Predicting configuration performance of modular product family using principal component analysis and support vector machine
来源期刊：中南大学学报(英文版)2014年第7期
论文作者：ZHANG Meng(张萌) LI Guo-xi(李国喜) GONG Jing-zhong(龚京忠) WU Bao-zhong(吴宝中)
文章页码：2701 - 2711
Key words：design configuration; performance prediction; modularity; principal component analysis; support vector machine
Abstract: A novel configuration performance prediction approach with combination of principal component analysis (PCA) and support vector machine (SVM) was proposed. This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments, which helps to evaluate whether or not the product variant can satisfy the customers’ individual requirements. The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance. Then, these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data. The performance values of a newly configured product can be predicted by means of the trained SVM models. This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately, even under the small sample conditions. The applicability of the proposed method was verified on a family of plate electrostatic precipitators.
ZHANG Meng(张萌), LI Guo-xi(李国喜), GONG Jing-zhong(龚京忠), WU Bao-zhong(吴宝中)
(College of Mechatronic Engineering and Automation,
National University of Defense Technology, Changsha 410073, China)
Abstract:A novel configuration performance prediction approach with combination of principal component analysis (PCA) and support vector machine (SVM) was proposed. This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments, which helps to evaluate whether or not the product variant can satisfy the customers’ individual requirements. The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance. Then, these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data. The performance values of a newly configured product can be predicted by means of the trained SVM models. This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately, even under the small sample conditions. The applicability of the proposed method was verified on a family of plate electrostatic precipitators.
Key words:design configuration; performance prediction; modularity; principal component analysis; support vector machine
