Prototype-Driven Class-Wise Adversarial Transfer Networks for Partial Domain Fault Diagnosis of Rolling Bearings
Yuteng Zhang, Hongliang Zhang, Rui Wang, Bin Chen, Haiyang Pan
Abstract
The main challenges for partial set cross-domain fault diagnosis problems, where the target label space is only a subset of the source label space, are to facilitate positive transfer between shared classes and avoid negative transfer caused by unrelated classes. To address the above challenges, a prototype-driven class-wise adversarial transfer network is proposed in this study. First, aiming to enhance the classification robustness, a fault prototype-based discrimination method without learnable parameters is designed to replace the traditional classifier for health state recognition. Then, based on the intrinsic similarity between the target samples and the fault prototypes, a novel prototype similarity based weighting mechanism is proposed to precisely measure the transferability of each source class, thus decreasing the contribution of unrelated source class samples. Finally, the proposed class-wise adversarial adaptation framework facilitates fine-grained knowledge transfer between shared classes and enhances domain adaptation performance. The experimental results show that the proposed method outperforms all the comparison methods, achieving over 10% improvement in average diagnostic accuracy on the two rolling bearing datasets and maintaining over 90% overall diagnostic accuracy.