基于贝叶斯理论的HotellingT2小样本多元工序质量监控方法  

HotellingT2 Small Sample Multivariate Process Quality Monitoring Method Based on Bayesian Theory

作　　者：宁方华[1] 骞文成 屠震元 陈智峰 NING Fanghua;QIAN Wencheng;TU Zhenyuan;CHEN Zhifeng(School of Mechanical Engineering,Zhejiang Sci-tech University,Hangzhou 310018,China;Hangzhou Hikvision DIGITAL Technology Co.,Ltd.,Hangzhou 310051,China)

机构地区：[1]浙江理工大学机械工程学院,浙江杭州310018 [2]杭州海康威视数字技术股份有限公司,浙江杭州310051

出　　处：《软件导刊》2024年第1期122-127,共6页Software Guide

基　　金：国家自然科学基金项目(51475434);浙江省2023年度“尖兵“”领雁”研发攻关计划项目(2022C01SA111123)。

摘　　要：适用于多元质量控制的传统HotellingT2控制图方法,在小批量加工时因样本数据少而对异常值较敏感,存在一定局限性。为此,将贝叶斯理论与传统HotellingT2控制图相结合,通过历史批次工序质量分布信息和现有样本实时数据估计贝叶斯参数,构造基于贝叶斯理论的HotellingT2工序质量控制图,以在小批量加工的多元质量控制过程中抵御异常值造成的影响。通过发动机凸轮轴工序质量控制实例分析表明,所提方法相较于传统方法能有效抵御异常值造成的影响,具有良好的抗干扰性和稳定性,能更好地监测工序质量控制的失控状态。Traditional Hotelling T2 control chart method suitable for multivariate quality control has certain limitations in small batch process-ing due to its sensitivity to outliers due to limited sample data.To this end,Bayesian theory is combined with traditional Hotelling T2 control charts to estimate Bayesian parameters through historical batch process quality distribution information and existing real-time sample data.A Hotelling T2 process quality control chart based on Bayesian theory is constructed to resist the impact of outliers in the multivariate quality con-trol process of small batch processing.The analysis of quality control examples for engine camshaft processes shows that the proposed method can effectively resist the impact of outliers compared to traditional methods,has good anti-interference and stability,and can better monitor the uncontrolled state of process quality control.

关 键 词：多元质量控制 贝叶斯理论 HotellingT2控制图 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]