Motor Compound Fault Adaptation Transfer Learning of Rotary Machinery for Multi-source Signal and Small Samples
To solve the problem of insufficient representation in single source signal for different position compound fault informa-tion and small sample of compound fault signal in rotary machinery motor,a multi-head convolutional neural network transfer learning model for motor compound fault based small samples was proposed,to realize the multi-source heterogeneous migration diagnosis of mo-tor compound fault under small samples.Taking the original data of current,vibration and other sources in the power units as input,a model of multi-head convolutional neural network by hyperparameter optimization was constructed.Then,the original dataset of the large sample single fault was taken as the source domain,and the small sample compound fault transfer learning network model of the motor with the original data as the input under the target domain was constructed.Finally,the regularization penalty term was applied to the transfer learning model,and a criterion for updating the model's objective function parameters was constructed,to achieve adaptive upda-ting and adaptation of the model parameters between the source and target domains.Experimental results show that the diagnostic relia-bility of single source information depends on the selection of data sources,the information fusion and feature extraction of current and vibration signals is realized by the multi-head convolutional neural network mode.Compared with other models,the proposed method can significantly improve the recognition accuracy of motor compound fault based small samples,and the convergence time is shortened by nearly 2/3.
万方数据
维普
