工业资产实体问答系统：面向工业资产维护的神经符号操作智能系统

摘要

工业维护环境日益依赖人工智能系统来协助操作人员理解设备行为、诊断故障及评估干预措施。尽管大语言模型（LLMs）能够实现流畅的自然语言交互，但已部署的维护助手常产生基于遥测数据支撑薄弱的通用解释，遗漏可验证的数据来源，且无法为反事实或面向行动的推理提供可检验的支持，这削弱了在安全关键场景中的可信度。我们提出IndustryAssetEQA——一种神经符号操作智能系统，该系统将时序遥测表征与故障模式影响分析知识图谱（FMEA-KG）相结合，实现对工业设备的具身问答（EQA）。我们在涵盖旋转机械、涡扇发动机、液压系统及信息物理生产系统四种工业设备类型的四个数据集上进行评估。与纯LLM基线相比，IndustryAssetEQA将结构有效性提升最高达0.51，反事实推理准确度提升最高达0.47，解释蕴涵度提升0.64，同时将专家评定的严重过度断言从28%降至2%（降幅约93%）。代码、数据集及FMEA-KG详见https://github.com/IBM/AssetOpsBench/tree/IndustryAssetEQA/IndustryAssetEQA。

English

Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evaluating interventions. Although large language models (LLMs) enable fluent natural-language interaction, deployed maintenance assistants routinely produce generic explanations that are weakly grounded in telemetry, omit verifiable provenance, and offer no testable support for counterfactual or action-oriented reasoning that undermine trust in safety-critical settings. We present IndustryAssetEQA, a neurosymbolic operational intelligence system that combines episodic telemetry representations with a Failure Mode Effects Analysis Knowledge Graph (FMEA-KG) to enable Embodied Question Answering (EQA) over industrial assets. We evaluate on four datasets covering four industrial asset types, including rotating machinery, turbofan engines, hydraulic systems, and cyber-physical production systems. Compared to LLM-only baselines, IndustryAssetEQA improves structural validity by up to 0.51, counterfactual accuracy by up to 0.47, and explanation entailment by 0.64, while reducing severe expert-rated overclaims from 28% to 2% (approximately 93% reduction). Code, datasets, and the FMEA-KG are available at https://github.com/IBM/AssetOpsBench/tree/IndustryAssetEQA/IndustryAssetEQA.