ADAPTN: A progressive adaptive prototype network with dual-risk control for open-set fault diagnosis

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Abstract
Standard data-driven fault diagnosis methods typically rely on the assumption of identical label spaces across source and target domains. However, the emergence of novel failure modes in the target domain invalidates this premise, presenting a complex open-set fault diagnosis (OSFD) challenge where traditional closed-set techniques fail to generalize. To bridge this gap, we propose a novel Adaptive Prototype Network. This framework operates through a progressive two-stage paradigm: a warm-up phase that leverages prototype learning and domain adversarial training to establish coarse-grained alignment, followed by a refinement phase that optimizes the model via adaptive clustering and dual-risk minimization. Specifically, we introduce a dynamic sample weighting mechanism guided by a learnable unknown detector to suppress negative transfer. Extensive experiments on two distinct datasets demonstrate that proposed method effectively aligns shared classes while robustly segregating unknown faults, outperforming state-of-the-art baselines.
Funding Information
  • Zhenjiang Key Research and Development Program (GY2023020)
  • National Natural Science Foundation of China (No. 62276117)

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