Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation

To address the issue of low diagnostic accuracy caused by noise interference and varying rotational speeds in rolling bearings, a fault diagnosis method based on domain-conditioned feature correction is proposed for rolling bearings under complex working conditions. The approach first constructs a multi-scale self-calibrating convolutional neural network to aggregate input signals across different scales, adaptively establishing long-range spatial and inter-channel dependencies at each spatial location, thereby enhancing feature modeling under noisy conditions. Subsequently, a domain-conditioned adaptation strategy is introduced to dynamically adjust the activation of self-calibrating convolution channels in response to the differences between source and target domain inputs, generating correction terms for target domain features to facilitate effective domain-specific knowledge extraction. The method then aligns source and target domain features by minimizing inter-domain feature distribution discrepancies, explicitly mitigating the distribution variations induced by changes in working conditions. Finally, within a structural risk minimization framework, model parameters are iteratively optimized to achieve minimal distribution discrepancy, resulting in an optimal coefficient matrix for fault diagnosis. Experimental results using variable working condition datasets demonstrate that the proposed method consistently achieves diagnostic accuracies exceeding 95%, substantiating its feasibility and effectiveness.