Control chart pattern recognition using K-MICA clustering and neural networks.

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.

[1]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[2]  Ruey-Shan Guo,et al.  Decision tree based control chart pattern recognition , 2008 .

[3]  Amjed M. Al-Ghanim,et al.  Automated unnatural pattern recognition on control charts using correlation analysis techniques , 1997 .

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

[5]  Wang Lei.  Genetic k-means Algorithm for Vector Quantization , 2003 .

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

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

[8]  M. Arif Wani,et al.  Parallel algorithm for control chart pattern recognition , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

[9]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[10]  Ángel Fernando Kuri Morales,et al.  A search space reduction methodology for data mining in large databases , 2009, Eng. Appl. Artif. Intell..

[11]  Taher Niknam,et al.  An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering , 2011, Eng. Appl. Artif. Intell..

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

[13]  Ali Maroosi,et al.  A honeybee-mating approach for cluster analysis , 2008 .

[14]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[15]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[16]  Kevin J. Dooley,et al.  Identification of change structure in statistical process control , 1992 .

[17]  Atsushi Imiya,et al.  Unsupervised cluster discovery using statistics in scale space , 2009, Eng. Appl. Artif. Intell..

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

[19]  A. Anglani,et al.  Adaptive resonance theory-based neural algorithms for manufacturing process quality control , 2004 .

[20]  Way Kuo,et al.  Identification of control chart patterns using wavelet filtering and robust fuzzy clustering , 2007, J. Intell. Manuf..

[21]  C. A. Murthy,et al.  In search of optimal clusters using genetic algorithms , 1996, Pattern Recognit. Lett..

[22]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[23]  Qinghong Le,et al.  A new ANN model and its application in pattern recognition of control charts , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

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

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

[26]  Mehmet Erler,et al.  Control Chart Pattern Recognition Using Artificial Neural Networks , 2000 .

[27]  Zhiqiang Cheng,et al.  A Research about Pattern Recognition of Control Chart Using Probability Neural Network , 2008, 2008 ISECS International Colloquium on Computing, Communication, Control, and Management.

[28]  Taher Niknam,et al.  An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering , 2009 .

[29]  Caro Lucas,et al.  Colonial competitive algorithm: A novel approach for PID controller design in MIMO distillation column process , 2008, Int. J. Intell. Comput. Cybern..

[30]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

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