AI让传统质量监控变智能，可自动预防和修复生产问题。
A Review of Artificial Intelligence Impacting Statistical Process Monitoring and Future Directions
- 用神经网络等AI方法分析单变量、多变量及图像质量数据。
- 生成对抗网络等技术已用于检测异常模式，提升监控精度。
- 适合关注智能制造与自适应控制的研究者和工程师。
统计过程监控（SPM）自百年前身以来广泛应用于制造、医疗和服务行业，传统方法以统计为主。近年来，人工智能（AI）与机器学习（ML）推动了新型应用发展。本文回顾了基于统计的SPM发展历程，探讨了在单变量、多变量、轮廓及图像质量监控中的各类AI方法，包括分类、模式识别、时间序列分析和生成式AI。其中，人工神经网络（ANN）、卷积神经网络（CNN）、循环神经网络（RNN）和生成对抗网络（GAN）是最广泛应用的模型。最后，文章展望了大型多模态模型（LMM）在复杂系统中推动SPM向智能过程控制（SMPC）演进的潜力，目标是实现异常自动纠正，提升系统自主性。
原文摘要 · Abstract (English)
It has been 100 years since statistical process control (SPC) or statistical process monitoring (SPM) was first introduced for production processes and later applied to service, healthcare, and other industries. The techniques applied to SPM applications are mostly statistically oriented. Recent advances in Artificial Intelligence (AI) have reinvigorated the imagination of adopting AI for SPM applications. This manuscript begins with a concise review of the historical development of the statistically based SPM methods. Next, this manuscript explores AI and Machine Learning (ML) algorithms and methods applied in various SPM applications, addressing quality characteristics of univariate, multivariate, profile, and image. These AI methods can be classified into the following categories: classification, pattern recognition, time series applications, and generative AI. Specifically, different kinds of neural networks, such as artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN), are among the most implemented AI methods impacting SPM. Finally, this manuscript outlines a couple of future directions that harness the potential of the Large Multimodal Model (LMM) for advancing SPM research and applications in complex systems. The ultimate objective is to transform statistical process monitoring (SPM) into smart process control (SMPC), where corrective actions are autonomously implemented to either prevent quality issues or restore process performance.
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