定制产品制造过程质量控制与诊断方法研究
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摘要
定制生产方式已经成为企业取得竞争优势的主要手段。个性化的需求对产品质量的要求更高。定制模式下产品数量少、甚至出现单件的产品,复杂的定制产品还具有关键质量特性维度高,质量影响因素关系复杂、波动溯源难度很高等特点。传统的统计质量控制方法在定制生产模式下面临着考验,需要研究面向定制生产的,适应小样本、缺少先验信息条件下的质量控制和诊断方法,以提升质量管理的能力。
本论文利用粗糙集和支持向量机理论,主要研究定制模式下小样本、高维、缺少先验信息条件下的控制图的特征提取方法和模式识别方法,以及辅助质量诊断的方法。具体研究内容包括:
提出基于粗糙集和Pawlak属性重要度的定制产品控制图关键统计特征提取方法,建立特征提取的决策模型。依据粗糙集理论,将控制图的统计特征看成是条件属性,将控制图的类别作为决策属性,利用正域概念来刻画条件属性的重要度,并根据条件属性重要度的大小来进行控制图特征的选取。
提出基于R-ν-SVC的模式分类模型,并建立分类规则。该模型将粗糙间隔的概念引入v支持向量机,训练分类超平面,以处理由离群点带来的过拟合问题。该模型通过最大化粗糙间隔的方式来寻找超平面,粗糙间隔被定义为上间隔和下间隔。训练样本在上下间隔中的位置决定了这些点是否能被正确的划分。对于二分类问题,将位于上超平面右侧的点定义为正类,位于下超平面左侧的点定义为负类。对于R-ν-SVC多分类问题,采用1-ν-1方法,结合粗糙集中的正域、负域和边界域的概念,定义一些等价关系类来解决多分类问题。通过比较可知该模型能有效地处理噪声数据和离群点带来的过拟合问题,较传统的方法更节省存储空间。
将R-ν-SVC模型应用到控制图异常模式分类中,实现对小样本模糊条件下控制图的异常模式进行识别,以估计异常状况的走向,及时进行在线控制。
研究基于粗糙集的定制产品制造过程在线质量诊断模型与方法。主要包括如何对制造过程进行知识表达,利用决策表,建立影响因素(条件属性)与产品质量特征(决策属性)的动态关系模型,计算因素对质量特征的重要度,当出现质量缺陷时,首先从重要度最高的因素来溯源,进行诊断。
Customization has b een identified as a competitive strategy by an increasing number of companies. High degree of customization also requires higher quality. The number of custom ized products is few, even single. Com plex customized products also have high dim ensional critical quali ty characteristics and com plex relationship among influence factors, and it is difficu lt to identif y the source of variation . Consequently, trad itional qua lity co ntrol m ethos would no t be eas ily adapted to customization environments. It is natural to expect that quality control and diagnosis issues should be taken into account when deciding upon product customization.
This dissertation introduces rough set (RS) theory into support vector m achine (SVM)--currently the b est m achine learning theory about sm all sa mple statistical learning. The purpose of the dissertation is to develop a feature extraction and pattern recognition approach based rough-S VM under few products with few transcendental information and high-dimensional feature. Specific research includes:
1 .An algorithm based rough set and Pawlak attribute significance is proposed to extract critical control chart statistic feature. A model is described as a decision table with condition attributes--raw statistic feature of control chart and decision attributes--classification of control chart. The critical feature is extracted according to the importance of condition attributes which is described by‘positive region’and Pawlak algorithm.
2. A pattern classification m odel and rules are provided by introducing the rough set theory into SVC(support vector classification). A rough margin basedν-SVC(R-ν-SVC)is proposed to train separating hyper -plane and deal with the overfitting problem due to outliers. The R-ν-SVC searches for the se parating hyper-plane that maximizes the rough margin which is defined by upper margin and lower margin. The position of training sample will dec ide if they can be classif ied correctly or not. For binary classifier, the rules can be defined that those samples lie in the right side of the upper hyper -plane are positiv e class and thos e lie in th e left side o f the lower hyper-plane are negative class. For R-ν-SVC multi-classifiers, the 1-ν-1 approach is used for pairwise classifications between negative class and positive class. We also define some equivalence cl asses according to the rough concept of po sitive region, negative region and b oundary reg ion. In add ition, we s how that th e R-ν-SVC approach may reduced storage requirements and is useful to deal with the outliers and noisy data compared with conventionalν-SVC.
The R-ν-SVC model also is used in contro l chart abnormal pattern re cognition under the conditions of the sm all sample and fuzzy information to estimate the trend of abnormal conditions and realize on-line quality control in time.
3. Finally, a m odel about custom izied products m anufacture quality diagnosis based rough-set is provided which desc ribes the m anufacture processing and establishes the dynamic relationship among influence factor(scondition attributes)and product quality characteristics(decision attributes)in the use of decision tables. By calculating the importance of influence factors,the source of variation is identified.
本论文利用粗糙集和支持向量机理论,主要研究定制模式下小样本、高维、缺少先验信息条件下的控制图的特征提取方法和模式识别方法,以及辅助质量诊断的方法。具体研究内容包括:
提出基于粗糙集和Pawlak属性重要度的定制产品控制图关键统计特征提取方法,建立特征提取的决策模型。依据粗糙集理论,将控制图的统计特征看成是条件属性,将控制图的类别作为决策属性,利用正域概念来刻画条件属性的重要度,并根据条件属性重要度的大小来进行控制图特征的选取。
提出基于R-ν-SVC的模式分类模型,并建立分类规则。该模型将粗糙间隔的概念引入v支持向量机,训练分类超平面,以处理由离群点带来的过拟合问题。该模型通过最大化粗糙间隔的方式来寻找超平面,粗糙间隔被定义为上间隔和下间隔。训练样本在上下间隔中的位置决定了这些点是否能被正确的划分。对于二分类问题,将位于上超平面右侧的点定义为正类,位于下超平面左侧的点定义为负类。对于R-ν-SVC多分类问题,采用1-ν-1方法,结合粗糙集中的正域、负域和边界域的概念,定义一些等价关系类来解决多分类问题。通过比较可知该模型能有效地处理噪声数据和离群点带来的过拟合问题,较传统的方法更节省存储空间。
将R-ν-SVC模型应用到控制图异常模式分类中,实现对小样本模糊条件下控制图的异常模式进行识别,以估计异常状况的走向,及时进行在线控制。
研究基于粗糙集的定制产品制造过程在线质量诊断模型与方法。主要包括如何对制造过程进行知识表达,利用决策表,建立影响因素(条件属性)与产品质量特征(决策属性)的动态关系模型,计算因素对质量特征的重要度,当出现质量缺陷时,首先从重要度最高的因素来溯源,进行诊断。
Customization has b een identified as a competitive strategy by an increasing number of companies. High degree of customization also requires higher quality. The number of custom ized products is few, even single. Com plex customized products also have high dim ensional critical quali ty characteristics and com plex relationship among influence factors, and it is difficu lt to identif y the source of variation . Consequently, trad itional qua lity co ntrol m ethos would no t be eas ily adapted to customization environments. It is natural to expect that quality control and diagnosis issues should be taken into account when deciding upon product customization.
This dissertation introduces rough set (RS) theory into support vector m achine (SVM)--currently the b est m achine learning theory about sm all sa mple statistical learning. The purpose of the dissertation is to develop a feature extraction and pattern recognition approach based rough-S VM under few products with few transcendental information and high-dimensional feature. Specific research includes:
1 .An algorithm based rough set and Pawlak attribute significance is proposed to extract critical control chart statistic feature. A model is described as a decision table with condition attributes--raw statistic feature of control chart and decision attributes--classification of control chart. The critical feature is extracted according to the importance of condition attributes which is described by‘positive region’and Pawlak algorithm.
2. A pattern classification m odel and rules are provided by introducing the rough set theory into SVC(support vector classification). A rough margin basedν-SVC(R-ν-SVC)is proposed to train separating hyper -plane and deal with the overfitting problem due to outliers. The R-ν-SVC searches for the se parating hyper-plane that maximizes the rough margin which is defined by upper margin and lower margin. The position of training sample will dec ide if they can be classif ied correctly or not. For binary classifier, the rules can be defined that those samples lie in the right side of the upper hyper -plane are positiv e class and thos e lie in th e left side o f the lower hyper-plane are negative class. For R-ν-SVC multi-classifiers, the 1-ν-1 approach is used for pairwise classifications between negative class and positive class. We also define some equivalence cl asses according to the rough concept of po sitive region, negative region and b oundary reg ion. In add ition, we s how that th e R-ν-SVC approach may reduced storage requirements and is useful to deal with the outliers and noisy data compared with conventionalν-SVC.
The R-ν-SVC model also is used in contro l chart abnormal pattern re cognition under the conditions of the sm all sample and fuzzy information to estimate the trend of abnormal conditions and realize on-line quality control in time.
3. Finally, a m odel about custom izied products m anufacture quality diagnosis based rough-set is provided which desc ribes the m anufacture processing and establishes the dynamic relationship among influence factor(scondition attributes)and product quality characteristics(decision attributes)in the use of decision tables. By calculating the importance of influence factors,the source of variation is identified.
引文
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