Multi-adversarial deep transfer network for multi-source open-set fault diagnosis of rotating machinery with category shift

Abstract

Multi-source open-set fault diagnosis (MS-OSFD) with category shift is a critical challenge in industrial scenarios, as it involves potential unknown faults in the target domain and diverse label spaces across multiple source domains. The existing OSFD methods ignore the challenges posed by multiple source knowledge and category shift, resulting in incomplete fault knowledge transfer and subpar fault diagnosis performance. Aiming at the abovementioned problems, a multi-adversarial deep transfer network (MADTN) for MS-OSFD with category shift of rotating machinery is proposed. First, a multi-source fault classes matching (MSFCM) module is designed to align the data distribution of multiple source domains and alleviate the influence of category shift. Second, adversarial learning is adopted to reduce the domain gap between each pair of the source domain and target domain, and multiple fault identifiers integrated decision (MFIID) strategy is developed to obtain comprehensive predictions of target domain fault samples. Third, a global alignment (GA) is proposed based on the predictions of MFIID to further improve the domain-invariant feature extraction between domains. Finally, extensive experimental results demonstrate the superiority of the proposed MADTN method.