Discriminative fine-grained domain confusion framework for bearing fault diagnosis under varying working conditions

Distribution alignment is the foundation of cross-domain fault diagnosis and has a direct impact on the transfer diagnostic accuracy. Some representative joint distribution alignment methods reduce fine-grained distribution discrepancies between the source domain and the target domain by employing the maximum mean discrepancy metric and have achieved success in certain aspects. However, these methods neglect the interference of noise signals and do not consider the zero-mean characteristic of mechanical vibration signals, making them insufficient to fully characterize distribution discrepancies, thereby limiting the effectiveness of fine-grained distribution alignment. To this end, a discriminative fine-grained domain confusion (DFDC) framework is proposed in this article to achieve targeted fine-grained alignment. First, a novel reinforced memory discriminative feature extractor is proposed, which can extract fault-discriminative information from monitoring signals under noisy environments while overcoming the catastrophic forgetting problem of gated recurrent units. Then, a new robust fine-grained domain confusion mechanism is developed to enhance the discrepancy representation capability between source domain and target domain. Finally, the designed DFDC framework is validated through two scenarios, demonstrating excellent diagnostic performance.