用无需分布假设的校准预测提升制造质量监控可靠性
Distribution-Free Process Monitoring with Conformal Prediction
- 结合校准预测的无分布假设方法，突破传统控制图依赖正态分布的限制
- 提出不确定性突增信号和基于p值的多变量异常检测新图表
- 保留经典方法易懂性，适合工业界落地应用
传统统计过程控制（SPC）在现代复杂制造环境中因依赖常被违背的统计假设而不可靠。本文提出一种混合框架，通过引入无需分布假设、模型无关的校准预测，增强SPC能力。提出两种新应用：校准增强型控制图，可可视化过程不确定性并发出如‘不确定性突增’等主动预警；校准增强型过程监控，将多变量控制重构为直观的异常检测问题，采用p值图表呈现结果。该框架在保持经典方法可解释性和易用性的基础上，提供了更鲁棒、统计上更严谨的质量控制方式。
原文摘要 · Abstract (English)
Traditional Statistical Process Control (SPC) is essential for quality management but is limited by its reliance on often violated statistical assumptions, leading to unreliable monitoring in modern, complex manufacturing environments. This paper introduces a hybrid framework that enhances SPC by integrating the distribution free, model agnostic guarantees of Conformal Prediction. We propose two novel applications: Conformal-Enhanced Control Charts, which visualize process uncertainty and enable proactive signals like 'uncertainty spikes', and Conformal-Enhanced Process Monitoring, which reframes multivariate control as a formal anomaly detection problem using an intuitive p-value chart. Our framework provides a more robust and statistically rigorous approach to quality control while maintaining the interpretability and ease of use of classic methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。