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Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts

Author

Listed:
  • Yuehjen E. Shao

    (Department of Statistics and Information Science, Fu Jen Catholic University Xinzhuang Dist., New Taipei City 24205, Taiwan)

  • Shih-Chieh Lin

    (Department of Statistics and Information Science, Fu Jen Catholic University Xinzhuang Dist., New Taipei City 24205, Taiwan)

Abstract

With the rapid development of advanced sensor technologies, it has become popular to monitor multiple quality variables for a manufacturing process. Consequently, multivariate statistical process control (MSPC) charts have been commonly used for monitoring multivariate processes. The primary function of MSPC charts is to trigger an out-of-control signal when faults occur in a process. However, because two or more quality variables are involved in a multivariate process, it is very difficult to diagnose which one or which combination of quality variables is responsible for the MSPC signal. Though some statistical decomposition methods may provide possible solutions, the mathematical difficulty could confine the applications. This study presents a time delay neural network (TDNN) classifier to diagnose the quality variables that cause out-of-control signals for a multivariate normal process (MNP) with variance shifts. To demonstrate the effectiveness of our proposed approach, a series of simulated experiments were conducted. The results were compared with artificial neural network (ANN), support vector machine (SVM) and multivariate adaptive regression splines (MARS) classifiers. It was found that the proposed TDNN classifier was able to accurately recognize the contributors of out-of-control signal for MNPs.

Suggested Citation

  • Yuehjen E. Shao & Shih-Chieh Lin, 2019. "Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts," Mathematics, MDPI, vol. 7(10), pages 1-14, October.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:959-:d:275977
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    References listed on IDEAS

    as
    1. Jinho Kim & Myong K. Jeong & Elsayed A. Elsayed & K.N. Al-Khalifa & A.M.S. Hamouda, 2016. "An adaptive step-down procedure for fault variable identification," International Journal of Production Research, Taylor & Francis Journals, vol. 54(11), pages 3187-3200, June.
    2. Yuehjen E. Shao & Chi-Jie Lu & Yu-Chiun Wang, 2012. "A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-12, September.
    3. Yuehjen E. Shao & Chia-Ding Hou, 2013. "Fault Identification in Industrial Processes Using an Integrated Approach of Neural Network and Analysis of Variance," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, June.
    4. Hsu-Hao Yang & Mei-Ling Huang & Shih-Wei Yang, 2015. "Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection," Energies, MDPI, vol. 8(10), pages 1-16, October.
    5. Yuehjen E. Shao & Chia-Ding Hou, 2013. "Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

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