本文關鍵詞： 過程方差 合成S~2圖 已知參數(shù) 未知參數(shù)　出處：《浙江工商大學》2015年碩士論文　論文類型：學位論文

【摘要】：在生產過程中,有些生產因素如劣質的原材料或者工人操作不熟練并不會引起過程均值發(fā)生變化,但卻會引起過程方差的變化,過程方差變大說明產品的質量不穩(wěn)定,過程方差減小說明產品的質量提高。另外過程方差失控會使均值控制圖的表現(xiàn)不同于預期,因此監(jiān)測過程方差的變化是十分重要的。傳統(tǒng)的休哈特S2圖常被用于過程方差的監(jiān)控,但存在一定缺陷,具體表現(xiàn)為對過程方差大漂移比較敏感,而對過程方差中小漂移不敏感。本文引入合成圖的方法,將休哈特S2圖與合格品鏈長圖結合組成合成S2圖來對方差進行監(jiān)控,以改進休哈特s2圖的表現(xiàn),提高控制圖監(jiān)測過程方差變化的能力。本文主要研究內容如下：(1)本文將合成圖的方法分別應用于對過程方差單、雙側漂移的監(jiān)測。基于參數(shù)已知情況,設計了上單側合成S2圖、下單側合成s2圖以及雙側合成s2圖分別應用于監(jiān)測正態(tài)總體假定下過程方差變大、過程方差變小以及過程方差漂移方向不確定等三種情況,并與相應的休哈特s2圖比較。結果顯示本文設計的單、雙側合成s2圖的監(jiān)測效果均優(yōu)于休哈特S2圖。(2)控制圖的設計通常假設受控參數(shù)己知,而實際應用中受控參數(shù)常常未知并需要由已獲受控樣本進行估計。本文分析了參數(shù)估計對三種合成s2圖的影響,發(fā)現(xiàn)參數(shù)估計會增加偽警概率,大大降低合成s2圖的監(jiān)控能力,特別是在過程方差漂移量較小和用于估計受控參數(shù)的第1階段樣本組數(shù)較小之時。(3)因為參數(shù)估計大大影響三種合成S2圖的表現(xiàn),所以基于己知參數(shù)的控制圖不再合適,因此本文基于估計參數(shù)設計了新的單、雙側合成s2圖對過程方差進行監(jiān)控,并與相應的的休哈特s2圖比較,結果顯示在參數(shù)未知時,新設計的單、雙側合成s2圖的監(jiān)測效果優(yōu)于相應的單、雙側休哈特s2圖。
[Abstract]:In the process of production, some production factors, such as inferior raw materials or unskilled operation of workers, will not cause changes in the mean value of the process, but will cause changes in the variance of the process. The increase of process variance indicates that the quality of the product is unstable, and the decrease of the process variance indicates the improvement of the quality of the product. In addition, the runaway process variance will make the performance of the mean control chart different from the expected. Therefore, it is very important to monitor the variation of process variance. The traditional Shewhart S2 diagram is often used to monitor the process variance, but it has some defects, which shows that it is sensitive to the large drift of process variance. However, it is not sensitive to the small drift of process variance. In this paper, the method of composite graph is introduced, which combines Shewhart S2 diagram with qualified product chain length graph to monitor the variance to improve the performance of Heinhart S2 diagram. The main contents of this paper are as follows: 1) in this paper, the method of composite graph is applied to the monitoring of single and bilateral drift of process variance, based on the known parameters. The S 2 map of upper side synthesis, the second side S 2 and the double side S 2 are designed to monitor the normal population under the assumption that the process variance becomes larger. The process variance becomes smaller and the direction of process variance drift is uncertain. The results are compared with the corresponding Hewhart S2 diagram. The results show that the design of this paper is single. The monitoring effect of bilateral s2 diagram is better than that of Hewhart S2 chart. 2) the design of the control chart usually assumes that the controlled parameters are known. In practical application, the controlled parameters are often unknown and need to be estimated by controlled samples. In this paper, the effect of parameter estimation on three kinds of composite S2 graphs is analyzed, and it is found that parameter estimation will increase false alarm probability. Greatly reduces the monitoring ability of the composite S2 graph. Especially when the variance drift of the process is small and the number of samples used in the first stage to estimate controlled parameters is small) the parameter estimation greatly affects the performance of the three kinds of composite S2 graphs. Therefore, the control chart based on known parameters is no longer suitable, so this paper designs a new single- and double-side composite S2 graph to monitor the process variance based on the estimated parameters, and compares it with the corresponding Shewhart S2 graph. The results show that when the parameters are unknown, the monitoring effect of the newly designed single and bilateral composite S2 charts is better than that of the corresponding single and bilateral Heinhart S2 diagrams.
【學位授予單位】：浙江工商大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：F273.2;F224

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本文編號：1442329