Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data

• URL: http://arxiv.org/abs/2603.08171v1
• Date: Mon, 09 Mar 2026 09:51:50 GMT
• Title: Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data
• Abstract summary: Condition Insight Agent integrates maintenance language, behavioral abstractions of operational data, and engineering failure semantics to produce evidence-grounded explanations and advisory actions.<n>System constrains reasoning through deterministic evidence construction and structured failure knowledge, and applies a rule-based verification loop to suppress unsupported conclusions.
• Score: 1.5991617440415131
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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