The conventional real - time monitoring method does not use the non - parametrical pdf of the principal components , which are capable of indicating the real - time changes of batch production processes 本文從這一背景出發(fā),重點是對用核函數(shù)法概率估計對間歇生產(chǎn)過程實時狀態(tài)監(jiān)測的方法進行較廣泛、深入的研究。
To improve the monitoring sensitivity , it is for the first time to propose the utilization of the pdf of the principal components in real - time to monitor batch production process 針對這一缺點,本文首次提出用kde提取主元的概率密度函數(shù)用作為實時狀態(tài)監(jiān)測圖,應(yīng)用于對間歇生產(chǎn)過程主元空間實時狀態(tài)監(jiān)測的問題上。
This paper investigates the application of the multivariate statistical process monitoring and control technology , which employs both multiway principal component analysis ( mpca ) and kernel density estimation ( kde ) , to real time status monitoring and fault diagnosis of batch production processes 本文主要研究了運用多向主元分析法和核函數(shù)法概率密度估計相結(jié)合的多元統(tǒng)計過程監(jiān)控技術(shù)對間歇生產(chǎn)過程進行實時的狀態(tài)監(jiān)測與故障診斷。
Kde is a non - parametric method which is capable of extracting the population ' s probability density function ( pdf ) based on data sample only without any a prior knowledge about the statistic properties of the data regime . in this thesis , it is conducted the implementation of the kde for monitoring the performance of batch production processes 用核函數(shù)法概率密度估計對間歇生產(chǎn)過程進行實時狀態(tài)監(jiān)測的主要優(yōu)點是它屬于非參數(shù)法概率密度估計的一種,不需要數(shù)據(jù)總體的任何先驗知識或是假設(shè)而直接基于實測數(shù)據(jù)樣本求出總體的概率分布密度函數(shù),擺脫了對不可靠的先驗知識的依賴。