  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.
