Gearbox Fault Diagnosis Based on Mixed Data-Assisted Multisource Domain Transfer Learning Under Unbalanced Data

In the actual working environment, mechanical equipment is in normal operation for a long time, which leads to unbalanced sample data collected and will be accompanied by problems such as changes in working conditions. All these issues contribute to problems such as the low accuracy of intelligent fault diagnosis models. In this regard, this article proposes a new method for fault diagnosis based on hybrid data-assisted multisource domain transfer learning under unbalanced conditions. First, fault signals simulated and real vibration signals are converted into 2-D images using the Gram angle product field (GAPF), and the conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) is used to augment the real data of the source domain to get high-quality, usable samples. Second, the mixed data consisting of these three are input into a residual network with receptive field attention–graph convolutional networks (ResRFA-GCN) common feature extractor as source domain samples and target domain samples to train the model and the extraction of common features. Next, the features extracted from each pair of sources and target domains are mapped into a specific space, the local maximum mean discrepancy (LMMD) is employed to minimize the difference in the distribution of the domains, and the module allows it to learn the domain-invariant properties between each pair of source and target domain subclasses. Finally, after realizing the classification in a specific classifier. The model is tested on two datasets, and satisfactory results are achieved, thus reducing the dependence of the intelligent fault diagnosis model on measured data of fault samples.