Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917324425003008 |
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| author | O'Donncha, Fearghal Zhou, Nianjun Martinez, Natalia Rayfield, James T Heath III, Fenno F. Langbridge, Abigail Vaculin, Roman |
| author_facet | O'Donncha, Fearghal Zhou, Nianjun Martinez, Natalia Rayfield, James T Heath III, Fenno F. Langbridge, Abigail Vaculin, Roman |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_08171 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data O'Donncha, Fearghal Zhou, Nianjun Martinez, Natalia Rayfield, James T Heath III, Fenno F. Langbridge, Abigail Vaculin, Roman Artificial Intelligence 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. |
| title | Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.08171 |