Source-Free Adaptation Diagnosis for Rotating Machinery

Abstract

Domain adaptation technology has been intensively studied in machine fault diagnosis for more reliable diagnosis performance. Nonetheless, most approaches rely on the availability of source data, which is always unattainable in many practical industrial scenarios due to the costs of expensive data storage and transmission, as well as privacy protection. As a consequence, there is an urgent need to design an adaptation method that is independent of source data. This technology is also more in line with the requirements for lightweight and timely diagnosis. Given this, we develop a novel source-free adaptation diagnosis (SFAD) method in this work. In SFAD, a robust self-training mechanism and a target prediction matrix constraint are presented, achieving model adaption with only unlabeled target data. Extensive experiments on our own and public datasets demonstrate the effectiveness and superiority of the proposed method.