Devices by Means of Artificial Neural Networks

Abstract

This work proposes a deep learning-based model for predicting the lifetime of power devices subjected to power cycling. To this purpose, a neural network based on bidirectional long short-term memory is adopted. The neural network is trained with experimental on-voltage degradation profiles. The application of the proposed method is based on the monitoring of a precursor, that is the on-voltage degradation. According to considered precursor, the model allows predicting the remaining useful lifetime (RUL) of power components. In order to prove the accuracy of the model, TO-247 power devices are stressed under power cycling and their wear-out is experimentally investigated. RUL predicted by the neural network is then compared with the experimental lifetime of power devices. Thanks to the proposed deep learning model, the accuracy in the lifetime estimation improves as long as more information about the state of health of the device under test is acquired.

Keywords

• Power cycling
• IGBT
• semiconductor power device reliability
• remaining useful lifetime
• artificial neural network