On the Implementation of the Principal Component Analysis-Based Approach in Measuring Process Capability

被引：13
|
作者：
Perakis, M. [1 ]
Xekalaki, E. [2 ]
机构：
[1] Gen Accounting Off, Athens 10564, Greece
[2] Athens Univ Econ & Business, Dept Stat, Athens, Greece
来源：
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL | 2012年 / 28卷 / 04期
关键词：
process capability indices; multivariate processes; principal component analysis; INDEXES;
D O I：
10.1002/qre.1260
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The use of principal component analysis in measuring the capability of a multivariate process is an issue initially considered by Wang and Chen (1998). In this article, we extend their initial idea by proposing new indices that can be used in situations where the specification limits of the multivariate process are unilateral. Moreover, some new indices for multivariate processes are suggested. These indices have been developed so as to take into account the proportion of variance explained by each principal component, thus making the measurement of process capability more effective. Copyright (c) 2011 John Wiley & Sons, Ltd.
引用
下载
收藏
页码：467 / 480
页数：14
相关论文
共 50 条
  • [21] A kernel principal component analysis-based approach for determining the spatial warning domain of dam safety
    Su, Huaizhi
    Wen, Zhiping
    Ren, Jie
    SOFT COMPUTING, 2020, 24 (19) : 14921 - 14931
  • [22] Kernel principal component analysis-based Gaussian process regression modelling for high-dimensional reliability analysis
    Zhou, Tong
    Peng, Yongbo
    COMPUTERS & STRUCTURES, 2020, 241
  • [23] Robust Principal Component Analysis-Based Infrared Small Target Detection
    Chen, Qiwei
    Wu, Cheng
    Wang, Yiming
    THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, : 9925 - 9926
  • [24] Principal component analysis-based quantitative differential interference contrast microscopy
    Wei, Qi
    Li, Yangyang
    Vargas, Javier
    Wang, Jian
    Gong, Qingtao
    Kong, Yan
    Jiang, Zhilong
    Xue, Liang
    Liu, Cheng
    Liu, Fei
    Wang, Shouyu
    OPTICS LETTERS, 2019, 44 (01) : 45 - 48
  • [25] Principal component analysis-based control charts for multivariate nonnormal distributions
    Phaladiganon, Poovich
    Kim, Seoung Bum
    Chen, Victoria C. P.
    Jiang, Wei
    EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (08) : 3044 - 3054
  • [26] Principal Component Analysis-based Occupancy Detection with Ultra WideBand Radar
    Baird, Zach
    Gunasekara, Isuru
    Bolic, Miodrag
    Rajan, Sreeraman
    2017 IEEE 60TH INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS), 2017, : 1573 - 1576
  • [27] Modified Multivariate Process Capability Index Using Principal Component Analysis
    Zhang Min
    Wang, G. Alan
    He Shuguang
    He Zhen
    CHINESE JOURNAL OF MECHANICAL ENGINEERING, 2014, 27 (02) : 249 - 259
  • [28] Modified multivariate process capability index using principal component analysis
    Min Zhang
    G. Alan Wang
    Shuguang He
    Zhen He
    Chinese Journal of Mechanical Engineering, 2014, 27 : 249 - 259
  • [29] Modified Multivariate Process Capability Index Using Principal Component Analysis
    ZHANG Min
    WANG G Alan
    HE Shuguang
    HE Zhen
    Chinese Journal of Mechanical Engineering, 2014, 27 (02) : 249 - 259
  • [30] A Multivariate Process Capability Index Based on the First Principal Component Only
    Tano, Ingrid
    Vannman, Kerstin
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2013, 29 (07) : 987 - 1003
12345