Digital-analog driven multi-scale transfer for smart bearing fault diagnosis

Abstract

Self-diagnosis and self-decision are crucial to smart bearing, where intelligent and robust models should be built and deployed on the smart bearing chip for an on-line edge effect. Whereas, this process requires a large amount of labeled prior data to train the fault identification model. Although the existing digital-analog driven transfer learning methods can realize fault identification under small samples, these algorithms mainly focus on how to reduce the difference between the two domains. These algorithms do not form a complete and applicable method for smart bearing fault diagnosis. Focusing on these issues, a digital-analog driven multi-scale transfer (DaD-MsT) method was proposed for smart bearing fault diagnosis. Different from the conventional methods, it can be achieved through end-side and edge-side cooperation, and the effect of transfer diagnosis is further improved by the proposed deep branch transfer network (DBTN) model. First, the smart bearing dynamic model is established, and the dynamic model response is obtained for use as source domain data in end-side. Then, a DBTN model was proposed to realize more effective digital-analog driven transfer learning. Finally, the trained model is deployed on the edge chip of the smart bearing for real-time fault identification and parameter fine-tuning. Experiments and comparisons verify the effectiveness of the proposed method in the case of small-sample data. Specifically, an online edge intelligent diagnosis system is also built to illustrate the ability in actual application of smart bearing intelligent diagnosis.