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首页> 外文期刊>International Journal of Production Research >Control chart pattern recognition using an integrated model based on binary-tree support vector machine
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Control chart pattern recognition using an integrated model based on binary-tree support vector machine

机译：基于二叉树支持向量机的集成模型控制图模式识别

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摘要

Control charts are important tools in statistical process control for determining whether a process is in control. Furthermore, effective recognition of abnormal control chart pattern (CCP) can greatly narrow the set of possible assignable causes, significantly shortening the diagnostic process. In this article, an integrated model in which binary-tree support vector machine (BTSVM) is applied for abnormal CCP recognition is proposed. The integrated model consists of three stages. In the first stage, five statistical features and eight shape features are extracted. In the second stage, a binary-class support vector machine is used to detect abnormal CCPs. In the third stage, the BTSVM is applied to classify the detected abnormal CCPs. Additionally, the Fisher Ratio method is utilised at each node of the binary tree to design the architecture of the BTSVM. Simulation experimental results show that the proposed model is able to effectively identify the type of CCPs and that the classification accuracies of the second and the third stages are up to 100% and 98.5%, respectively. A series of contrast experiments prove that the classification accuracy of BTSVM outperforms that of one against one and one against rest for abnormal CCP classification.
机译：控制图是统计过程控制中用于确定过程是否处于受控状态的重要工具。此外，对异常控制图模式（CCP）的有效识别可以大大缩小可能的可分配原因的范围，从而大大缩短诊断过程。本文提出了一种将二叉树支持向量机（BTSVM）用于CCP异常识别的集成模型。集成模型包括三个阶段。在第一阶段，提取五个统计特征和八个形状特征。第二阶段，使用二进制支持向量机检测异常CCP。在第三阶段，将BTSVM应用于对检测到的异常CCP进行分类。另外，在二叉树的每个节点处使用Fisher比率方法来设计BTSVM的体系结构。仿真实验结果表明，该模型能够有效识别CCP的类型，第二，第三阶段的分类准确率分别达到100％和98.5％。一系列对比实验证明，对于异常CCP分类，BTSVM的分类精度优于一对一的分类精度和一对静止的分类精度。

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