Simulation-Driven Domain Adaptation for Rolling Element Bearing Fault Diagnosis
Chenyu Liu, Konstantinos Gryllias
Abstract
State-of-the-art deep learning models remain data-intensive, requiring large training datasets to ensure their generalization ability. However, in industry, it is quite expensive or impractical to obtain massive training samples for condition monitoring practitioners. This article proposes a simulation-driven domain adaptation method to circumvent the data deficiency issue using physical-based simulations. A bearing phenomenological model is developed to generate simulated vibration signals. In the frame of domain adaptation transfer learning, a domain adversarial neural network (DANN) is proposed utilizing the simulated data as the source domain. The DANN can align the coarse supervised source domain data and the fine supervised target domain data to conduct adversarial training. Experimental results indicate that the proposed method can reach high classification accuracy using a small amount of real data. Compared to nonadapted and other transfer learning models, the proposed method demonstrates superior performance for bearing fault diagnosis, which is very promising for real industrial applications.