The scarcity of labeled fault data in rotating machinery diagnosis poses significant challenges for model reliability, particularly when encountering novel operational scenarios or cross-domain applications. While unsupervised domain adaptation (UDA) has emerged as a promising solution, conventional UDA methods often exhibit static architectures that fail to dynamically adjust to target-domain distribution shifts, thereby limiting diagnostic accuracy and practical utility. To address these limitations, we propose an adaptive dynamic cooperative multisource domain adaptation network (ADCMDAN) for rotating machinery fault diagnosis. Firstly, a multi-perspective dynamic cooperative feature extractor is constructed to enable the model to dynamically adjust its architecture, thereby enhancing its ability to extract domain-invariant features across different domains. Secondly, a dynamic cooperative training mechanism is designed to adaptively adjust the training strategy that conducts targeted training for the model’s bi-module in different training phases, so as to strengthen the domain adaptation performance. Finally, a dual-object dynamic optimization loss is introduced to treat distinctively different source domains, thereby improving the utilization of existing diagnostic knowledge. Extensive experimental evaluations are conducted based on two different cases to rigorously assess the diagnostic performance of ADCMDAN. The results conclusively demonstrate that the proposed method achieves exceptional fault identification accuracy even in the absence of labeled target-domain data.
Lei et al. (Wed,) studied this question.