In order to monitor the working state of switchgear more effectively,the switchgear was taken as the re-search object,and the working state of the equipment was evaluated according to the non-contact voiceprint signal.A monitoring method of working state of switchgear based on chromaticity diagram and convolutional neural network is proposed.Firstly,the collected voiceprint signal is converted into chromaticity diagram,and then the characteristic atlas of normal and abnormal state of switchgear are constructed.Then the convolution neural network is used to deeply mine the features,and finally the different states of the switchgear can be identified.The experimental results show that the proposed method can effectively characterize different working states of switchgear,and the identifica-tion accuracy can reach 99.2％.
维普
万方数据