From Documents to Database: Failure Modes for Industrial Assets

2025-09-22

AI Summary

Traditional Failure Mode and Effects Analysis (FMEA) development for industrial equipment is highly manual, time-consuming, and suffers from low knowledge reuse. Method: This paper proposes a foundation-model-based approach for automated FMEA generation and structured database ingestion. It integrates domain-adapted natural language processing and information extraction to accurately identify fault modes, effects, causes, and detection mechanisms from unstructured technical documents, mapping them to standardized FMEA table entries. An interpretable, interactive correction mechanism enables expert feedback integration for iterative refinement, while structured outputs are automatically persisted into a relational database. Contribution/Results: Experiments demonstrate over 80% reduction in FMEA development cycle time, significantly improving efficiency and consistency in industrial asset knowledge construction. The approach validates the feasibility and practical value of foundation models in high-reliability industrial knowledge engineering applications.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Knowledge Engineering
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

We propose an interactive system using foundation models and user-provided technical documents to generate Failure Mode and Effects Analyses (FMEA) for industrial equipment. Our system aggregates unstructured content across documents to generate an FMEA and stores it in a relational database. Leveraging this tool, the time required for creation of this knowledge-intensive content is reduced, outperforming traditional manual approaches. This demonstration showcases the potential of foundation models to facilitate the creation of specialized structured content for enterprise asset management systems.

Problem

• Generating Failure Mode and Effects Analyses from technical documents
• Aggregating unstructured content across documents into structured database
• Reducing time required for creating knowledge-intensive industrial content

Innovation

• Interactive system using foundation models
• Generates FMEA from technical documents
• Stores structured content in database