A Graph Attention Based Multichannel Transfer Learning Network for Wheelset Bearing Fault Diagnosis With Nonshared Fault Classes

Most fault diagnosis methods require that the source and target machines’ fault classes should overlap and the number of samples should be comparable. However, such assumptions are unrealistic in the wheelset-bearing fault diagnosis. The high reliability of wheelset bearings and the difficulty of collecting fault signals make the datasets insufficient. However, the datasets obtained by laboratories can be rich in the number of samples, types of faults, and working conditions, which contain a wealth of fault information. Therefore, a graph attention-based multichannel transfer learning network (GAMTLN) is proposed. Fault features of multiple working conditions are transferred by combining a recurrence graph attention residual network (ResGANet) with multiple distribution adaptations and a multichannel diagnosis decision strategy. The former enriches the prior knowledge under small samples and improves the transfer effect through margin disparity discrepancy (MDD). The latter can jointly train multiple channels by a multichannel loss compensation strategy. The nonshared fault class filtering is combined to obtain useful information in nonshared fault class samples and avoid the negative effects of large domain differences. Two cases verify that GAMTLN can enhance the wheelset bearings’ fault diagnosis accuracy and fully use the existing fault samples as to the traditional methods.