智能制造中的深度视觉：用于智能质量监测与诊断的MODERN框架

中文摘要 AI 辅助

智能制造流程通常配备大量传感器、成像设备与计算机，这不仅能实现生产系统各模块间的即时通信，还能助力智能制造管理。本文介绍MODERN——一款用于质量监测与故障隔离的深度学习框架，将这些增强能力融入工业质量控制实践。我们采用Inception残差神经网络架构，开发了监测产品含缺陷概率的控制图；还提出基于迁移学习的缺陷区域估计器，用于识别缺陷区域。为将框架扩展至训练数据不足的场景，我们提出仅需少量样本的迁移监测技术，以及用于定量评估方法适用性的假设检验方法。理论上，我们确定了缺陷概率估计与故障诊断的极小极大最优收敛速率。研究结果得出看似反直觉的管理启示：制造商未必总是应不计成本升级监测设备。实验中，我们通过模拟实验与真实数据，将所提方法与最新技术对比，证明其性能更优。

英文摘要

Smart manufacturing processes are often installed with a large number of sensors, imaging devices and computers, which not only enable instant communication across various modules of a production system but also aid in intelligent manufacturing management. In this paper, we introduce MODERN, a deep learning framework for quality monitoring and fault isolation, which integrates these enhanced capabilities into the practice of industrial quality control. Using the architecture of an inception residual neural network, we develop a control chart that monitors the likelihood of a product containing defects. We also propose a faulty region estimator that identifies the defective area using transfer learning. To extend our framework to cases where there are not sufficient training data, we suggest a transfer monitoring technique that requires only a small sample size and a hypothesis testing approach for quantitatively assessing the applicability of our method. Theoretically, we establish the minimax optimal convergence rate for both our defect likelihood estimation and fault diagnosis. Our results lead to a seemingly counter-intuitive managerial implication - it may not always be in a manufacturer's best interests to keep upgrading its monitoring equipment regardless of the cost. Empirically, we demonstrate the superior performance of our method in comparison with a state-of-the-art approach using both simulated experiments and real data.

发表机构

• Miami University（迈阿密大学）
• Wuhan University（武汉大学）
• University of Miami（迈阿密大学）
• The University of British Columbia（不列颠哥伦比亚大学）

机构由 AI 辅助整理，请以论文原文为准。