Remaining useful life prediction of circuit breaker operating mechanisms based on wavelet-enhanced dual-tree residual networks

The remaining useful life prediction of circuit breaker operating mechanisms is crucial for the condition-based maintenance of national power grids. To realize accurate remaining useful life prediction, a novel wavelet-enhanced dual-tree residual network is proposed in this paper. Through this wavelet transform, the time series is decomposed into two components (high frequency and low frequency). Then the two decomposed components are fed into two lightweight residual neural network structures. By concatenating the dual-tree features, the remaining useful life of a circuit breaker operating mechanism can be predicted. The proposed network is validated using a full-life cycle experiment of the circuit breaker operating mechanism. Results show that the proposed method has good capability when it comes to predicting the remaining useful life of the circuit breaker operating mechanism. Along with application in the construction of smart grids and green energy, it is expected that the proposed method has potential in running state prognostics of circuit breakers.