Feature-based recognition of control chart patterns

Control charts primarily in the form of [email protected]? chart are widely used to identify the situations when control actions will be needed for manufacturing systems. Various types of patterns are observed in control charts. Identification of these control chart patterns (CCPs) can provide clues to potential quality problems in the manufacturing process. Each type of control chart pattern has its own geometric shape and various related features can represent this shape. Feature-based approaches can facilitate efficient pattern recognition since extracted shape features represent the main characteristics of the patterns in a condensed form. In this paper, a set of eight new features, extraction of which does not call for utilizing the experience and skill of the user in any form, is presented. Two feature-based approaches using heuristics and artificial neural network (ANN) are developed, which are capable of recognizing eight most commonly observed CCPs including stratification and systematic patterns. Relative performances of the feature-based heuristic and feature-based ANN recognizers are extensively studied using synthetic pattern data. The feature-based ANN recognizer results in better recognition performance and generalization compared to the feature-based heuristic recognizer.

[1]  Miin-Shen Yang,et al.  A control chart pattern recognition system using a statistical correlation coefficient method , 2005, Comput. Ind. Eng..

[2]  Yeou-Ren Shiue,et al.  On-line identification of control chart patterns using self-organizing approaches , 2005 .

[3]  J. Morgan,et al.  Thaid a Sequential Analysis Program for the Analysis of Nominal Scale Dependent Variables , 1973 .

[4]  Miin-Shen Yang,et al.  A fuzzy-soft learning vector quantization for control chart pattern recognition , 2002 .

[5]  Seref Sagiroglu,et al.  Training multilayered perceptrons for pattern recognition: a comparative study of four training algorithms , 2001 .

[6]  W. Loh,et al.  Tree-Structured Classification via Generalized Discriminant Analysis. , 1988 .

[7]  W. Loh,et al.  SPLIT SELECTION METHODS FOR CLASSIFICATION TREES , 1997 .

[8]  R Guh.  IntelliSPC: a hybrid intelligent tool for on-line economical statistical process control , 1999 .

[9]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[10]  Norma Faris Hubele,et al.  Back-propagation pattern recognizers for X¯ control charts: methodology and performance , 1993 .

[11]  Jill A. Swift,et al.  Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems , 1995 .

[12]  James R. Evans,et al.  A framework for expert system development in statistical quality control , 1988 .

[13]  Adnan Hassan,et al.  Improved SPC chart pattern recognition using statistical features , 2003 .

[14]  Roger M. Sauter,et al.  Introduction to Statistical Quality Control (2nd ed.) , 1992 .

[15]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[16]  Duc Truong Pham,et al.  Control chart pattern recognition using learning vector quantization networks , 1994 .

[17]  Yousef Al-Assaf,et al.  Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks , 2004, Comput. Ind. Eng..

[18]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[19]  Duc Truong Pham,et al.  Feature-based control chart pattern recognition , 1997 .

[20]  J.D.T. Tannock,et al.  Recognition of control chart concurrent patterns using a neural network approach , 1999 .

[21]  Tomas Velasco,et al.  Back propagation artificial neural networks for the analysis of quality control charts , 1993 .

[22]  Marcus B. Perry,et al.  Control chart pattern recognition using back propagation artificial neural networks , 2001 .

[23]  Lloyd S. Nelson,et al.  Column: Technical Aids: The Shewhart Control Chart--Tests for Special Causes , 1984 .

[24]  J. D. T. Tannock,et al.  On-line control chart pattern detection and discrimination - a neural network approach , 1999, Artif. Intell. Eng..

[25]  Lloyd S. Nelson,et al.  Interpreting Shewhart X̄ Control Charts , 1985 .

[26]  Eugene L. Grant,et al.  Statistical Quality Control , 1946 .

[27]  Duc Truong Pham,et al.  XPC: an on-line expert system for statistical process control , 1992 .

[28]  A. K. Kochhar,et al.  Pattern recognition for statistical process control charts , 1998 .

[29]  Chuen-Sheng Cheng,et al.  A NEURAL NETWORK APPROACH FOR THE ANALYSIS OF CONTROL CHART PATTERNS , 1997 .

[30]  Ercan Oztemel,et al.  Control chart pattern recognition using neural networks , 1992 .

[31]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[32]  Khaled Assaleh,et al.  Features extraction and analysis for classifying causable patterns in control charts , 2005, Comput. Ind. Eng..