基于Kalman滤波的过程调节与质量监控方法  

Process adjustment and quality monitoring method based on Kalman filter

作　　者：殷建军[1] 余忠华[1] 李兴林[2] 王兆卫[1] 

机构地区：[1]浙江大学机械与能源工程学院,浙江杭州310027 [2]杭州轴承试验研究中心有限公司,浙江杭州310022

出　　处：《浙江大学学报（工学版）》2008年第8期1419-1422,共4页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(70272041)

摘　　要：针对加工过程质量监控问题,提出了基于Kalman滤波原理的过程质量调节方法,推导了Grubbs调节模型和扩展Grubbs调节模型.应用能量原理和信噪比概念,给出了评定过程质量的平滑梯度指标函数及平滑处理方法,并利用该指标函数建立了过程调节与质量监控策略,解决了控制图不能直接参与过程质量调节的局限性.根据给出的过程调节准则,通过监控指标函数所处的阈值区间来确定是否介入或退出过程调节活动,从而避免可能出现的过调节或欠调节现象,实现与统计过程控制(SPC)方法的有效集成.模拟试验和实际应用结果表明,平滑梯度指标函数具有较高的过程质量评定能力,调节模型与质量监控方法是行之有效的.A process quality monitoring method based on Kalman filter theory was developed to solve the problem that the statistical process control （SPC） cannot intervene process adjustments. The Grubbs＇ harmonic adjustment rule and an extended rule were first deduced. A smooth gradient index function and a smoothing method were given, then a process adjustment protocol was set with the index function, and the problem that the control chart cannot be directly used to adjustment processes was hence solved. Furthermore, it is decided that a process adjustment is imported or exited according to the value of the index function with the given adjustment rules, adequately both over-adjustment and under-adjustment are avoided, and an integrated system of SPC and engineering process control （EPC） is established. The smooth gradient index function has high capability of assessing the process quality, and the simulating test prove the adjustment model and the integrated protocol are effective and practical.

关 键 词：KALMAN滤波 质量损失平滑梯度函数 过程调节 质量监控 

分 类 号：TH7[机械工程—仪器科学与技术]