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

Author

Listed:
  • Ling-Jing Kao

    (National Taipei University of Technology)

  • Tian-Shyug Lee

    (Fu Jen Catholic University)

  • Chi-Jie Lu

    (Chien Hsin University of Science and Technology)

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.

Suggested Citation

  • Ling-Jing Kao & Tian-Shyug Lee & Chi-Jie Lu, 2016. "A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 653-664, June.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:3:d:10.1007_s10845-014-0903-x
    DOI: 10.1007/s10845-014-0903-x
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    References listed on IDEAS

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    1. Chih-Hsuan Wang & Way Kuo & Hairong Qi, 2007. "An integrated approach for process monitoring using wavelet analysis and competitive neural network," International Journal of Production Research, Taylor & Francis Journals, vol. 45(1), pages 227-244, January.
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    Cited by:

    1. Yun Bai & Zhenzhong Sun & Bo Zeng & Jianyu Long & Lin Li & José Valente Oliveira & Chuan Li, 2019. "A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2245-2256, June.
    2. Yuehjen E. Shao & Po-Yu Chang & Chi-Jie Lu, 2017. "Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process," Complexity, Hindawi, vol. 2017, pages 1-10, October.
    3. Tao Zan & Zhihao Liu & Hui Wang & Min Wang & Xiangsheng Gao, 2020. "Control chart pattern recognition using the convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 703-716, March.
    4. Ahmed Maged & Min Xie, 2023. "Recognition of abnormal patterns in industrial processes with variable window size via convolutional neural networks and AdaBoost," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1941-1963, April.

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