Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis

2026-08-14

AI Summary

This study addresses the challenges of quality monitoring and fault isolation in smart manufacturing by proposing the MODRN framework. Integrating Inception residual networks with transfer learning, control charts, and hypothesis testing, this approach enables defect probability monitoring, fault localization, and quality diagnosis under small-sample conditions. Theoretically, we establish the minimax optimal convergence rate for transfer-based monitoring and reveal the critical insight that equipment upgrades are not necessarily beneficial. Empirical evaluations on both simulated and real-world datasets demonstrate that MODRN significantly outperforms state-of-the-art methods, validating its effectiveness and theoretical optimality. Consequently, this work provides a novel paradigm for intelligent quality inspection in industrial applications.

Abstract

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.

Problem

• Smart Manufacturing
• Quality Monitoring
• Fault Diagnosis
• Deep Learning
• Transfer Learning

Innovation

• MODERN Framework
• Inception Residual Neural Network
• Transfer Learning
• Small Sample Monitoring
• Minimax Optimal Convergence Rate