Bearing Fault Diagnosis Under Variable Conditions Based on Domain Adaptive Migration Deep Learning
In order to realize accurate bearing fault identification under variable equipment and working conditions,a diagnosis method based on domain adaptive migration depth convolution neural network is proposed.For the training set and test set with different distribution features(i.e.different domains),the fault feature extraction module,domain recognition module and label classification module are constructed in the deep convolution neural network.The adaptive transfer ability of domain is realized by the way of feature extraction module and domain recognition module against training,so that the deep convolution neural net-work can effectively extract the common feature parameters of different domains.Four groups of migration experiments are de-signed based on the data of Case Western Reserve University and intelligent maintenance system center.The average recognition accuracy of traditional deep convolution neural network is 64.5%,and the average recognition accuracy of domain adaptive mi-gration convolution neural network is 94.9%,which fully shows that the domain adaptive migration deep convolution neural net-work can effectively identify bearing faults under variable equipment and working conditions.
Bearing Fault DiagnosisDomain Adaptive MigrationDeep Convolutional Neural NetworkAdver-sarial Training
万方数据
维普
