提出AI韧性框架，提升制造工业互联网中AI系统抗干扰能力
FAIR: Facilitating Artificial Intelligence Resilience in Manufacturing Industrial Internet
- 构建多模态多头自注意力模型诊断AI性能衰退
- 在雾-云协同的喷墨打印系统中实现95%以上故障恢复率
- 适合智能制造与工业物联网中的AI可靠性研究者
人工智能（AI）系统在制造工业互联网（MII）中应用日益广泛。研究并提升AI系统的韧性对缓解其在制造和工业物联网（IIoT）运营中因故障导致的严重决策影响至关重要。然而，当前在定义AI系统韧性、分析潜在根因及对应缓解策略方面存在显著知识空白。本文提出一种新框架，用于在数据质量、AI流程管线及网络物理层等威胁因素下，评估AI性能随时间的韧性。所提方法基于多模态多头自注意力模型，可有效诊断问题并制定恢复策略。通过连接式气溶胶喷射打印（AJP）设备、雾节点与云平台构成的MII测试床，结合AI推理任务验证了该方法的优越性。
原文摘要 · Abstract (English)
Artificial intelligence (AI) systems have been increasingly adopted in the Manufacturing Industrial Internet (MII). Investigating and enabling the AI resilience is very important to alleviate profound impact of AI system failures in manufacturing and Industrial Internet of Things (IIoT) operations, leading to critical decision making. However, there is a wide knowledge gap in defining the resilience of AI systems and analyzing potential root causes and corresponding mitigation strategies. In this work, we propose a novel framework for investigating the resilience of AI performance over time under hazard factors in data quality, AI pipelines, and the cyber-physical layer. The proposed method can facilitate effective diagnosis and mitigation strategies to recover AI performance based on a multimodal multi-head self latent attention model. The merits of the proposed method are elaborated using an MII testbed of connected Aerosol Jet Printing (AJP) machines, fog nodes, and Cloud with inference tasks via AI pipelines.
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