One-class classification-based control charts for multivariate process monitoring

One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, attempts are made to extend the scope of application of the one-class classification technique to Statistical Process Control (SPC) problems. New multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II analysis in SPC are proposed. These charts use a monitoring statistic to represent the degree of being an outlier as obtained through one-class classification. The control limits of the proposed charts are established based on the empirical level of significance on the percentile, estimated by the bootstrap method. A simulation study is conducted to illustrate the limitations of current one-class classification control charts and demonstrate the effectiveness of the proposed control charts.

[1]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[2]  Zhisheng Zhang,et al.  SVC-Based Multivariate Control Charts for Automatic Anomaly Detection in Computer Networks , 2007, Third International Conference on Autonomic and Autonomous Systems (ICAS'07).

[3]  Peihua Qiu,et al.  Distribution-free multivariate process control based on log-linear modeling , 2008 .

[4]  Douglas C. Montgomery,et al.  Research Issues and Ideas in Statistical Process Control , 1999 .

[5]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[6]  S. Bakir.  Distribution-Free Quality Control Charts Based on Signed-Rank-Like Statistics , 2006 .

[7]  York Marcel Dekker.  The State of Statistical Process Control as We Proceed into the 21st Century , 2000 .

[8]  James R. Wilson,et al.  A distribution-free tabular CUSUM chart for autocorrelated data , 2007 .

[9]  S. Chakraborti,et al.  Nonparametric Control Charts: An Overview and Some Results , 2001 .

[10]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[11]  Manoj Kumar Tiwari,et al.  Kernel distance-based robust support vector methods and its application in developing a robust K-chart , 2006 .

[12]  William H. Woodall,et al.  Controversies and Contradictions in Statistical Process Control , 2000 .

[13]  Ratna Babu Chinnam,et al.  Support vector machines for recognizing shifts in correlated and other manufacturing processes , 2002 .

[14]  William H. Woodall,et al.  Introduction to Statistical Quality Control, Fifth Edition , 2005 .

[15]  H. Hotelling,et al.  Multivariate Quality Control , 1947 .

[16]  Rick L. Edgeman,et al.  Multivariate Statistical Process Control with Industrial Applications , 2004, Technometrics.

[17]  George C. Runger,et al.  Tuned artificial contrasts to detect signals , 2007 .

[18]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[19]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[20]  Alice E. Smith,et al.  X-bar and R control chart interpretation using neural computing , 1994 .

[21]  George C. Runger,et al.  Multivariate statistical process control with artificial contrasts , 2007 .

[22]  Robert P.W. Duin,et al.  PRTools3: A Matlab Toolbox for Pattern Recognition , 2000 .

[23]  William H. Woodall,et al.  The State of Statistical Process Control as We Proceed into the 21st Century , 2000 .

[24]  Chih-Chou Chiu,et al.  Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters , 1998 .

[25]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[26]  Chandan Srivastava,et al.  Support Vector Data Description , 2011 .

[27]  Regina Y. Liu,et al.  DDMA-charts: Nonparametric multivariate moving average control charts based on data depth , 2004 .

[28]  Susan L. Albin,et al.  Determining the number of operational modes in baseline multivariate SPC data , 2007 .

[29]  Fugee Tsung,et al.  A kernel-distance-based multivariate control chart using support vector methods , 2003 .

[30]  Douglas C. Montgomery,et al.  A review of multivariate control charts , 1995 .