Semantic grounding for AI agents in industrial maintenance
Industrial maintenance and asset-intensive operations generate large volumes of equipment and sensor data, but the ability to use this data effectively in AI-supported workflows is often limited by fragmented semantics, inconsistent tagging, and unclear contextual meaning. In recycling and process-oriented environments, predictive maintenance is a high-value area where failures are costly and decision-making requires trust, traceability, and operational relevance. This case is grounded in real maintenance settings where heterogeneous data sources, equipment models, and domain terminology must be aligned to support scalable decision support.
Production and maintenance data is typically structured according to equipment models such as ISA-95 / IEC 62224, and operations apply OEE-based logic for prioritisation. A key objective is to understand how semantic models and knowledge graphs can support prompt engineering for AI agents in this setting. Predictive maintenance agents require not only access to sensor values, but machine-interpretable meaning: what a measurement represents, how it relates to asset condition, and how different systems define events, failures, and maintenance actions. Semantic grounding is therefore essential to reduce ambiguity and improve reliability in autonomous or semi-autonomous maintenance recommendations.
How does the level of Semantic Data Readiness affect the ability of AI agents to interpret equipment state and propose reliable maintenance actions?
Higher SDR levels (explicit semantics, traceability, and consistent terminology) correlate strongly with increased robustness, explainability, and trustworthiness in agentic predictive maintenance systems.
Predictive maintenance scenarios will be replicated using controlled datasets representing different SDR maturity levels. The same maintenance problem can be evaluated under varying degrees of semantic structure, from minimally tagged time-series data to fully linked semantic representations connected to asset models and failure ontologies. Comparative experiments will focus on interpretability, decision consistency, and error modes in AI-supported diagnostics.
The use case enables exploration of how semantic improvements can be introduced incrementally into existing maintenance data flows without disrupting production systems. Practical constraints such as legacy system structures, confidentiality, and operational safety shape the feasibility of SDR adoption. The case provides empirical feedback on what 'first steps' toward SDR look like in real industrial maintenance environments.
Connects to other AI-driven and trust-sensitive cases, particularly those involving multi-step reasoning, secondary use of operational data, and semantic governance across systems. Relates to broader questions of explainability and semantic stability in autonomous industrial decision support.