The Need

Industries operating safety‑critical and asset‑intensive systems struggle to leverage the full value of disparate maintenance data sources. Current maintenance management and optimization tools operate in silos, limiting real‑time decision‑making, automation, and scalability. No existing solution unifies component data, monitoring signals, fault models, risk information, and cost structures into a cohesive, machine‑interpretable framework. A standardized, interoperable knowledge layer is urgently needed to enable intelligent, data‑driven maintenance planning.

The Technology

OSU engineers have developed Multiple Aspects Maintenance Ontology (MuAMO), a unified, extensible knowledge framework combined with a suite of system interfaces. MuAMO integrates data from monitoring systems, fault detection tools, risk assessment platforms, and maintenance management systems into a single structured ontology. Algorithms within the platform automatically aggregate, reason over, and query maintenance‑relevant information, enabling selection of optimal maintenance actions. A built‑in heuristic method allows seamless incorporation of external ontologies for rapid deployment across diverse industrial domains.

Commercial Applications

• Industrial Plants
• Nuclear Power Plants
• Avionics
• Electric Vehicles

Benefits/Advantages

• Comprehensive data unification: Integrates sensor data, fault models, maintenance logs, risk analytics, and cost structures within a single ontology.
• Plug‑and‑play interoperability: Seamlessly incorporates external ontologies and third‑party tools without extensive re‑engineering.
• Automated reasoning & optimization: Built‑in algorithms identify optimal maintenance actions, critical components, and cost‑risk tradeoffs in real time.
• Scalable across industries: Applicable to any safety‑critical or asset‑heavy environment with complex maintenance workflows.