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arXiv · 2609.23582

ARID: A Deployable Edge AI System for Structured Information Extraction from Industrial Maintenance Work Orders

Kuanlin Chen·Chen-Wei Kuo
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Abstract

Maintenance work orders must often be processed offline on embedded hardware, yet downstream software requires predictable structured output. We present ARID (Aviation-inspired Routing for Industrial Deployment), which extracts component, failure mode, symptom, and maintenance action into fixed-schema JSON on an 8 GB NVIDIA Jetson Orin NX. ARID combines conservative dual-teacher filtering, targeted noise-aware synthesis, one routing decision per work order, 4-bit inference, and grammar-constrained decoding. From 2,326 unlabeled OMIn records, it retains 716 training pairs and adds 99 topology-constrained records targeting action extraction. On 300 human-labeled records, ARID reaches 84.8% token-F1 on the reference stack and 82.9% on the deployed Jetson. Resident serving achieves 5,310/5,656 ms P50/P99 at 12.5 W. On zero-shot MaintNet transfer, semantic F1 falls to 46.4% while parser success remains at least 99.8%, showing that output validity transfers but field semantics do not.

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Kuanlin Chen, Chen-Wei Kuo. 2026-09-20. ARID: A Deployable Edge AI System for Structured Information Extraction from Industrial Maintenance Work Orders. https://arxiv.org/abs/2609.23582

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