为工业时间序列模型提供可落地的鲁棒性评估与文档规范。
Industrial AI Robustness Card for Time Series Models
- 设计轻量级协议，整合漂移监测与压力测试
- 支持可复现的鲁棒性证据生成与监控触发定义
- 适配欧盟《人工智能法案》合规要求，适合工业落地
工业AI从业者面临新兴法规中模糊的鲁棒性要求，却缺乏可直接实施的规范。本文提出工业时间序列模型鲁棒性卡片（IARC-TS），一种轻量级协议，用于记录和评估工业时间序列模型。该协议规定了必需字段，并建立结合漂移监测、运行域监控、不确定性量化与压力测试的实证测量与报告流程，同时映射至欧盟《人工智能法案》中的文档、测试与监控义务。通过生物制药软传感器案例研究，展示了IARC-TS如何支持可复现的鲁棒性证据生成，并定义监控触发条件。
原文摘要 · Abstract (English)
Industrial AI practitioners face vague robustness requirements in emerging regulations and standards but lack concrete, implementation-ready protocols. This paper introduces the Industrial AI Robustness Card for Time Series (IARC-TS), a lightweight protocol for documenting and evaluating industrial time series models. IARC-TS specifies required fields and an empirical measurement and reporting protocol that combines drift and operational domain monitoring, uncertainty quantification, and stress tests, and maps these to selected EU AI Act documentation, testing, and monitoring obligations. A biopharmaceutical soft sensor case study illustrates how IARC-TS supports reproducible robustness evidence and defines monitoring triggers.
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