In this paper, a simulation data-driven intelligent fault diagnosis algorithm based on attention mechanism and transfer learning is proposed to address insufficient fault data, low diagnostic accuracy, and inefficiency in rolling bearing monitoring under cross-condition and cross-location scenarios. To overcome the lack of real fault data, a dynamic vibration-response model is constructed through analysis of bearing and fault dynamics, generating high-fidelity fault signals across multiple operating conditions. Based on this, a diagnostic model is developed using a self-attention–assisted weighted autoencoder, where the proposed weighted autoencoder integrates a self-attention mechanism and a weight allocation mechanism and the former captures inter-feature dependencies while the latter adaptively reweighting feature contributions to enhance fault-discriminative representations. Therefore, the diagnostic model can assign corresponding weights to different importance features according to the constructed self-attention mechanism-assisted weighted self-encoding feature extraction model, effectively avoiding the problems of insufficient diagnostic accuracy and low diagnostic efficiency caused by feature redundancy and difficulty in distinguishing the importance of rolling bearing faults. Furthermore, the local maximum mean discrepancy (LMMD) method is applied to align both global and sub-domain distributions between simulated and measured data. By synthesizing cross-condition and cross-location simulated signals with real measurements, an LMMD-based intelligent transfer diagnosis model is built to enhance generalization and robustness against large distribution discrepancies. Finally, the stability and robustness of the proposed method are validated by analyzing the transfer learning performance and anti-noise disturbance ability across different operating conditions and locations.
Qiu et al. (Fri,) studied this question.