An unsupervised intelligent fault diagnosis research for rotating machinery based on NND-SAM method

Abstract Currently, intelligent fault diagnostics of rotating machinery have significantly contributed to mechanical health monitoring. However, real-world labeled data obtained from high-value equipment such as gas turbine units, pumps, and other rotating components are occasionally insufficient for model training. This article proposes an unsupervised deep transfer learning model that can directly extract features from the data itself, thus reducing the number of training samples required. The well-designed neural network with a domain-specific antagonism mechanism aligns features between the source and target domains and so makes data-driven decisions more efficiently. The parameter-free gradient reversal layer is used as an optimizer, considerably reducing the cross-domain discrepancy and accelerating convergence. The average multi-classification accuracy under transferable conditions reaches 97%, 91%, and 95% over three cases of fault diagnosis. Moreover, the time consumption of the system improves by more than 3.5% compared to existing models. The results reveal that the suggested strategy is suitable for a challenging unlabeled dataset and represents a significant improvement over existing unsupervised learning techniques.