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

References

• Alwan , LC and Roberts , HV . 1988 . Time series modelling for statistical process control . J. Busi. Eco. Stat. , 6 : 87 – 95 .
   Web of Science ®Google Scholar
• Atienza , OO , Tang , LC and Ang , BW . 1998 . A SPC procedure for detecting level shifts of autocorrelated processes . J. Qual. Tech. , 30 : 340 – 351 .
   Google Scholar
• Box , GEP , Jenkins , GM and MacGregor , JF . 1974 . Some recent advances in forecasting and control, Part II . J. Royal Stat. Soc., Ser. C , 23 : 158 – 179 .
   Google Scholar
• Box , GEP , Jenkins , GM and Reinsel , GC . 1994 . Time Series Analysis: Forecasting and Control , 3rd , Englewood Cliffs, NJ : Prentice-Hall .
   Google Scholar
• Chang , SI and Aw , CA . 1996 . A neural fuzzy control chart for detecting and classifying process mean shifts . Int. J. Prod. Res. , 34 : 2265 – 2278 .
   Web of Science ®Google Scholar
• Cheng , CS . 1995 . A multi-layer neural network model for detecting changes in the process mean . Comput. Indust. Eng. , 28 : 51 – 61 .
   Web of Science ®Google Scholar
• Cheng , CS . 1997 . A neural network approach for the analysis of control chart patterns . Int. J. Prod. Res. , 35 : 667 – 697 .
   Web of Science ®Google Scholar
• Chiu , CC , Chen , MK and Lee , KM . 2001 . Shifts recognition in correlated process data using a neural network . Int. J. Syst. Sci. , 32 : 137 – 143 .
   Web of Science ®Google Scholar
• Cook , DF and Chiu , CC . Using radial basis function neural networks to recognise shifts in correlated manufacturing process parameters . IIE Trans. , 30 227 – 234 .
   Google Scholar
• Davis , RB and Woodall , WH . 1988 . Performance of the control chart trend rule under linear shift . J. Qual. Tech. , 20 : 260 – 262 .
   Web of Science ®Google Scholar
• Desieno , D . 1988 . “ Adding a conscience to competitive learning ” . In Proceedings of International Joint Conference on Neural Networks Vol. 1 , 117 – 124 . San Diego, CA
   Google Scholar
• Dieterle , F , Muller-Hagedorn , S , Liebich , HM and Gauglitz , G . 2003 . Urinary nucleosides as potential tumor markers evaluated by learning vector quantisation . Artif. Intel. Med. , 28 : 265 – 279 .
   PubMed Web of Science ®Google Scholar
• Duncan , AJ . 1986 . Quality Control and Industrial Statistics , 5th , Irwin : Illinois .
   Google Scholar
• Grant , EL and Leavenworth , RS . 1996 . Statistical Quality Control , 7th , New York, NY : McGraw-Hill .
   Google Scholar
• Guh , RS and Tannock , JDT . 1999 . Recognition of control chart concurrent patterns using a neural network approach . Int. J. Prod. Res. , 37 : 1743 – 1765 .
   Web of Science ®Google Scholar
• Guh , RS . 2002 . Robustness of the neural network based control chart pattern recognition system to non-normality . Int. J. Qual. Rel. Manage. , 19 : 97 – 112 .
   Google Scholar
• Guh , RS . 2004 . Optimising feedforward neural networks for control chart pattern recognition through genetic algorithms . Int. J. Pattern Recog. Artif. Intel. , 18 : 75 – 99 .
   Web of Science ®Google Scholar
• Guh , RS . 2005 . A hybrid learning-based model for on-line detection and analysis of control chart patterns . Comput. Indust. Eng. , 49 : 35 – 62 .
   Web of Science ®Google Scholar
• Harris , TJ and Ross , WH . 1991 . Statistical process control procedures for correlated observations . Can. J. Chem. Eng. , 69 : 48 – 57 .
   Web of Science ®Google Scholar
• Ho , ES and Chang , SI . 1999 . An integrated neural network approach for simultaneous monitoring of process mean and variance shifts—a comparative study . Int. J. Prod. Res. , 37 : 1881 – 1901 .
   Web of Science ®Google Scholar
• Hornik , KM , Stinchcombe , M and White , H . 1989 . Multiple feedforward networks are universal approximations . Neural Compu. , 2 : 210 – 215 .
   Google Scholar
• Hwarng , HB and Hubele , NF . 1993a . Back-propagation pattern recognisers for X-bar control charts: methodology and performance . Comput. Indust. Eng. , 24 : 219 – 235 .
   Web of Science ®Google Scholar
• Hwarng , HB and Hubele , NF . 1993b . X-bar control chart pattern identification through efficient off-line neural network training . IIE Trans. , 25 : 27 – 40 .
   Web of Science ®Google Scholar
• Hwarng , HB and Chong , CW . 1995 . Detecting process non-randomness through a fast and cumulative learning ART-based pattern recogniser . Int. J. Prod. Res. , 33 : 1817 – 1833 .
   Web of Science ®Google Scholar
• Hwarng , HB . 1997 . A neural network approach to identifying cyclic behaviour on control charts: a comparative study . Int. J. Syst. Sci. , 28 : 99 – 112 .
   Google Scholar
• Hwarng , HB . 2004 . Detecting process mean shift in the presence of autocorrelation: A neural-network based monitoring scheme . Int. J. Prod. Res. , 42 : 573 – 595 .
   Web of Science ®Google Scholar
• Hwarng , HB . 2005 . Simultaneous identification of mean shift and correlation change in AR(1) processes . Int. J. Prod. Res. , 43 : 1761 – 1783 .
   Web of Science ®Google Scholar
• Kohonen , T . 1988 . An introduction to neural computing . Neural Networks , 1 : 3 – 16 .
   Web of Science ®Google Scholar
• Kohonen , T , Barna , G and Chrisley , R . 1988 . “ Statistical pattern recognition with neural networks: Benchmarking studies ” . In Proceedings of International Joint Conference on Neural Networks Vol. 1 , 61 – 68 . San Diego, CA
   Google Scholar
• Kohonen , T . 1995 . Self Organisation Maps , Heidelberg : Springer-Verlag .
   Google Scholar
• Law , AM and Kelton , WD . 1982 . Simulation Modelling and Analysis , New York, NY : McGraw-Hill .
   Google Scholar
• Lucy-Bouler , TL . 1991 . “ Using autocorrelations, CUSUMs and runs rules for control chart pattern recognition: an expert system approach ” . In PhD dissertation , USA : University of Alabama .
   Google Scholar
• Lucy-Bouler , TL . 1993 . “ Application to forecasting of neural network recognition of shifts and trends in quality control data ” . In Proceedings of WCNN’93— World Congress on Neural Networks Vol. 1 , 631 – 633 . Portland, , UK
   Google Scholar
• Maragah , HO and Woodall , WH . 1992 . The effects of autocorrelation on the retrospective X-chart . J. Statist. Compu. Simul. , 40 : 29 – 42 .
   Google Scholar
• Minitab Inc. 2003 . Meet MINITAB Release , Vol. 14 , Minitab, PA : State College .
   Google Scholar
• Montgomery , DC . 2001 . Introduction to Statistical Quality Control , 4th , New York, NY : John Wiley & Sons .
   Google Scholar
• Nelson , LS . 1984 . The Shewhart control chart-tests for special causes . J. Qual. Tech. , 16 : 237 – 239 .
   Web of Science ®Google Scholar
• Nelson , LS . 1985 . Interpreting Shewhart X-bar control charts . J. Qual. Tech. , 17 : 114 – 116 .
   Web of Science ®Google Scholar
• NeuralWare Inc. 2001 . NeuralWorks Professional II Plus Reference Guide , Pittsburgh, PA : NeuralWare .
   Google Scholar
• Noorossana , R , Farrokhi , M and Saghaei , A . 2003 . Using neural networks to detect and classify out-of-control signals in autocorrelated processes . Qual. Reliab. Eng. Int. , 19 : 493 – 504 .
   Web of Science ®Google Scholar
• Pandit , SM and Wu , S . 1983 . Time Series and System Analysis with Applications , New York, NY : John Wiley and Sons .
   Google Scholar
• Runger , GC , Willemain , TR and Prabhu , S . 1995 . Average run lengths for CUSUM control charts applied to residuals . Comm. in Stat.—Theory and Methods , 24 : 273 – 282 .
   Web of Science ®Google Scholar
• Shaffer , RE , Rose-Pehrsson , SL and McGill , RA . 1999 . A comparison study of chemical sensor array pattern recognition algorithms . Analytica Chimica Acta , 384 : 305 – 317 .
   Web of Science ®Google Scholar
• Swift , JA and Mise , JH . 1995 . Out-of-control pattern recognition and analysis for quality control charts using Lisp-based system . Comput. Indust. Eng. , 28 : 81 – 91 .
   Web of Science ®Google Scholar
• Vasilopoulos , AV and Stamboulis , AP . 1978 . Modification of control chart limits in the presence of data correlation . J. Qual. Tech. , 10 : 20 – 30 .
   Web of Science ®Google Scholar
• Wang , TY and Chen , LH . 2002 . Mean shifts detection and classification in multivariate process: a neural-fuzzy approach . J. Intel. Manuf. , 13 : 211 – 221 .
   Web of Science ®Google Scholar
• Wardell , DG , Moskowitz , H and Plante , RD . 1994 . Run-length distributions of special-cause control charts for correlated processes . Technometrics , 36 : 3 – 17 .
   Web of Science ®Google Scholar
• Western Electric Company . 1958 . Statistical Quality Control Handbook , Indianapolis, IN : Western Electric .
   Google Scholar
• Zhang , ZP , Chen , HG , Ye , SW and Zhao , JW . 1996 . Comparison of the BP training algorithm and LVQ neural networks for e, µ, π identification . Nuclear Instru. Meth. Phys. Res. A , 379 : 271 – 275 .
   Web of Science ®Google Scholar