Source free progressive subspace calibration for transportation equipment diagnosis under time varying conditions

Plenty of source-free domain adaptation (SFDA)-based diagnosis approaches have been explored to tackle distribution shift and data privacy protection. Nevertheless, these approaches do not consider the uncertainty and large distribution characteristic of target predictions in time-varying conditions. Hence, their effectiveness is only suitable for scenarios with stable data distribution. Therefore, a new SFDA paradigm named source-free progressive subspace calibration (SFPSC) is developed to enhance model adaptation in complex dynamic data structures. In SFPSC, the CAS-Softmax is first constructed to suppress the overfitting of the source model, improving the mismatch of target samples. A new progressive pseudo-label selection strategy is designed for the training phase of model adaptation to gradually leverage easy-to-learn samples and guide the self-correction of hard-to-learn ones. Moreover, the scale robust subspace calibration is proposed to explicitly reduce the cross-domain distribution discrepancy. Finally, Extensive experiments on a laboratory gearbox and an actual equipment bearing validate the effectiveness and advantage of the proposed SFPSC.