TL;DR
IndustryAssetEQA is a neurosymbolic system that enhances industrial asset maintenance by providing grounded, verifiable, and counterfactual reasoning capabilities for Embodied Question Answering, outperforming LLM-only approaches.
Contribution
The paper introduces IndustryAssetEQA, a neurosymbolic system combining telemetry data and FMEA-KG for improved industrial asset reasoning and explanation accuracy.
Findings
Improves structural validity by up to 0.51
Enhances counterfactual accuracy by up to 0.47
Reduces severe overclaims from 28% to 2%
Abstract
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,…
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