ARID：面向工业维护工单结构化信息提取的可部署边缘AI系统
ARID: A Deployable Edge AI System for Structured Information Extraction from Industrial Maintenance Work Orders
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中文总结 AI 辅助
针对嵌入式离线处理维护工单需可预测结构化输出的问题，ARID在Jetson上结合双教师过滤、噪声感知合成、4位推理与语法约束解码，实现高F1的固定模式JSON提取，并验证输出有效性可迁移但语义不可迁移。
中文摘要 AI 辅助
维护工单通常必须在嵌入式硬件上离线处理，但下游软件需要可预测的结构化输出。我们提出了ARID（航空启发的工业部署路由），该系统在8 GB NVIDIA Jetson Orin NX上将组件、故障模式、症状和维护动作提取为固定模式的JSON。ARID结合了保守的双教师过滤、针对性的噪声感知合成、每张工单一次路由决策、4位推理和语法约束解码。从2,326条未标记的OMIn记录中，它保留了716个训练对，并添加了99条针对动作提取的拓扑约束记录。在300条人工标记的记录上，ARID在参考堆栈上达到84.8%的token-F1，在部署的Jetson上达到82.9%。常驻服务在12.5 W功耗下实现5,310/5,656 ms的P50/P99延迟。在零样本MaintNet迁移中，语义F1降至46.4%，而解析器成功率仍至少为99.8%，这表明输出有效性可迁移，但字段语义不可迁移。
英文摘要
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.
发表机构
- National Tsing Hua University（国立清华大学）
机构由 AI 辅助整理，请以论文原文为准。
