# Physics-Informed Graph Learning with Uncertainty Awareness for Open-Set Domain Generalization in Fault Diagnosis

arXiv: https://arxiv.org/abs/2607.04188 · HTML: https://mlsanity.com/paper/2607.04188/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 99 references that are in the map.
- First posted on arXiv 5 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Open Set Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1804.10427/) (2018)
- [Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study](https://mlsanity.com/paper/2003.03315/) (2020)
- [NCSAC: Effective Neural Community Search via Attribute-augmented Conductance](https://mlsanity.com/paper/2511.04712/) (2025)
- [A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis](https://mlsanity.com/paper/2606.23010/) (2026)
- [Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data](https://mlsanity.com/paper/2009.04756/) (2020)
- [Open-Set Fault Diagnosis in Multimode Processes via Fine-Grained Deep Feature Representation](https://mlsanity.com/paper/2508.09462/) (2025)

## Abstract (arXiv)

Intelligent industrial maintenance critically relies on reliable fault diagnosis of rotating machinery. However, it faces formidable challenges from unknown fault types and domain shifts induced by varying operating conditions, which is formally formulated as the open-set domain generalization (OSDG) problem. Existing methods are mainly data-driven, thereby overlooking the cascaded propagation of uncertainty across feature extraction, topological learning, and decision-making stages.To tackle this challenge, we propose PGU-OD, a novel Physics-Informed Graph Learning framework with Uncertainty Awareness for Open-set Domain generalization. First, it designs a physics-informed spectral attention module to extract condition-robust fault features, thereby suppressing perceptual uncertainty caused by frequency shifts. Further, it constructs an uncertainty aware adaptive graph learning mechanism to dynamically adjust the edge weights of the sample graph guided by class-scale Gaussian distribution parameters, which mitigates the structural propagation of uncertainty. Finally, a Gaussian-distribution-based adaptive boundary loss function and a dual-criteria open-set inference strategy are developed to optimize decision boundaries and reliably reject unknown faults. Extensive experimental evaluations on two public and widely used rotating machinery fault datasets demonstrate that the proposed PGU-OD outperforms state-of-the-art baselines in both known fault classification and unknown fault rejection under domain shifts.