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Fault Prediction of Papermaking Process Based on Gaussian Mixture Model and Mahalanobis Distance

In: Artificial Intelligence for Smart Manufacturing

Author

Listed:
  • Guojian Chen

    (South China University of Technology)

  • Zhenglei He

    (South China University of Technology)

  • Yi Man

    (South China University of Technology)

  • Jigeng Li

    (South China University of Technology)

  • Mengna Hong

    (South China University of Technology)

  • Kim Phuc Tran

    (University of Lille, ENSAIT, ULR 2461 - GEMTEX - Génie et Matériaux Textiles)

Abstract

Equipment monitoring and process fault prediction are increasingly concerned in the modern industry due to the growing complexity of the production process and the high risk derived from severe consequences on the paper mills in case of production failure. Whereas the paper manufacturing process is continuous that is difficult to be warned early of faults. To address such issues, this Chapter proposes a data-driven approach to predict fault in the papermaking process on the basis of correlation analysis and clustering algorithms. Historical operating data of key variables were acquired in normal operating conditions. The health benchmark dataset was constructed based on the Gaussian mixture model (GMM) and Mahalanobis distance (MD) to evaluate the operating status of the papermaking process. The verification results showed that the proposed model has a fault prediction accuracy of 76.8% and a recall rate of 72.5%, which allows anomalous data to be observed in advance, providing valuable time for subsequent fault diagnosis.

Suggested Citation

  • Guojian Chen & Zhenglei He & Yi Man & Jigeng Li & Mengna Hong & Kim Phuc Tran, 2023. "Fault Prediction of Papermaking Process Based on Gaussian Mixture Model and Mahalanobis Distance," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Artificial Intelligence for Smart Manufacturing, pages 83-96, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-30510-8_5
    DOI: 10.1007/978-3-031-30510-8_5
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