Machinery fault diagnostic method based on numerical simulation driving partial transfer learning

Artificial intelligence (AI), which has recently gained popularity, is being extensively employed in modern fault diagnostic research to preserve the reliability and productivity of machines. The effectiveness of AI is influenced by the quality of the labeled training data. However, in engineering scenarios, available data on mechanical equipment are scarce, and collecting massive amounts of well-annotated fault data to train AI models is expensive and difficult. In response to the inadequacy of training samples, a numerical simulation-based partial transfer learning method for machinery fault diagnosis is proposed. First, a suitable simulation model of critical components in a mechanical system is developed using the finite element method (FEM), and numerical simulation is performed to acquire FEM simulation samples containing different fault types. Second, several synthetic simulation samples are generated to form complete source domain training samples using a generative adversarial network. Subsequently, the partial transfer learning network is trained to extract shared fault characteristics between the simulation and measured samples in the case of class imbalance. Finally, the resulting model is used to diagnose unknown samples from real-world mechanical systems in operation. The proposed method is tested on actual fault samples of bearings and gears obtained from a public dataset and experimental test rig available in our laboratory, achieving average classification accuracy of 99.54% and 99.64%, respectively. Comparison investigations reveal that the proposed method has superior classification and generalization ability when detecting faults in real mechanical systems.