Entropy-enhanced batch sampling and conformal learning in VGAE for physics-informed causal discovery and fault diagnosis

Industry 4.0 has increased the demand for advanced fault detection and diagnosis (FDD) in complex industrial processes. This research introduces a novel approach to causal discovery and FDD using Variational Graph Autoencoders (VGAEs) enhanced with physics-informed constraints and conformal learning...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Computers & chemical engineering Ročník 197; s. 109053
Hlavní autoři:	Modirrousta, Mohammadhossein, Memarian, Alireza, Huang, Biao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.06.2025
Témata:	Causal discovery Conformal learning Fault detection and diagnosis Physics-informed constraints Variational Graph Autoencoders Variational Graph Autoencoders Physics-informed constraints Conformal learning Fault detection and diagnosis Causal discovery
ISSN:	0098-1354
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Industry 4.0 has increased the demand for advanced fault detection and diagnosis (FDD) in complex industrial processes. This research introduces a novel approach to causal discovery and FDD using Variational Graph Autoencoders (VGAEs) enhanced with physics-informed constraints and conformal learning. Our method addresses limitations in conventional techniques, such as Granger causality, which struggle with high-dimensional, nonlinear systems. By integrating Graph Convolutional Networks (GCNs) and an entropy-based dynamic edge sampling method, the framework focuses on high-uncertainty regions of the causal graph. Conformal learning establishes rigorous thresholds for causal inference. Validated through simulation and case studies, including an Australian refinery and the Tennessee Eastman Process, our approach improves causal discovery accuracy, reduces spurious connections, and enhances fault classification. Integrating domain-specific physics information also led to faster convergence and reduced computational demands. This research provides an efficient, statistically robust approach for causal discovery and FDD in complex industrial systems. •Novel VGAE framework combines physics-informed constraints & conformal learning for causal discovery.•Dynamic edge sampling with entropy-based method targets high-uncertainty regions in process graphs.•Physics domain knowledge integration enhances model convergence and computational performance.•Framework validated through industry cases: Australian refinery & Tennessee Eastman process.•Method shows higher causal accuracy & fewer spurious links in complex industrial systems.
ISSN:	0098-1354
DOI:	10.1016/j.compchemeng.2025.109053