A deep learning-based approach for electrical equipment remaining useful life prediction

Abstract

Electrical equipment maintenance is of vital importance to management companies. Efficient maintenance can significantly reduce business costs and avoid safety accidents caused by catastrophic equipment failures. In the current context, predictive maintenance (PdM) is becoming increasingly popular based on machine learning approaches, while its research on electrical equipment such as low-voltage contactors is in its infancy. The failure modes are mainly fusion welding and explosion, and a few are unable to switch on. In this study, a data-driven approach is proposed to predict the remaining useful life (RUL) of the low-voltage contactor. Firstly, the three-phase alternating voltage and current records the life of electrical equipment by tracking the number of times it has been operated. Secondly, the failure-relevant features are extracted by using the time domain, frequency domain, and wavelet methods. Then, a CNN-LSTM network is designed and used to train an electrical equipment RUL prediction model based on the extracted features. An experimental study based on ten datasets collected from low-voltage AC contactors reveals that the proposed method shows merits in comparison with the prevailing deep learning algorithms in terms of MAE and RMSE.