ARID: A Deployable Edge AI System for Structured Information Extraction from Industrial Maintenance Work Orders
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% ...
| Authors | Kuanlin Chen, Chen-Wei Kuo |
|---|---|
| Primary category | cs.CL |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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