A coarse-to-fine bi-level adversarial domain adaptation method for fault diagnosis of rolling bearings

Recently, domain adaptation has been used to solve the fault diagnosis problem in rolling bearings. However, most of the existing methods only align the distribution of domains, and ignore the fine-grained information of the same fault categories in different domains, which leads to the degradation of diagnostic performance. To address such domain difference issues, this paper proposes a novel coarse-to-fine bi-level adversarial domain adaptation approach (C2FADA) for bearings fault diagnosis. Firstly, a sparse auto-encoder (SAE) is used to extract features from raw data (containing both the source and target domains), and a Kullback-Leibler (KL) divergence term is then introduced to measure the discrepancy between the features from the source domain and the target domain. Secondly, a bi-level adversarial module is established to gradually align different domains at the domain level (with a coarse-grained model) and the class level (with a fine-grained approach) to tackle the domain shift issue, and enable the classifier to learn the domain invariant representation features. Thirdly, a spectral norm regularization constraint term is introduced to improve the stability of adversarial training process by mitigating the effect of adversarial perturbations. results show that the classification performance of the proposed C2FADA method is better than the compared existing peer methods.