10 papers
Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support
Gary Simethy, Daniel Ortiz Arroyo, Petar Durdevic
Wastewater operators need answers grounded in how their plant's variables interact and how fast effects propagate, not in generic pretraining text, when asking causal questions suc…
Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support
Gary Simethy, Daniel Ortiz Arroyo, Petar Durdevic
Operators of safety-critical industrial processes increasingly rely on digital twins to screen control interventions, but such simulators rarely carry certified safety guarantees.…
DART: A Vision-Language Foundation Model for Comprehensive Rope Condition Monitoring
Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic
The condition monitoring (CM) of synthetic fibre ropes (SFRs) used in offshore, maritime, and industrial settings demands more than a classifier: inspectors need continuous severit…
Imagery Dataset for Remaining Useful Life Estimation of Synthetic Fibre Ropes
Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic
Remaining useful life (RUL) estimation of synthetic fibre ropes (SFRs) is critical for safe operation in offshore-crane, wind turbine installation, and heavy-load handling applicat…
Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment
Gary Simethy, Daniel Ortiz Arroyo, Petar Durdevic
Wastewater treatment plants (WWTPs) need digital-twin-style decision support tools that can simulate plant response under prescribed control plans, tolerate irregular and missing s…
STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
Incomplete sensor data is a major obstacle in industrial time-series analytics. In wastewater treatment plants (WWTPs), key sensors show long, irregular gaps caused by fouling, mai…