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Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

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  • Debón, A.
  • Carlos Garcia-Díaz, J.

Abstract

Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures.

Suggested Citation

  • Debón, A. & Carlos Garcia-Díaz, J., 2012. "Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 102-114.
    • Handle: RePEc:eee:reensy:v:100:y:2012:i:c:p:102-114
    DOI: 10.1016/j.ress.2011.12.022
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    References listed on IDEAS

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