Remaining Useful Life Prediction of Power Electronic Devices Using Recurrent Neural Networks

Abstract: The growing demand for sustainable technology has led to an increased application of power electronics. As these devices are often exposed to harsh conditions, their reliability is a primary concern for both manufacturers and users. Addressing these reliability challenges involves a set of activities known as Prognostics and Health Management (PHM). In PHM, predicting the Remaining Useful Life (RUL) is crucial. This prediction relies on identifying failure precursors, which signify the presence of degradation. These precursors are then used to construct a degradation model that enables the prediction of the remaining time that the device can work before failure. The project focuses on examining a MOSFET aging dataset from the NASA PCoE dataset depository and a diode aging dataset from Fraunhofer ENAS. The prediction of the remaining useful life of devices using failure precursors has been done by applying recurrent neural network (RNN) methods. However, the prediction results from a single feature is significantly deviated from the actual values. To improve the prediction, the age of the device was proposed as an additional feature. RNNs with a similar number of weights and RNNs with the same hyperparameters are implemented and their performance is evaluated by the accuracy of prediction. The results show that all the RNN models implemented manage to capture the characteristics of the aging data. Despite its simpler structure, the vanilla RNN manages to produce a comparable result with the GRU and LSTM by simpler mechanism and less number of weights. The results also reveal that the characteristics of the data have a significant impact on the final results.