Remaining Useful Life Prediction for Circuit Breaker Based on SM-CFE and SA-BiLSTM

Targeting at the uncertainty of the degradation of conventional circuit breakers (CCBs) and the perfect mechanical degradation characterization by vibration signals, a remaining useful life (RUL) prediction method based on similarity measure (SM), comprehensive feature extraction (CFE), and self-attention bidirectional long short-term memory network (SA-BiLSTM) is proposed in this article. First, the degradation-sensitive intrinsic mode functions (IMFs) of the vibration signals are separated by the SM method, and the reconstructed signals are divided into intervals based on the working process of CCBs to obtain the vibration signals for life prediction. Second, the explicit parametric features (EPFs) are obtained based on the vibration events and waveform features, and the implicit parametric features (IPFs) are extracted using the multiscale convolutional autoencoder (MSCAE). Next, the comprehensive features are obtained by the combination of the EPFs and IPFs. On this basis, the degradation mode judgment method is formed by using the least square method (LSM) linear fitting of the EPFs. Finally, a quantitative life prediction model based on SA-BiLSTM is constructed, and greater weights are assigned to the important time steps. The proposed method is proved to show high prediction accuracy and good stability, which is more advantageous compared with other hybrid prediction models.

I. Introduction

With the development of switchgears in intelligence, it is very important to apply the intelligent sensing and advanced detection technologies to the health management of them. Conventional circuit breakers (CCBs) are switchgears with complex mechanical components and rated current of up to 8000 A [1]. Life prediction is an important reflection of the intelligence level of switchgears, and the remaining useful life (RUL) prediction is important to enhance the operational reliability of CCBs [2].