基于自相关观测和隐马尔科夫模型的统计过程监控  

Tatistical process control based on hidden Markov models with auto-correlated observations

作　　者：张媛 陈震[1] 潘尔顺[1] 奚立峰[1] ZHANG Yuan;CHEN Zhen;PAN Ershun;XI Lifeng(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240

出　　处：《计算机集成制造系统》2018年第10期2388-2394,共7页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(51475289)~~

摘　　要：自相关现象在实际统计过程中广泛存在,传统控制图无法进行有效的监控。针对该问题,提出一种考虑自相关观测的隐马尔科夫模型。通过建立观测序列概率分布在时域上的一阶自相关关系,优化建模,并将其应用于过程监控,建立基于此模型的残差控制图。实例与仿真分析显示,与基于自回归移动平均模型相比,该方法具有预测准确、灵敏度高、可操作性强的特点,且对自相关过程的监控效果良好。The observations are usually auto-correlated in actual processes.To solve the problem that the traditional control charts could not monitor effectively caused by auto-correlation in actual processes,by considering the auto-correlated data,a modification of Hidden Markov Model(HMM)was proposed.Through building the first order auto-correlation relation of observation sequence probability on time domain,the residual chart was built.Results of case study and simulation showed that the proposed method made significant improvements in monitoring auto-correlation process.Specifically it performed higher sensitivity and was easier to be implemented by comparing with residual charts based on Auto-Regressive and Moving Average(ARMA)models.

关 键 词：统计过程控制 自相关观测 隐马尔科夫模型 控制图 

分 类 号：TH16[机械工程—机械制造及自动化] TP273[自动化与计算机技术—检测技术与自动化装置]