用多源数据生成可解释的维修决策建议，提升工业维护可靠性。
Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data
- 融合文本、传感器与故障知识，构建统一推理框架
- 在真实数据中验证支持条件化决策，错误率显著降低
- 适合需要可解释性与人工监督的工业运维场景
工业维护平台包含丰富的但分散的证据，如自由文本工单、异构运行传感器或指标，以及结构化故障知识。这些信息常被孤立分析，导致警报或预测无法支持条件决策：根据资产历史和行为，当前发生了什么？应采取何种行动？我们提出条件洞察代理（Condition Insight Agent），一个已部署的决策支持框架，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动作。系统通过确定性证据构建和结构化故障知识约束推理，并采用基于规则的验证循环抑制无依据结论。来自生产环境CMMS系统的案例研究显示，该验证优先设计在异构且不完整数据下仍可靠运行，同时保留人工监督。结果表明，受控的大型语言模型推理可在工业维护中作为有治理的决策支持层有效运作。
原文摘要 · Abstract (English)
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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