@inproceedings{odonncha-etal-2026-evidence,
title = "Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data",
author = "O{'}Donncha, Fearghal and
Zhou, Nianjun and
Martinez, Natalia and
Rayfield, James T and
Heath III, Fenno F. and
Langbridge, Abigail and
Vaculin, Roman",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 6: Industry Track)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.50/",
doi = "10.18653/v1/2026.acl-industry.50",
pages = "736--748",
ISBN = "979-8-89176-394-4",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data
%A O’Donncha, Fearghal
%A Zhou, Nianjun
%A Martinez, Natalia
%A Rayfield, James T.
%A Heath III, Fenno F.
%A Langbridge, Abigail
%A Vaculin, Roman
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F odonncha-etal-2026-evidence
%X 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.
%R 10.18653/v1/2026.acl-industry.50
%U https://aclanthology.org/2026.acl-industry.50/
%U https://doi.org/10.18653/v1/2026.acl-industry.50
%P 736-748
Markdown (Informal)
[Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data](https://aclanthology.org/2026.acl-industry.50/) (O’Donncha et al., ACL 2026)
ACL
- Fearghal O’Donncha, Nianjun Zhou, Natalia Martinez, James T Rayfield, Fenno F. Heath III, Abigail Langbridge, and Roman Vaculin. 2026. Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 736–748, San Diego, California, USA. Association for Computational Linguistics.