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Plant Memory: Technical Language Processing and a Verified Maintenance Ontology for Agent-Accessible Failure Knowledge from SAP PM Work Orders

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Briefings

Plant Memory is a system that converts unstructured SAP PM work orders into verified maintenance knowledge using technical language processing and an ISO 14224-aligned ontology. It extracts reified facts with confidence scores and verification states, serving both human technicians and AI agents via a web app and Model Context Protocol. On a synthetic 12-month dataset of 400 work orders from a parboiled-rice plant, rule-based extraction achieved 96.2% component accuracy, 88.9% failure-mode accuracy, and 100% root-cause precision at 62% coverage.

Key points

• Plant Memory applies technical language processing to SAP PM exports to build an ISO 14224-aligned maintenance ontology where every extracted statement is a reified fact carrying a confidence score.
• On a synthetic 12-month export of 400 work orders from a parboiled-rice plant, rule-based extraction reached 96.2% component accuracy, 88.9% failure-mode accuracy, and 100% root-cause precision at 62% coverage.

Context

Industrial equipment failure records are typically close to unusable for analysis due to abbreviated, misspelt texts and buried asset identities. This system addresses that by converting raw work orders into structured, verified knowledge accessible to both human technicians and AI agents.

Limitations

The 96.2% component accuracy and 88.9% failure-mode accuracy are derived from a synthetic 12-month export of 400 work orders from a parboiled-rice plant, not real-world operational data. The 100% root-cause precision at 62% coverage is based on this synthetic dataset only. The system's performance on real-world data remains unverified.

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