Abstract：Aiming at the four typical defects of shunt capacitor,such as internal defect,contact defect,overlapping defect and oil defect,a fault diagnosis method of shunt capacitor based on Markov transfer field transformation of vibration signal is proposed. Firstly,four typical defect samples are made according to the field conditions,the mechanical vibration signals of the samples under different defects are tested,and the vibration data set is built. Then,the one-dimensional vibration signal is transformed into two-dimensional image based on Markov transfer field transform to improve the visibility of the signal. Finally,the feature Self Extraction and classification of Markov density map are carried out by convolution neural network. The visualization ability of signals under different quantiles is discussed and compared with common diagnosis methods. The results show that the Markov transfer field transformation of the vibration signal improves the visualization ability of the signal,and the deep learning algorithm can extract the signal features more comprehensively. The average recognition accuracy of the proposed method is about 98%,which can better realize the fault diagnosis of shunt capacitor,and is better than other common methods.
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2023, Vol. 44 
