Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach

Researchers have been investigating the use of artificial neural networks (NNs) in the application of control chart pattern (CCP) recognition with encouraging results in recent years. Most of the NN models in this field are designed to be used in uncorrelated processes where the process data are independent. Unfortunately, the prerequisite of data independence is not even approximately satisfied in many manufacturing processes. To the best of the author's knowledge, no research results have been published to date on the application of NNs for CCP recognition in autocorrelated processes. This work first shows that autocorrelation in process data greatly affects the performance of NN-based CCP recognizers developed with independent data and then presents a learning vector quantization network-based system that can effectively recognize CCPs in real-time for processes with various levels of autocorrelation. The system performance is evaluated in terms of the classification rate and the average run length. An empirical comparison using simulation indicates that the proposed learning-based system performs better than the traditional control chart methods in detecting shifts when the process data are positively correlated, while it also offers pattern classification. A demonstration example is provided using real data.

[1]  Ruey-Shiang Guh.  Optimizing Feedforward Neural Networks For Control Chart Pattern Recognition Through Genetic Algorithms , 2004, Int. J. Pattern Recognit. Artif. Intell..

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

[3]  S. Rose-Pehrsson,et al.  A comparison study of chemical sensor array pattern recognition algorithms , 1999 .

[4]  William H. Woodall,et al.  Performance of the Control Chart Trend Rule under Linear Shift , 1988 .

[5]  Chih-Chou Chiu,et al.  Shifts recognition in correlated process data using a neural network , 2001, Int. J. Syst. Sci..

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

[7]  George C. Runger,et al.  Average run lengths for cusum control charts applied to residuals , 1995 .

[8]  Shien-Ming Wu,et al.  Time series and system analysis with applications , 1983 .

[9]  William H. Woodall,et al.  The effect of autocorrelation on the retrospective X-chart , 1992 .

[10]  N F Hubele,et al.  X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING , 1993 .

[11]  Ruey-Shiang Guh,et al.  A hybrid learning-based model for on-line detection and analysis of control chart patterns , 2005, Comput. Ind. Eng..

[12]  G. B. Wetherill,et al.  Quality Control and Industrial Statistics , 1975 .

[13]  Chuen-Sheng Cheng.  A multi-layer neural network model for detecting changes in the process mean , 1995 .

[14]  Ruey‐Shiang Guh.  Robustness of the neural network based control chart pattern recognition system to non‐normality , 2002 .

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

[16]  Frank Dieterle,et al.  Urinary nucleosides as potential tumor markers evaluated by learning vector quantization , 2003, Artif. Intell. Medicine.

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

[18]  Rassoul Noorossana,et al.  Using Neural Networks to Detect and Classify Out‐of‐control Signals in Autocorrelated Processes , 2003 .

[19]  George E. P. Box,et al.  Some Recent Advances in Forecasting and Control. Part II. , 1972 .

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

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

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

[23]  John F. MacGregor,et al.  Some Recent Advances in Forecasting and Control , 1968 .

[24]  Tai-Yue Wang,et al.  Mean shifts detection and classification in multivariate process: a neural-fuzzy approach , 2002, J. Intell. Manuf..

[25]  H. Brian Hwarng.  Detecting process mean shift in the presence of autocorrelation: a neural-network based monitoring scheme , 2004 .

[26]  Thomas Lee Lucy-Bouler.  Using autocorrelations, cusums and runs rules for control chart pattern recognition: an expert system approach , 1991 .

[27]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[28]  T. Harris,et al.  Statistical process control procedures for correlated observations , 1991 .

[29]  H. B. Hwarng,et al.  Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer , 1995 .

[30]  Herbert Moskowitz,et al.  Run-Length Distributions of Special-Cause Control Charts for Correlated Processes , 1994 .

[31]  Lloyd S. Nelson.  Column: Technical Aids: Interpreting Shewhart X-bar Control Charts , 1985 .

[32]  Shing I. Chang,et al.  An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study , 1999 .

[33]  Z. P. Zhang,et al.  Comparison of the BP training algorithm and LVQ neural networks for e, μ, π identification , 1996 .

[34]  H. B. Hwarng *,et al.  Simultaneous identification of mean shift and correlation change in AR(1) processes , 2005 .

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

[36]  A. Vasilopoulos,et al.  Modification of Control Chart Limits in the Presence of Data Correlation , 1978 .

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

[38]  H. Brian Hwarng,et al.  A neural network approach to identifying cyclic behaviour on control charts: a comparative study , 1997, Int. J. Syst. Sci..

[39]  Shing I. Chang,et al.  A neural fuzzy control chart for detecting and classifying process mean shifts , 1996 .

[40]  B. W. Ang,et al.  A SPC Procedure for Detecting Level Shifts of Autocorrelated Processes , 1998 .

[41]  Duane DeSieno,et al.  Adding a conscience to competitive learning , 1988, IEEE 1988 International Conference on Neural Networks.

[42]  T. Kohonen,et al.  Statistical pattern recognition with neural networks: benchmarking studies , 1988, IEEE 1988 International Conference on Neural Networks.

[43]  Averill M. Law,et al.  Simulation modelling and analysis , 1991 .

[44]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.