工艺产品加工质量监测优化

发布时间：2018-02-03 15:03

本文关键词： 质量监测质量特性主元分析(PCA) 支持向量机(SVM) 改进网格优化　出处：《科学技术与工程》2017年18期 　论文类型：期刊论文

【摘要】：质量监测可以有效地提高产品质量和生产效率。在复杂产品的生产过程当中,多个质量特性之间相互作用,共同对产品的生产质量产生影响。由于质量特性的数量较多、有些特性的关系是耦合的,因此准确诊断出异常变量是研究的难点。为了高效、准确地诊断出异常变量,提高产品的质量和生产效率,提出了基于改进网格优化的principal component analysis(PCA)-support vector machines(SVM)多元控制图均值偏移诊断模型。在模型训练之前,使用主元分析(PCA)算法对数据进行预处理,降低数据维数和提取数据特征信息;再用改进网格算法对支持向量机(SVM)的参数进行优化,最终得到优化的SVM模型。仿真结果表明,采用的方法与传统方法相比,训练时间更短,且拥有更高的分类准确率。
[Abstract]:Quality monitoring can effectively improve product quality and production efficiency. In the production process of complex products, multiple quality characteristics interact with each other. Due to the large quantity of the quality characteristics and the coupling of some characteristics, it is difficult to accurately diagnose the abnormal variables in order to be efficient. Accurate diagnosis of abnormal variables to improve product quality and production efficiency. Principal component analysis(PCA)-support vector machine based on improved grid optimization is proposed. SVM) multiple control chart mean shift diagnostic model. Before model training. The principal component analysis (PCA) algorithm is used to preprocess the data to reduce the dimension of the data and extract the characteristic information of the data. Finally, the optimized SVM model is obtained by using the improved mesh algorithm. The simulation results show that the training time of the proposed method is shorter than that of the traditional method. And has higher classification accuracy.
【作者单位】：中国科学院沈阳自动化研究所;中国科学院大学;
【分类号】：TB114.2;TP181
【正文快照】： 引用格式:蔡亚军,陈书宏,王宇.工艺产品加工质量监测优化[J].科学技术与工程,2017,17(18):277—281Cai Yajun,Cheng Shuhong,Wang Yu.Optimization of process qualitymonitoring[J].Science Technology and Engineering,2017,17(18):277—281工艺的复杂化和顾客对产品质量要

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