Disconnector fault diagnosis based on multi-granularity attention mechanism
In view of the problem that most existing deep learning methods can only work with limited labeled sam-ple data,which makes the diagnosis model too serious,resulting in high accuracy when training the model but low fault identification accuracy when put into use,this paper studies isolation switches a high-accuracy diagnosis meth-od for small data sample sets in different working conditions,and a Multi-Granular Attention Mechanism(MG-AM)network framework for checking isolation switch fault diagnosis under different working conditions is constructed.First,this framework preprocesses the isolation switch fault data to obtain enhanced data samples and data feature libraries.Next,the time comparison module is used to compare the fault data roughly,and several possibilities of the fault condition are preliminarily obtained.The original data are predicted by the multi-granularity context com-parison module,and the predicted results are compared with the enhanced data.Then,making full use of the col-lected sample data,the labeled and unlabeled sample data are input into the network,and the network is optimized simultaneously through semi-supervised learning and unsupervised learning.Finally,the isolation switch fault diag-nosis model is established to realize the accurate identification of the unknown sample fault data.The experimental results show that the MG-AM network framework can effectively use the inherent samples for fault diagnosis,and has a good recognition rate,with an average recognition rate of 96.47%.
万方数据
维普
