Dual-path adversarial and progressive self-training network for cross-condition open-set domain adaptation fault diagnosis

Fault diagnosis approaches leveraging deep transfer learning typically assume that the fault classes in the source and target domains are identical. However, this assumption is often unrealistic in engineering practice due to operational complexity and uncertainty that result in unknown fault classes. In this paper, a dual-path adversarial and progressive self-training network (DAPSN) is proposed for cross-condition open-set domain adaptation (OSDA) fault diagnosis. A three-stage dual-path adversarial progressive self-training strategy is designed to mitigate negative transfer. Through the integration of a gradient reversal layer and a dual-path adversarial discriminator, DAPSN concurrently filters novel target samples and aligns representations of known classes, thereby enhancing inter-class separability and intra-class cohesion. Its effectiveness is verified under two OSDA scenarios. Comparative experiments demonstrate that the proposed DAPSN sustains high classification accuracy and reliable unknown-class detection across varying domain shifts and different proportions of open-set samples, outperforming existing baselines. The DAPSN method combines dual-path adversarial and progressive self-training mechanisms to achieve precise classification of known faults and effective identification of unknown faults.