MES环境下基于数据挖掘技术的质量预测与诊断系统研究

发布时间：2018-03-25 10:31

本文选题：决策树　切入点：聚类分析　出处：《山东大学》2014年硕士论文

【摘要】：产品质量是对顾客需求的具体反映,也是顾客满意的必要因素。为了能在竞争激烈的市场当中占据有利地位,提高企业竞争力,现代企业必须加强质量管理。随着科学技术的不断发展与市场的日趋成熟,市场对质量的要求不断提高,现有质量管理技术与工具已不能满足需求。制造执行系统(Manufacturing Execution System,MES)环境下生产过程中的质量数据是由车间作业现场控制收集,系统与工程师进行分析处理并储存在指定的数据库或数据仓库中。其数据内容包含加工记录、实时监控信息等,这些数据的累积导致分析人员对数据的处理能力下降,其潜在价值没有被进一步挖掘而产生信息的浪费。为了充分利用生产过程中收集的大量数据,实现全面质量管理,应用数据挖掘技术(Data Mining,DM)实现智能化加工过程质量诊断控制的技术和系统成为众多专家学者及企业新的研究热点。本文在总结前人研究基础之上构建MES环境下制造过程质量数据挖掘平台,分析加工质量相关数据,研究具有控制图模式识别、质量预测及诊断功能的质量控制系统,主要研究内容包括以下三方面： (1)针对制造过程数据特点,提出一种适用于过程质量数据分析的不纯性度量：Fβ度量类置信度比Fβ-Confidence Proportion,FCP),并建立基于FCP不纯性度量的决策树(Decision Tree, DT)。在构建分类器的基础上,构建基于FCP决策树的控制图模式识别系统,针对控制图数据维度较高的现象引用统计量作为控制图模式识别的统计特征。经测试表明该模式识别系统精度高,处理速度快,符合质量预警控制的需求。 (2)提出基于FCP决策树与聚类分析(Cluster Analysis,CA)的质量预测方法。同时应用两种数据挖掘算法分析不同种控制图模式下的制造过程质量数据,在获取聚类信息及异常质量归类的基础上研究工序质量的异常预测。通过质量影响因素的聚类分析,获取不同模式下的质量影响因素组合,利用决策树分析现有过程质量数据实现质量预测。 (3)将基于案例推理(Case-Based Reasoning, CBR)的知识库引入质量诊断系统,实现了诊断知识的管理与自学习功能。通过对数控加工中心底座铣削制造过程实例中信息数据的处理,展示了知识提取与质量诊断的全过程；最终建立MES环境下数控加工中心质量预测诊断系统,建立质量预测数据库、基于案例推理的知识库及各子系统模块的人机交互界面,实现了控制图模式识别、质量预测与诊断、知识库的自学习与维护功能。
[Abstract]:Product quality is a concrete reflection of customer demand and a necessary factor for customer satisfaction. In order to occupy a favorable position in a highly competitive market and improve the competitiveness of enterprises, Modern enterprises must strengthen quality management. With the development of science and technology and the maturation of the market, the market demand for quality has been improved constantly, the existing quality management technology and tools can not meet the demand. The quality data in production process in manufacturing Execution system mes environment is collected by workshop job field control, analyzed and processed by system and engineer and stored in a specified database or data warehouse. In order to make full use of the large amount of data collected in the production process, the accumulation of such data leads to a decline in the ability of the analyst to process the data, and the potential value of the data is not further mined, resulting in a waste of information. Achieve total quality management, The technology and system of intelligent manufacturing process quality diagnosis and control based on data mining technology (DM) has become a new research hotspot for many experts and enterprises. Based on the summary of previous researches, this paper constructs manufacturing under MES environment. Process quality data mining platform, The quality control system with the function of pattern recognition, quality prediction and diagnosis of control chart is studied by analyzing the relevant data of processing quality. The main research contents include the following three aspects:. 1) according to the characteristics of manufacturing process data, a kind of uncertainty metric: F 尾 -confidence ratio F 尾 is proposed for process quality data analysis, and a decision tree based on FCP impureness metric is established. The control chart pattern recognition system based on FCP decision tree is constructed, and the statistical quantity is used as the statistical feature of the control chart pattern recognition for the phenomenon with high dimension of the control chart data. The test results show that the pattern recognition system has high precision and fast processing speed. In line with the quality of early warning control requirements. (2) A quality prediction method based on FCP decision tree and cluster analysis is proposed, and two kinds of data mining algorithms are used to analyze the quality data of manufacturing process under different control chart models. On the basis of obtaining clustering information and classifying abnormal quality, the abnormal prediction of process quality is studied. Through clustering analysis of quality influencing factors, the combination of quality influencing factors under different models is obtained. Using decision tree to analyze the existing process quality data to achieve quality prediction. The knowledge base based on Case-Based reasoning (CBR) is introduced into the quality diagnosis system, and the management and self-learning function of diagnosis knowledge is realized. The whole process of knowledge extraction and quality diagnosis is demonstrated. Finally, the quality prediction and diagnosis system of NC machining center under MES environment, the quality prediction database, the knowledge base based on case-based reasoning and the man-machine interface of each subsystem module are established. The functions of pattern recognition, quality prediction and diagnosis, self learning and maintenance of knowledge base are realized.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP311.13;TB114.2

【参考文献】