Mixture control chart patterns recognition using independent component analysis and support vector machine

Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control charts can be associated with certain assignable causes which affect the process. Most of the existing studies assume that the observed process data which needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns, which consist of two basic CCPs combined together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme initially applies an ICA to the mixture patterns in order to generate independent components (ICs). The hidden basic patterns of the mixture patterns can be discovered in these ICs. The ICs can then serve as the input variables of the SVM for building a CCP recognition model. Experimental results revealed that the proposed scheme is able to effectively recognize mixture control chart patterns and outperform the single SVM models, which did not use an ICA as a preprocessor.

[1]  Shankar Chakraborty,et al.  Feature-based recognition of control chart patterns , 2006, Comput. Ind. Eng..

[2]  A. V.DavidSánchez,et al.  Frontiers of research in BSS/ICA , 2002, Neurocomputing.

[3]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[4]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[5]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Chun-Chin Hsu,et al.  Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring , 2010, Expert Syst. Appl..

[8]  David Zhang,et al.  Sequential row-column independent component analysis for face recognition , 2009, Neurocomputing.

[9]  Ratna Babu Chinnam,et al.  Support vector machines for recognizing shifts in correlated and other manufacturing processes , 2002 .

[10]  Lijuan Cao,et al.  Support vector machines experts for time series forecasting , 2003, Neurocomputing.

[11]  Chi-Jie Lu,et al.  Integrating independent component analysis-based denoising scheme with neural network for stock price prediction , 2010, Expert Syst. Appl..

[12]  Chi-Jie Lu,et al.  Integrated Application of SPC/EPC/ICA and neural networks , 2008 .

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

[14]  Chun-Chin Hsu,et al.  Integrate Independent Component Analysis and Support Vector Machine for Monitoring Non-Gaussian Multivariate Process , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

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

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

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

[18]  Xiaoling Zhang,et al.  Multiway kernel independent component analysis based on feature samples for batch process monitoring , 2009, Neurocomputing.

[19]  Lijuan Cao,et al.  Bond rating using support vector machine , 2006, Intell. Data Anal..

[20]  Bo-Suk Yang,et al.  Intelligent fault diagnosis system of induction motor based on transient current signal , 2009 .

[21]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[22]  Du-Ming Tsai,et al.  An independent component analysis-based filter design for defect detection in low-contrast surface images , 2006, Pattern Recognit..

[23]  Hang Joon Kim,et al.  Support Vector Machines for Texture Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[25]  Francis Eng Hock Tay,et al.  Financial Forecasting Using Support Vector Machines , 2001, Neural Computing & Applications.

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

[27]  Chris J. Harris,et al.  On the modelling of nonlinear dynamic systems using support vector neural networks , 2001 .

[28]  J. D. T. Tannock,et al.  A review of neural networks for statistical process control , 1998, J. Intell. Manuf..

[29]  Yingwei Zhang,et al.  Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM , 2009 .

[30]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

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

[32]  Zheng Chen,et al.  A hybrid system for SPC concurrent pattern recognition , 2007, Adv. Eng. Informatics.

[33]  Way Kuo,et al.  A hybrid approach for identification of concurrent control chart patterns , 2009, J. Intell. Manuf..

[34]  Shankar Chakraborty,et al.  Recognition of control chart patterns using improved selection of features , 2009, Comput. Ind. Eng..

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

[36]  Chun-Chin Hsu,et al.  Integrating independent component analysis and support vector machine for multivariate process monitoring , 2010, Comput. Ind. Eng..

[37]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[38]  Hairong Qi,et al.  An integrated approach for process monitoring using wavelet analysis and competitive neural network , 2007 .

[39]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

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

[41]  Du-Ming Tsai,et al.  Independent component analysis-based defect detection in patterned liquid crystal display surfaces , 2008, Image Vis. Comput..

[42]  Du-Ming Tsai,et al.  Independent component analysis based filter design for defect detection in low-contrast textured images , 2006, 18th International Conference on Pattern Recognition (ICPR'06).