Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data

2026-03-09

AI Summary

This study addresses the challenge of isolated analysis of multi-source heterogeneous data—such as work order texts, sensor signals, and fault knowledge—in industrial maintenance, which often hinders explainable condition-based decision-making. To overcome this limitation, the authors propose the Condition Insight Agent framework, which integrates maintenance language, abstracted operational behaviors, and structured fault semantics. By leveraging deterministic evidence construction and a rule-driven validation loop, the framework enables constrained yet reliable reasoning with large language models (LLMs). Designed to operate under human oversight, the approach effectively handles data sparsity and heterogeneity while generating interpretable, evidence-backed maintenance recommendations. Its reliability and practicality have been validated through real-world deployment in a Computerized Maintenance Management System (CMMS).

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Industrial maintenance platforms contain rich but fragmented evidence, including free-text work orders, heterogeneous operational sensors or indicators, and structured failure knowledge. These sources are often analyzed in isolation, producing alerts or forecasts that do not support conditional decision-making: given this asset history and behavior, what is happening and what action is warranted? We present Condition Insight Agent, a deployed decision-support framework that integrates maintenance language, behavioral abstractions of operational data, and engineering failure semantics to produce evidence-grounded explanations and advisory actions. The system constrains reasoning through deterministic evidence construction and structured failure knowledge, and applies a rule-based verification loop to suppress unsupported conclusions. Case studies from production CMMS deployments show that this verification-first design operates reliably under heterogeneous and incomplete data while preserving human oversight. Our results demonstrate how constrained LLM-based reasoning can function as a governed decision-support layer for industrial maintenance.

Problem

• Industrial Maintenance
• Heterogeneous Data
• Evidence-Driven Reasoning
• Conditional Decision-Making
• Failure Knowledge

Innovation

• evidence-driven reasoning
• constrained LLM reasoning
• heterogeneous data integration
• rule-based verification
• industrial maintenance decision support