A multi-source data fusion driven power Field Effect Transistor health state assessment and remaining useful life prediction method

Abstract The metal oxide semiconductor field effect transistor (MOSFET) is subjected to various stresses due to the external and internal operating environments, leading to ageing and failure of the device. With multiple degradation mechanisms, a single piece of information can no longer fully reflect the health state of MOSFETs, so how to use multi-source data to characterise the state of the device and predict the remaining useful life (RUL) is an issue worth exploring. To address this problem, a method for constructing health indicators (HI) as well as predicting RUL using multi-source data is proposed. In this method, firstly, the features are computed by selecting the appropriate ageing signal from the ageing mechanism. Secondly, the extracted features are filtered using Pearson’s algorithm to find the features that are strongly correlated with longevity. Then, the filtered features are merged by dimensionality reduction using the kernel principal component analysis algorithm and the HI is constructed from the reduced result. Finally, an unsupervised clustering algorithm is used to classify the states based on the data distribution in HI, and the filtered features are used as input to the grey wolf optimisation bidirectional long short-term memory neural network to predict the RUL of the device. In this paper, the proposed method is validated using data from the MOSFET Accelerated Aging Experiment at the NASA Ames Centre of Excellence for Prediction. The results show that the method is able to achieve good results in health state assessment and RUL prediction of MOSFETs. The proposed method is an effective and comprehensive strategy that is more helpful for the operation and maintenance of electronics.