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.

翻译：工业维护平台包含丰富但碎片化的证据，包括自由文本工单、异构运行传感器或指标以及结构化故障知识。这些来源通常被孤立分析，产生的警报或预测无法支持条件决策：给定此资产历史与行为，当前发生何种状况以及应采取何种行动？本文提出"状态洞察代理"——一个已部署的决策支持框架，该框架整合维护语言、运行数据的行为抽象与工程故障语义，生成基于证据的解释与建议措施。系统通过确定性证据构建与结构化故障知识约束推理过程，并应用基于规则的验证循环来抑制无依据的结论。来自生产CMMS部署的案例研究表明，这种验证优先的设计能在异构与不完整数据下可靠运行，同时保持人工监督。我们的研究结果展示了基于约束的LLM推理如何作为工业维护中受管控的决策支持层发挥作用。