Hyperbolic Hypergraph Neural Networks for Hierarchical Fault Diagnosis in Rotating Machinery.

Lingzheng Pan, Kyaw Hlaing Bwar, Rifai Chai, Yuqi Wang, Boon Xian Chai

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Lingzheng PanDepartment of Materials Science and Engineering, Faculty of Engineering, Monash University, Melbourne 3800, Australia.

Kyaw Hlaing BwarSchool of Research, Swinburne University of Technology Sarawak, Kuching 93350, Malaysia.ORCID 0009-0007-7020-7735

Rifai ChaiSchool of Engineering, Swinburne University of Technology, Melbourne 3122, Australia.ORCID 0000-0002-1922-7024

Yuqi WangSchool of Engineering, Swinburne University of Technology, Melbourne 3122, Australia.ORCID 0009-0008-1745-7572

Boon Xian ChaiSchool of Engineering, Swinburne University of Technology, Melbourne 3122, Australia.ORCID 0000-0002-2873-8942

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intelligent fault diagnosis of rotating machinery is essential for ensuring the safety and reliability of industrial systems. While hypergraph neural networks (HGNNs) have recently shown promise for modeling high-order dependencies beyond pairwise graph methods, most existing variants operate in Euclidean space, which is not explicitly aligned with hierarchical fault-response structure (root cause to fault mode to observed response). To address this limitation, we propose Hyperbolic Hypergraph Neural Network (H

Indexed as

bearingdeep learningfault diagnosishyperbolic geometryhypergraph neural networksPoincaré ballrotating machineryvibration signals

Identifiers

PMID42515432

DOI10.3390/s26144549

PMCPMC13418414

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.