Progressive Transfer Learning: An Intelligent Fault Diagnosis Method for Unlabeled Rotating Machinery With Small Samples

Artificial intelligence is increasingly applied in the field of mechanical fault diagnosis, achieving remarkable success in various industrial applications. Intelligent fault diagnosis models require diverse and representative mechanical fault datasets for adequate training. However, in actual industrial environments, labeled fault data is difficult to obtain, posing significant challenges for the deployment and effectiveness of intelligent fault diagnosis models. Therefore, this paper proposes a Progressive Transfer Learning Network (PTLN) for equipment fault diagnosis under small-sample, unlabeled conditions. This method uses labeled fault data from laboratory-simulated faults as the source domain and a small amount of unlabeled fault data from industrial environments as the target domain. PTLN achieves the goal of transferring source domain data to the target domain’s distribution by progressively constraining the mapped features. In addition, multi-kernel clustering is used to generate pseudo-labels for the target domain fault samples. Finally, a state classifier is employed to identify faults in the mechanical equipment. In the experiments on gears and bearings, the proposed method’s average recognition rate was 4.12% and 3.00% higher, respectively, than the average recognition rates of six other advanced fault diagnosis methods. The results show that the proposed method demonstrates superior robustness and accuracy in unlabeled mechanical fault diagnosis.