Decision tree based control chart pattern recognition

References

• Al-Ghanim , AM and Ludeman , LC . 1997 . Automated unnatural pattern recognition on control charts using correlation analysis techniques . Comput. Ind. Eng. , 32 : 679 – 690 .
   Web of Science ®Google Scholar
• Chang , SI and Ho , ES . 1999 . A two-stage neural network for process variance change detection and classification . Int. J. Prod. Res. , 37 : 1581 – 1599 .
   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
• Cook , DF and Chiu , CC . 1998 . Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters . IIE trans. , 30 : 227 – 234 .
   Web of Science ®Google Scholar
• Ganesan , R , Das , TK and Venkataraman , V . 2004 . Wavelet-based multiscale statistical process monitoring: A literature review . IIE trans. , 36 : 787 – 806 .
   Web of Science ®Google Scholar
• Gauri , SK and Chakraborty , S . 2006 . A study on the various features for effective control chart pattern recognition . Int. J. Adv. Manuf. Techn. , [In press]
   Web of Science ®Google Scholar
• Guh , RS and Hsieh , YC . 1999 . A neural network based model for abnormal pattern recognition of control charts . Comput. Ind. Eng. , 36 : 97 – 108 .
   Web of Science ®Google Scholar
• Guh , RS and Tannock , JDT . 1999 . A neural network approach to characterize pattern parameters in process control charts . J. Intell. Manuf. , 10 : 449 – 462 .
   Web of Science ®Google Scholar
• Guh , RS and Shiue , YR . 2005 . On-line identification of control chart patterns using self-organized approaches . Int. J. Prod. Res. , 43 : 1225 – 1254 .
   Web of Science ®Google Scholar
• Hassan , A , Shariff , MNB , Shaharoun , AM and Jamaludin , H . 2003 . Improved SPC chart pattern recognition using statistical features . Int. J. Prod. Res. , 41 : 1587 – 1603 .
   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
• Hwarng , HB and Chong , CW . 1995 . Detecting process non-randomness through a fast and cumulative ART-based pattern recognizer . Int. J. Prod. Res. , 33 : 1817 – 1833 .
   Web of Science ®Google Scholar
• Jin , J and Shi , J . 2001 . Automatic feature extraction of waveform signals for in-process diagnostic performance improvement . J. Intell. Manuf. , 12 : 257 – 268 .
   Web of Science ®Google Scholar
• Lee , JM , Yoo , CK , Choi , SW , Vanrolleghem , PA and Lee , IB . 2004a . Nonlinear process monitoring using kernel principal component analysis . Chem. Eng. Sci. , 59 : 223 – 234 .
   Web of Science ®Google Scholar
• Lee , JM , Yoo , CK , Choi , SW and Lee , IB . 2004b . Statistical process monitoring with independent component analysis . J. Process Contr. , 14 : 467 – 485 .
   Web of Science ®Google Scholar
• Mitchell , TM . 1997 . Machine Learning , 52 – 80 . New York : McGraw-Hill .
   Google Scholar
• Pacella , M , Semeraro , Q and Anglani , A . 2004 . Adaptive resonance theory-based neural algorithms for manufacturing process quality control . Int. J. Prod. Res. , 42 : 4581 – 4607 .
   Web of Science ®Google Scholar
• Pham , DT and Oztemel , E . 1994 . Control chart pattern recognition using learning vector quantization networks . Int. J. Prod. Res. , 32 : 721 – 729 .
   Web of Science ®Google Scholar
• Pham , DT and Wani , MA . 1997 . Feature-based control chart recognition . Int. J. Prod. Res. , 35 : 1875 – 1890 .
   Web of Science ®Google Scholar
• Quinlan , JR . 1983 . “ Learning efficient classification procedures and their application to chess endgames ” . In Machine Learning: An Artificial Intelligence Approach , Edited by: Michalski , R.S. , Carbonell , J.G. and Mitchell , T.M. Vol. 1 , 463 – 482 . San Mateo, CA : Morgan Kaufmann .
   Google Scholar
• Quinlan , JR . 1986 . Induction of decision trees . Machine Learning , 1 : 81 – 106 .
   Google Scholar
• Quinlan , JR . 1993 . C4.5: Programs for Machine Learning , Los Altos, CA : Morgan Kaufmann .
   Google Scholar
• Quinlan , JR . 1996 . Improved use of continuous attributes in C4.5 . J. Artif. Intell. Res. , 4 : 77 – 90 .
   Web of Science ®Google Scholar
• Wang , CH , Kuo , W and Qi , H . 2007 . An integrated approach for process monitoring using wavelet analysis and competitive neural network . Int. J. Prod. Res. , 45 : 227 – 244 .
   Web of Science ®Google Scholar
• Yang , MS and Yang , JH . 2005 . A fuzzy-soft learning vector quantization for control chart pattern recognition . Comput. Ind. Eng. , 48 : 205 – 221 .
   Web of Science ®Google Scholar
• Yang , JH and Yang , MS . 2005 . A control chart pattern recognition scheme using a statistical correlation coefficient method . Int. J. Prod. Res. , 48 : 205 – 221 .
   Google Scholar
• Yousef , AA . 2004 . Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks . Comput. Ind. Eng. , 47 : 17 – 29 .
   Web of Science ®Google Scholar