A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC) charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA) and support vector machine (SVM) to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T2 MSPC chart to generate independent components (ICs). The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.

[1]  D. Montgomery,et al.  Contributors to a multivariate statistical process control chart signal , 1996 .

[2]  John C. Young,et al.  IMPROVING THE SENSITIVITY OF THE T2 STATISTIC IN MULTIVARIATE PROCESS CONTROL , 1999 .

[3]  Hassen Taleb,et al.  An assessment of the kernel‐distance‐based multivariate control chart through an industrial application , 2011, Qual. Reliab. Eng. Int..

[4]  Jun-Geol Baek,et al.  Spline regression based feature extraction for semiconductor process fault detection using support vector machine , 2011, Expert Syst. Appl..

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

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

[7]  ChangKyoo Yoo,et al.  Statistical monitoring of dynamic processes based on dynamic independent component analysis , 2004 .

[8]  Shi Hong-bo.  Application of Kernel Independent Component Analysis for Multivariate Statistical Process Monitoring , 2009 .

[9]  ChangKyoo Yoo,et al.  On-line Batch Process Monitoring Using Different Unfolding Method and Independent Component Analysis , 2003 .

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

[11]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[12]  Yuehjen E. Shao,et al.  A Combined MLE and Generalized P Chart Approach to Estimate the Change Point of a Multinomial Process , 2013 .

[13]  Yuehjen E. Shao,et al.  Mixture control chart patterns recognition using independent component analysis and support vector machine , 2011, Neurocomputing.

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

[15]  Chih-Chou Chiu,et al.  A FAULT DETECTION SYSTEM FOR AN AUTOCORRELATED PROCESS USING SPC/EPC/ANN AND SPC/EPC/SVM SCHEMES , 2011 .

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

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

[18]  Nola D. Tracy,et al.  Decomposition of T2 for Multivariate Control Chart Interpretation , 1995 .

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

[20]  Ruey-Shiang Guh,et al.  Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach , 2011, Comput. Ind. Eng..

[21]  Chih-Chou Chiu,et al.  Identification of process disturbance using SPC/EPC and neural networks , 2003, J. Intell. Manuf..

[22]  Manabu Kano,et al.  Monitoring independent components for fault detection , 2003 .

[23]  Yuehjen E. Shao,et al.  Change point determination for a multivariate process using a two-stage hybrid scheme , 2013, Appl. Soft Comput..

[24]  Chuen-Sheng Cheng,et al.  Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines , 2008, Expert Syst. Appl..

[25]  E. Oja,et al.  Independent Component Analysis , 2001 .

[26]  Chunming Xia,et al.  Isolating multiple sources of plant-wide oscillations via independent component analysis , 2003 .

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

[28]  Panitarn Chongfuangprinya,et al.  Integration of support vector machines and control charts for multivariate process monitoring , 2011 .

[29]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

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