Online diagnosis for rolling bearings based on multi-channel convolution and transfer learning

Abstract In recent years, the fault diagnosis methods based on deep learning have been widely applied. In practical engineering, there are great distribution differences between the training and testing data in the network, leading to low diagnosis reliability. Transfer learning can solve such problems by learning domain invariant features. In this paper, a multi-channel convolutional online transfer network model for rolling bearing fault diagnosis is proposed. In the model, the offline stage merges the time domain and frequency domain features of the original data. A three-channel dataset is constructed as input of the network. And the domain invariant features can be learnt by fully training the offline stage network model. The online model is initialized by the parameters transferred from the offline network. The model also designs an online update strategy according to the prediction error. So that the model can adapt to new data, and finally realize the online diagnosis of the rolling bearing fault state. The validity and accuracy of the model are verified by the different laboratory measurement of rolling bearing operating datasets.