Predicting Power Electronics Device Reliability under Extreme Conditions with Machine Learning Algorithms

• DOI: 10.48550/arxiv.2107.10292
• Publication Date: 2021-01-01
• Authors: Olivares, Carlos; Rahman, Raziur; Stankus, Christopher; Hampton, Jade; Zedwick, Andrew; Ahmed, Moinuddin
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Systems and Control (eess.SY); Electrical Engineering and Systems Science - Systems and Control; Machine Learning (cs.LG)

11 pages, 8 figures. Submitted to IEEE Transactions on Device and Materials Reliability<br/>Power device reliability is a major concern during operation under extreme environments, as doing so reduces the operational lifetime of any power system or sensing infrastructure. Due to a potential for system failure, devices must be experimentally validated before implementation, which is expensive and time-consuming. In this paper, we have utilized machine learning algorithms to predict device reliability, significantly reducing the need for conducting experiments. To train the models, we have tested 224 power devices from 10 different manufacturers. First, we describe a method to process the data for modeling purposes. Based on the in-house testing data, we implemented various ML models and observed that computational models such as Gradient Boosting and LSTM encoder-decoder networks can predict power device failure with high accuracy.<br/>