[
    {
        "id": "osp-23444",
        "type": "article-journal",
        "title": "ARID: A Deployable Edge AI System for Structured Information Extraction from Industrial Maintenance Work Orders",
        "author": [
            {
                "family": "Chen",
                "given": "Kuanlin"
            },
            {
                "family": "Kuo",
                "given": "Chen-Wei"
            }
        ],
        "URL": "https://omanscience.com/en/articles/arid-a-deployable-edge-ai-system-for-structured-information-extraction-from-industrial-maintenance-work-orders",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]