A robust source-free unsupervised domain adaptation method based on uncertainty measure and adaptive calibration for rotating machinery fault diagnosis

Unsupervised domain adaptation (UDA), usually trained jointly with labeled source data and unlabeled target data, is widely used to address the problem of lack of labeled data for new operating conditions of rotating machinery. However, due to the expensive storage costs and growing concern about data privacy, source-domain data are often not available, leading to the inapplicability of UDA. How to perform domain adaptation in scenarios without access to the source data has become an urgent problem to be solved. To this end, we propose a robust source-free unsupervised domain adaptation method based on uncertainty measure and adaptive calibration for fault diagnosis. The method only requires the use of the lightweight source model and unlabeled target data, which provides a new possibility to deploy domain adaptation models on resource-limited devices with good protection of data privacy. Specifically, based on proposed channel-level and instance-level uncertainty measures, adaptive calibration of source-domain model knowledge and target-domain risk samples during domain transfer is performed to attenuate the effect of negative transfer. Then, entropy minimization and target-domain diversity loss are introduced to redistribute the target samples and realize domain adaptation. Extensive cross-domain diagnostic experiments on two datasets demonstrate the effectiveness of the proposed method.