Generalized Simulation-Based Domain Adaptation Approach for Intelligent Bearing Fault Diagnosis

In recent years, various deep learning techniques have been utilized for dealing with bearing fault diagnosis. Although these methods have achieved remarkable accomplishments, they are challenging to apply in practice due to the difficulty in collecting fully labeled data in the industry. This paper proposes a solution to this issue by developing a generalized approach for simulating the vibration signals of bearings to establish data for deep learning. The proposed solution is based on insights from the classical physical model of bearings combined with an experimental method to estimate the natural frequency parameter and damping coefficient for simulation. The proposed simulation method can generate sufficiently high-quality data for the training of deep learning models. An efficient domain adaptation (DA) method is introduced to create a diagnostic model that can manage the domain shift between simulated training data and real-world testing data. Results from experiments conducted on the HUST bearing dataset indicate the proposed approach achieves a high performance in fault classification with up to 99.75% accuracy. Compared with other simulation methods on classical machine learning and DA models, the proposed approach exhibits superior performance in fault diagnosis and shows great promise for real-world industrial applications.