Multi-source information joint transfer diagnosis for rolling bearing with unknown faults via wavelet transform and an improved domain adaptation network

Recently, unsupervised domain adaptation fault diagnosis (FD) techniques, which learn transferable features by reducing distribution inconsistency of source and target domians, have gained abundant attention and greatly promoted the reliability of rolling bearing (RB) under variable operating conditions. However, open-set domain adaptation issues which contain unknown faults in the test set have not been well addressed. This paper presents a new semi-supervised FD method for RB by combining wavelet transform and an improved domain adaptation network. First, a multi-source domain adaptation network is proposed to extract rich transfer features and achieve complementary information from multiple sources. Then, a pseudo-margin vector is employed to handle unseen faults in the target domain and realize the accurate fault diagnosis of RB. Finally, a new loss function is designed by adding weights to the traditional maximum mean difference to make the common label set more compatible and combining a dynamic optimization strategy to adaptively update the loss of each part. Finally, two experiments indicate our proposed approach has a higher diagnosis accuracy and can effectively tackle the diagnosis issue of unseen faults across different working conditions.