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

Guojian Chen, Zhenglei He (), Yi Man, Jigeng Li, Mengna Hong and Kim Phuc Tran
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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

A chapter in Artificial Intelligence for Smart Manufacturing, 2023, pp 83-96 from Springer

Abstract: 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.

Keywords: Fault prediction; Papermaking process; Gaussian mixture model; Mahalanobis distance (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-30510-8_5

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DOI: 10.1007/978-3-031-30510-8_5

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