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, which leading to the low diagnosis reliability. And transfer learning can solve such problems by learning domain invariant features. In this paper, a multi-channel convolutional online transfer network (MC-OTN) 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.