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Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

Author

Listed:
  • Xueliang Zhou

    (Xi’an Jiaotong University)

  • Pingyu Jiang

    (Xi’an Jiaotong University)

  • Xianxiang Wang

    (Xi’an Jiaotong University)

Abstract

Accurate control chart patterns recognition (CCPR) plays an essential role in the implementation of control charts. However, it is a challenging problem since nonrandom control chart patterns (CCPs) are normally distorted by “common process variations”. In this paper, a novel method of CCPR by integrating fuzzy support vector machine (SVM) with hybrid kernel function and genetic algorithm (GA) is proposed. Firstly, two shape features and two statistical features that do not depend on the distribution parameters and number of samples are presented to explicitly describe the characteristics of CCPs. Then, a novel multiclass method based on fuzzy SVM with a hybrid kernel function is proposed. In this method, the influence of outliers on classification accuracy of SVM-based classifiers is weakened by assigning a degree of membership for every training sample. Meanwhile, a hybrid kernel function combining Gaussian kernel and polynomial kernel is adopted to further enhance the generalization ability of the classifiers. To solve the issue of features selection and parameters optimization, GA is used to simultaneously optimize the input features subsets and parameters of fuzzy SVM-based classifier. Finally, several simulation experiments and a real example are addressed to validate the feasibility and effectiveness of the proposed methodology. And the results of simulation experiments demonstrate that it can achieve excellent performance for CCPR and outperforms other approaches, such as learning vector quantization network, multi-layer perceptron network, probability neural network, fuzzy clustering and SVM, in term of recognition accuracy. The results of the practical cases manifest that the proposed method has application potential for solving the problem of control chart interpretation in real-world.

Suggested Citation

  • Xueliang Zhou & Pingyu Jiang & Xianxiang Wang, 2018. "Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 51-67, January.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1089-6
    DOI: 10.1007/s10845-015-1089-6
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    References listed on IDEAS

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    1. Du, Shichang & Lv, Jun, 2013. "Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes," International Journal of Production Economics, Elsevier, vol. 141(1), pages 377-387.
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    Cited by:

    1. 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.
    2. Pamela Chiñas-Sanchez & Ismael Lopez-Juarez & Jose Antonio Vazquez-Lopez & Jose Luis Navarro-Gonzalez & Aidee Hernandez-Lopez, 2021. "Out-of-Control Multivariate Patterns Recognition Using D 2 and SVM: A Study Case for GMAW," Mathematics, MDPI, vol. 9(5), pages 1-14, February.
    3. 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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