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IndustryAssetEQA, a neurosymbolic system, was developed to improve the reliability and trustworthiness of AI assistants in industrial maintenance by grounding LLM responses in telemetry data and a Failure Mode Effects Analysis Knowledge Graph (FMEA-KG). By combining episodic telemetry representations with the FMEA-KG, the system significantly enhances Embodied Question Answering (EQA) capabilities over industrial assets. Evaluations across four industrial asset types demonstrate that IndustryAssetEQA substantially improves structural validity, counterfactual accuracy, explanation entailment, and reduces overclaims compared to LLM-only baselines.
Neurosymbolic grounding of LLMs in telemetry and knowledge graphs slashes expert-rated overclaims in industrial maintenance explanations by 93%, making AI assistants far more trustworthy in safety-critical settings.
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.
