Precursor Prediction and Early Warning of Power MOSFET Failure Using Machine Learning With Model Uncertainty Considered

Abstract

As the core component of power electronic systems, health monitoring of metal-oxide–semiconductor field-effect transistor (MOSFET) is extremely crucial. In this article, a hybrid failure precursor prediction model based on machine learning techniques is proposed. It consists of an isolation forest method and a long short-term memory (LSTM) network. The proposed model extracts information from different aspects of the input data to make predictions and can be sensitive to abnormal data behavior. By detecting the abnormality in the curve and predicting its future behavior, the model can give early warning of the power MOSFET failure and help avoid unexpected accidents. Besides, the model uncertainty is discussed. Two main factors that affect the model uncertainty of the proposed model are evaluated. To reduce the model uncertainty, a Bayesian neural network (BNN) is used to quantify the uncertainty of the proposed model with different parameters. The performance of the proposed model is verified based on the power MOSFET data collected from the accelerated life tests (ALTs). The experimental results indicate satisfying performances of the proposed model, because it can not only give early warning of MOSFET failures but also provide more stable prediction results with less model uncertainty compared with other existing models.