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Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data

Fearghal O'Donncha, Nianjun Zhou, Natalia Martinez, James T Rayfield, Fenno F. Heath, Abigail Langbridge, Roman Vaculin

TL;DR

Condition Insight Agent is presented, 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 and demonstrates how constrained LLM-based reasoning can function as a governed decision-support layer for industrial maintenance.

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.

Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data

TL;DR

Condition Insight Agent is presented, 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 and demonstrates how constrained LLM-based reasoning can function as a governed decision-support layer for industrial maintenance.

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.
Paper Structure (33 sections, 13 equations, 2 figures, 1 table)

This paper contains 33 sections, 13 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Setting and Task
  4. Condition Insight Framework
  5. Overview of Reasoning Pipeline
  6. Deterministic Evidence Construction
  7. Domain LLM Agent and Deterministic Verification
  8. Case Studies
  9. Case Study A: Product Integration
  10. Case Study B: Enterprise-Scale Deployment
  11. Operational Impact and Scalability
  12. Evaluation
  13. Effect of Evidence Enrichment and Prompt Constraints
  14. Overall Observations
  15. Conclusion and Lessons Learned
  16. ...and 18 more sections

Figures (2)

  • Figure 1: End‑to‑end pipeline of the Condition Insight System. Heterogeneous asset data and failure‑mode knowledge are consolidated into structured evidence packets, which are processed by a domain LLM agent with verifier‑based checking to produce a UI‑agnostic condition insight summary.
  • Figure 2: Example condition insight generated by the proposed framework using historical work orders of the last 200 days for a motor asset. The output separates evidence-based observations (top) from prioritized, actionable recommendations (bottom), synthesizing maintenance history, asset metadata, health indicators, and data consistency checks into a decision-oriented summary.