Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions

Artificial intelligence-based fault diagnosis has recently been the subject of extensive research. However, the model learned from source data exhibits poor performance in target pattern recognition due to different data distributions caused by variable working conditions. Therefore, the transfer learning (TL) method, which reuses acquired knowledge and diagnoses the target domain fault without labels, has elicited the attention of researchers. The common deep TL method reduces the distance between the source and target domains in accordance with a certain divergence criterion that should be designed differently for specific tasks, leading to poor generalization results. In this study, we propose a knowledge mapping-based adversarial domain adaptation (KMADA) method with a discriminator and a feature extractor to generalize knowledge from target to source domain. The discriminator achieves the distance metric of the neural network wherein the target feature extractor maps the target data into the source feature space to explore domain-invariant knowledge. To accelerate the adversarial training process, KMADA fully utilizes the parameters obtained from the supervised pre-training. In addition, comparison analysis with other TL methods indicates the irreplaceable superiority of the KMADA, which achieves the highest diagnosis accuracy. Moreover, the visualization results demonstrate that the proposed model extracts the domain-invariant feature to realize knowledge mapping diagnosis, and thus, the model exhibits considerable research prospects.