Identifying the source of variance shifts in the multivariate process using ensemble classifiers

• Main Authors: Chih-Hung Lin, 林志鴻
• Other Authors: Chuen-Sheng Cheng
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/41200896371859749287

碩士 === 元智大學 === 工業工程與管理學系 === 99 === Multivariate control chart is used in the situation which simultaneous monitoring of two or more related quality characteristics. The generalized variance, |S|, control chart is usually applied to monitor process variability. Out-of-control signals in multivariate control charts may be caused by one or more variables. Although control chart is efficient in detecting a general multivariate shift in the variance, it fails to determine which variables are responsible for the variance shift. Many research papers address these problems and present various artificial intelligence approaches to identify aberrant variables. In the previous studies, only one classifier is applied in recognizing abnormal sources. However, the single model is limited to the same data or parameters setting and none of them could consistently perform well over all datasets. In this paper, we formulate the interpretation of out-of-control signal as a classification problem. The proposed system includes a SVM ensemble classifiers and a shift detector. When an out-of-control signal is generated, ensemble classifiers will determine which variable is responsible for the variance shift. Manipulating the training sample is usually used to create the diverse models. In the proposed approach, we base on some different statistical properties to construct ensemble and propose using extracted features as predictors to enhance the performance. The performance of the proposed system is evaluated by computing its classification accuracy. Results from studies indicate that the proposed approach is beneficial for identifying the source of variance changes.