An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector

Early detection of unnatural control chart patterns (CCP) is desirable for any industrial process. Most of recent CCP recognition works are on statistical feature extraction and artificial neural network (ANN)-based recognizers. In this paper, a two-stage hybrid detection system has been proposed using support vector machine (SVM) with self-organized maps. Direct Cosine transform of the CCP data is taken as input. Simulation results show significant improvement over conventional recognizers, with reduced detection window length. An analogous recognition system consisting of statistical feature vector input to the SVM classifier is further developed for comparison.

[1]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[2]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

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

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

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

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

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

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

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

[10]  Xiaoh Wang.  Hybrid Abnormal Patterns Recognition of Control Chart Using Support Vector Machining , 2008, 2008 International Conference on Computational Intelligence and Security.

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

[12]  Shaoxiong Wu,et al.  Wavelet Neural Network-based Control Chart Patterns Recognition , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[13]  J. D. T. Tannock,et al.  A neural network approach to characterize pattern parameters in process control charts , 1999, J. Intell. Manuf..

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

[15]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[16]  Shankar Chakraborty,et al.  A study on the various features for effective control chart pattern recognition , 2007 .

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

[18]  Amjed M. Al-Ghanim,et al.  Unnatural pattern recognition on control charts using correlation analysis techniques , 1995 .

[19]  A. Hassan.  Ensemble ANN-based recognizers to improve classification of X-bar control chart patterns , 2008, 2008 IEEE International Conference on Industrial Engineering and Engineering Management.

[20]  Ron Meir,et al.  A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks , 1992, NIPS.

[21]  Lukasz Kurgan,et al.  Fourth International Conference on Machine Learning and Applications, ICMLA 2005, Los Angeles, California, USA, 15-17 December 2005 , 2005, ICMLA.

[22]  Helge J. Ritter,et al.  Neural computation and self-organizing maps - an introduction , 1992, Computation and neural systems series.

[23]  D.T. Pham,et al.  Application of the Bees Algorithm to the Training of Learning Vector Quantisation Networks for Control Chart Pattern Recognition , 2006, 2006 2nd International Conference on Information & Communication Technologies.