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A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine

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Abstract

Recognition of unnatural control chart patterns (CCPs) is an important issue because the unnatural CCPs can be associated with specific assignable causes negatively affecting the manufacturing process. By assuming that an unnatural CCP is a combination of normal pattern and process disturbance, a multi-stage control chart pattern recognition scheme which integrates independent component analysis (ICA) and support vector machine (SVM) is proposed in this study. The proposed multi-stage ICA-SVM scheme first uses ICA to extract independent components (ICs) from the monitoring process data containing CCPs. The normal pattern and process disturbance hidden in the process data can be discovered in the ICs. Then, the IC representing the process disturbance can be identified. Finally, the identified IC and the data of the monitoring process are used as input variables to develop three different SVM models for CCP recognition. The simulation results show that the proposed multi-stage ICA-SVM scheme not only produces accurate and stable recognition results but also has better classification accuracy than four competing models.

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References

  • Assaleh, K., & Al-assaf, Y. (2005). Feature extraction and analysis for classifying causable patterns in control charts. Computer & Industrial Engineering, 49, 168–181.

    Article  Google Scholar 

  • Cao, L. J. (2003). Support vector machines experts for time series forecasting. Neurocomputing, 51, 321–339.

    Article  Google Scholar 

  • Chan, W., Cheung, K. C., & Harris, C. J. (2001). On the modelling of nonlinear dynamic system using support vector neural networks. Engineering Applications of Artificial Intelligence, 14, 105–113.

    Article  Google Scholar 

  • Chaudhuri, A., & De, K. (2011). Fuzzy support vector machine for bankruptcy prediction. Applied Soft Computing, 11, 472–486.

    Article  Google Scholar 

  • Chawla, M. P. S. (2011). PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: A survey and comparison. Applied Soft Computing, 11(2), 2216–2226.

    Article  Google Scholar 

  • Cherkassky, V., & Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks, 17, 113–126.

    Article  Google Scholar 

  • Cheung, Y. M., & Xu, L. (2001). Independent component ordering in ICA time series analysis. Neurocomputing, 41, 145–152.

    Article  Google Scholar 

  • Chinnam, R. B. (2002). Support vector machines for recognizing shifts in correlated and other manufacturing processes. International Journal of Production Research, 40, 4449–4466.

    Article  Google Scholar 

  • Chowdhury, S., Sing, J. K., Basu, D. K., & Nasipuri, M. (2011). Face recognition by generalized two-dimensional FLD method and multi-class support vector machines. Applied Soft Computing, 11(7), 282–292.

    Article  Google Scholar 

  • Chuang, C. C., & Lee, Z. J. (2011). Hybrid robust support vector machines for regression with outliers. Applied Soft Computing, 11, 64–72.

    Google Scholar 

  • Das, P., & Banerjee, I. (2011). An hybrid detection system of control chart patterns using cascaded SVM and neural network-based detector. Neural Computing and Applications, 20, 287–296.

    Article  Google Scholar 

  • David, V., & Sanchez, A. (2002). Frontiers of research in BSS/ICA. Neurocomputing, 49, 7–23.

    Article  Google Scholar 

  • Fatemi Ghomi, S. M. T., Lesany, S. A., & Koochakzadeh, A. (2011). Recognition of unnatural patterns in process control charts through combining two types of neural networks. Applied Soft Computing, 11(8), 5444–5456.

    Article  Google Scholar 

  • Gauri, S. K., & Charkaborty, S. (2006). Feature-based recognition of control chart patterns. Computer & Industrial Engineering, 51, 726–742.

    Article  Google Scholar 

  • Gauri, S. K., & Charkaborty, S. (2009). Recognition of control chart patterns using improved selection of features. Computer & Industrial Engineering, 56, 1577–1588.

    Article  Google Scholar 

  • Guh, R. S. (2005). A hybrid learning-based model for on-line detection and analysis of control chart patterns. Computer & Industrial Engineering, 49, 35–62.

    Article  Google Scholar 

  • Guh, R. S., & Shiue, Y. R. (2005). On-line identification of control chart patterns using self-organizing approaches. International Journal of Production Research, 43(6), 1225–1254.

    Article  Google Scholar 

  • Guh, R. S., & Tannock, J. D. T. (1999). Recognition of control chart concurrent patterns using a neural network approach. International Journal of Production Research, 37(8), 1743–1765.

    Article  Google Scholar 

  • Hachicha, W., & Ghorbel, A. (2012). A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme. Computers & Industrial Engineering, 63(1), 204–222.

    Article  Google Scholar 

  • Hsu, C. C., & Chen, L. S. (2008). Integrate independent component analysis and support vector machine for monitoring Non-Gaussian multivariate process. IEEE international conference on wireless communications, networking and mobile computing (pp. 1–4). Dalian, China.

  • Hsu, C. C., Chen, M. C., & Chen, L. S. (2010a). Integrating independent component analysis and support vector machine for multivariate process monitoring. Computers & Industrial Engineering, 59(1), 145–156.

    Article  Google Scholar 

  • Hsu, C. C., Chen, M. C., & Chen, L. S. (2010b). Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring. Expert Systems with Applications, 37(4), 3264–3273.

    Article  Google Scholar 

  • Hsu, C. C., & Cheng, C. Y. (2010). Adaptive chart based on independent component analysis for multivariate statistical process monitoring. International Journal of Innovative Computing, Information and Control, 6(8), 3365–3380.

    Google Scholar 

  • Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural, Network, 13, 415–425.

    Article  Google Scholar 

  • Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.

  • Hyvärinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3), 623–634.

    Article  Google Scholar 

  • Hyvärinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural Networks, 13, 411–430.

    Article  Google Scholar 

  • Hyvärinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. New York: Wiley.

    Book  Google Scholar 

  • Kim, K. I., Jung, K., Park, S. H., & Kim, H. J. (2002). Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 1542–1550.

    Article  Google Scholar 

  • Lin, S. Y., Guh, R. S., & Shiue, Y. R. (2011). Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach. Computers and Industrial Engineering, 61(4), 1123–1134.

    Article  Google Scholar 

  • Lu, C. J. (2012). An independent component analysis-based disturbance separation scheme for statistical process monitoring. Journal of Intelligent Manufacturing, 23, 561–573.

    Article  Google Scholar 

  • Lu, C. J. (2010). Integrating independent component analysis-based denoising scheme with neural network for stock price prediction. Expert Systems with Applications, 37(10), 7056–7064.

    Article  Google Scholar 

  • Lu, C. J., & Tsai, D. M. (2008). Independent component analysis-based defect detection in patterned liquid crystal display surfaces. Image & Vision Computing, 26(7), 955–970.

    Article  Google Scholar 

  • Lu, C. J., Lee, T. S., & Chiu, C. C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125.

    Article  Google Scholar 

  • Lu, C. J., Shao, Y. E., & Li, B. S. (2011). Mixture control chart patterns recognition using independent component analysis and support vector machine. Neurocomputing, 74(11), 1908–1914.

    Article  Google Scholar 

  • Lu, C. J., Wu, C. M., Keng, C. J., & Chiu, C. C. (2008). Integrated application of SPC/EPC/ICA and neural networks. International Journal of Production Research, 46(4), 873–893.

    Article  Google Scholar 

  • Mitra, V., Wang, C. J., & Banerjee, S. (2007). Text classification: A least square support vector machine approach. Applied Soft Computing, 7(3), 908–914.

    Article  Google Scholar 

  • Montgomery, D. C. (2001). Introduction to statistical quality control. New York: Wiley.

    Google Scholar 

  • Ranaee, V., & Ebrahimzadeh, A. (2011). Control chart pattern recognition using a novel hybrid intelligent method. Applied Soft Computing, 11(2), 2676–2686.

    Google Scholar 

  • Tay, F. E. H., & Cao, L. J. (2003). Support vector machine with adaptive parameters in financial time series forecastin. IEEE Transactions on Neural Networks, 14, 1506–1518.

    Google Scholar 

  • Vapnik, V. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 5, 988–999.

    Google Scholar 

  • Vapnik, V. N. (2000). The nature of statistical learning theory. Berlin: Springer.

    Book  Google Scholar 

  • Wang, C. H., Dong, T. P., & Kuo, W. (2009). A hybrid approach for identification of concurrent control chart patterns. Journal of Intelligent Manufacturing, 20, 409–419.

    Article  Google Scholar 

  • Wang, C. H., & Kuo, W. (2007). Identification of control chart patterns using wavelet filtering and robust fuzzy clustering. Journal of Intelligent Manufacturing, 18, 343–350.

    Article  Google Scholar 

  • Wang, C. H., Kuo, W., & Qi, H. (2007). An integrated approach for process monitoring using wavelet analysis and competitive neural network. International Journal of Production Research, 45(1), 227–244.

    Article  Google Scholar 

  • Western Electric Company. (1958). Statistical quality control handbook. Indianapolis, Indiana: Western Electric Co. Inc.

  • Yang, J. H., & Yang, M. S. (2005). A control chart pattern recognition scheme using a statistical correlation coefficient method. Computers & Industrial Engineering, 48, 205–221.

    Article  Google Scholar 

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Acknowledgments

This work is partially supported by the National Science Council of the Republic of China, Grant no. NSC 102-2221-E-231-012-. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Authors and Affiliations

  1. Department of Business Management, National Taipei University of Technology, Taipei, Taiwan

    Ling-Jing Kao

  2. Department of Business and Administration, Fu Jen Catholic University, New Taipei City, Taiwan

    Tian-Shyug Lee

  3. Department of Industrial Management, Chien Hsin University of Science and Technology, No.229, Jianxing Rd., Zhongli City, Taoyuan County , 32097, Taiwan

    Chi-Jie Lu

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  1. Ling-Jing Kao
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Correspondence to Chi-Jie Lu.

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Kao, LJ., Lee, TS. & Lu, CJ. A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine. J Intell Manuf 27, 653–664 (2016). https://doi.org/10.1007/s10845-014-0903-x

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  • DOI: https://doi.org/10.1007/s10845-014-0903-x

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