Papers for

field service software developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Edge AI system extracts structured data from maintenance work orders

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

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.

Sun 20 SeptComputation and LanguageArtificial IntelligenceMachine Learning
The gist
Maintenance work orders often need to be processed on small computing devices without internet access, but the results must be clear and structured. The authors present ARID, a system that reads and organizes key information from these work orders into a fixed digital format on an embedded NVIDIA Jetson device. ARID uses several techniques to improve accuracy and efficiency, including filtering training data and controlling output formatting. It works well on tested data and runs quickly on low-power hardware, though its understanding of new task types can be limited.
Open → 2609.23582v1