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Tong, Yan, Ku, Zhaoyu, Chen, Nanxin, Sheng, Hu.  2022.  Research on Mechanical Fault Diagnosis of Vacuum Circuit Breaker Based on Deep Belief Network. 2022 2nd International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT). :259–263.
VCB is an important component to ensure the safe and smooth operation of the power system. As an important driving part of the vacuum circuit breaker, the operating mechanism is prone to mechanical failure, which leads to power grid accidents. This paper offers an in-depth analysis of the mechanical faults of the operating mechanism of vacuum circuit breaker and their causes, extracts the current signal of the opening and closing coil strongly correlated with the mechanical faults of the operating mechanism as the characteristic information to build a Deep Belief Network (DBN) model, trains each data set via Restricted Boltzmann Machine(RBM) and updates the model parameters. The number of hidden layer nodes, the structure of the network layer, and the learning rate are determined, and the mechanical fault diagnosis system of vacuum circuit breaker based on the Deep Belief Network is established. The results show that when the network structure is 8-110-110-6 and the learning rate is 0.01, the recognition accuracy of the DBN model is the highest, which is 0.990871. Compared with BP neural network, DBN has a smaller cross-entropy error and higher accuracy. This method can accurately diagnose the mechanical fault of the vacuum circuit breaker, which lays a foundation for the smooth operation of the power system.