RESEARCH PAPER
Physical-Causal Guided Adaptive Time-Frequency and Hypergraph Co-evolutionary Modeling Method for Industrial Equipment Monitoring
1
University of Electronic Science and Technology of China, China
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2026-02-04
Final revision date: 2026-04-04
Acceptance date: 2026-07-28
Online publication date: 2026-08-13
Publication date: 2026-08-13
Eksploatacja i Niezawodność – Maintenance and Reliability 2027;29(1):226533
KEYWORDS
Physical-causal constraintsAdaptive time-frequency decompositionHypergraph neural networkCo-evolutionary mechanismIndustrial equipment monitoring
TOPICS
ABSTRACT
Accurate monitoring of industrial equipment is important for production safety and reliability. This paper proposes a physical-causal guided adaptive time-frequency and hypergraph co-evolutionary modeling method. The method constructs neural network representations with parameterized basis functions, enabling adaptive time-frequency decomposition with energy conservation and modal orthogonality constraints. Differentiable physical projection operators ensure hard constraint satisfaction during training. A transfer entropy-based causal discovery mechanism converts causal relationships into hyperedge generation rules for dynamic hypergraph construction. Bidirectional cross-attention mechanisms achieve collaborative updates between time-frequency and hypergraph features. Experiments achieve 98.73% fault diagnosis accuracy on CWRU dataset, 91.77% cross-working-condition transfer accuracy, 82.56% accuracy at -6dB SNR, and 82.4% cross-dataset generalization accuracy on unseen equipment types.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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