Plant Memory: A Technical Language Processing and ISO 14224-Aligned Maintenance Ontology for Industrial Equipment Failur

Summary:Plant Memory is a system for managing industrial equipment failure knowledge based on Technical Language Processing (TLP) and the ISO 14224 standard. It processes SAP Plant Maintenance (PM) work order exports to build an ontology that includes components, failure modes, and root causes. The system uses a rule-first extraction approach, supplemented by a large language model (LLM) for validation, and employs a technician review queue for fact verification. Tests on a synthetic dataset show that t

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Technical Mechanism Analysis

Plant Memory achieves the effective management of industrial equipment failure knowledge through the following steps:

1. Data Preprocessing and Rule-Based Extraction: The system first preprocesses the SAP PM work order exports to identify and extract key fields such as components, failure modes, and root causes. The extraction process uses a rule-first approach to ensure high coverage and accuracy.

2. LLM-Assisted Validation: On top of the rule-based extraction, the system introduces a large language model (LLM) as an auxiliary validation tool. The LLM analyzes the text content to identify potential errors or omissions and compares its findings with the rule-based extraction results. If discrepancies arise, the system increases the review priority of the fact for further verification by technicians.

3. Technician Review and Fact Verification: The system presents the extracted facts to technicians through a review queue, where they can confirm, correct, or reject the facts. Additionally, the system poses knowledge-gap questions for repeated failure modes, allowing technicians to answer and supplement the ontology.

4. Ontology Graph Construction and Query Service: The final step involves constructing an ISO 14224-aligned ontology graph and providing query services through a web application and Model Context Protocol (MCP) server. Both users and AI agents can query the same verified knowledge, with each answer citing the underlying work orders.

Engineering Trade-offs and Performance

• Strengths:

  • High Accuracy and Coverage: Tests on synthetic data show that Plant Memory excels in component identification, failure mode recognition, and root cause precision.
  • Scalability: The system is designed modularly, allowing easy adaptation to other domains and datasets.
  • Human-AI Collaboration: The technician review queue enables human-AI collaboration, enhancing the accuracy and reliability of knowledge management.

• Challenges and Limitations:

  • Data Quality Dependency: The system's performance heavily relies on the quality of the input data. Poor quality SAP PM exports may affect the accuracy of extraction and verification.
  • LLM Dependency: While the LLM as an auxiliary tool improves accuracy, it also introduces additional computational costs and complexity.

Developer Deployment and Implementation Recommendations

1. Data Preprocessing: Ensure the quality of SAP PM exports, performing data cleaning and preprocessing as necessary to enhance system performance.

2. Domain Adaptation: Adjust and maintain the ontology graph structure and content according to specific application scenarios to meet the knowledge management needs of different domains.

3. Optimizing Human-AI Workflow: Design an efficient technician review process to ensure timely processing and feedback of the review queue, thereby improving the overall efficiency of the system.

4. Continuous Learning and Updates: Regularly update and maintain the ontology graph, introducing new failure modes and root causes to maintain the system's accuracy and timeliness.

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Source: ArXiv AI (cs.AI) (2026-10-15)

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Tags: #Plant Memory Follow #Technical Language Processing Follow #ISO 14224 Follow #Industrial Equipment Failure Management Follow #AI Knowledge Management Follow