Adaptive Boosting Based on Multi-class Neural Networks for IGBT Health Parameter Prediction

Insulated gate bipolar transistor (IGBT) has important applications in industrial development. However, the IGBT has a complex integrated structure and works in a harsh environment, so it is prone to fault and therefore causes economic losses. Considering this, it is of great significance to use data-driven fault prediction. The fault prediction of IGBT aging parameters based on device data can avoid complex modeling procedure and the workload, so it has wider applicability. In this paper, an adaptive boosting approach based on a multi-class neural network is proposed to realize the analysis of the fault prediction of the aging parameters of IGBTs. This paper uses the IGBT accelerated aging test data released by NASA for processing. In order to improve the quality of data processing, exponentially weighted moving average (EWMA) and outlier processing are used to preprocess. Various neural network approaches are used for time series prediction. Finally, the adaptive boosting algorithm based on single-class and multi-class neural networks is used to achieve better prediction performance compared to the neural network algorithms. The results show that the adaptive boosting approach to integrating multi-class neural networks has a good prediction performance.