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Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

Author

Listed:
  • Riku-Pekka Nikula

    (University of Oulu)

  • Konsta Karioja

    (University of Oulu)

  • Kauko Leiviskä

    (University of Oulu)

  • Esko Juuso

    (University of Oulu)

Abstract

The continuous development of steel products generates new challenges for the maintenance of manufacturing machines in steel mills. Substantial mechanical stress is inflicted on the machines during the processing of modern high-strength steels. This increases the risks of damage and flaws in the processed material may appear if the capability of a machine is exceeded. Therefore, new approaches are needed to prevent the machine condition from deteriorating. This study introduces an approach to the prediction of mechanical stress inflicted on a roller leveler during the processing of cold steel strips. The relative stress level is indicated by features extracted from an acceleration signal. These features are based on the calculation of generalized norms. Steel strip properties are used as explanatory variables in regression models to predict values for the extracted vibration features. The models tested in this study include multiple linear regression, partial least squares regression and generalized regression neural network. The models were tested using an extensive data set from a roller leveler that is in continuous operation in a steel mill. The prediction accuracy of the best models identified indicates that the relative stress level inflicted by each steel strip could be predicted based on its properties.

Suggested Citation

  • Riku-Pekka Nikula & Konsta Karioja & Kauko Leiviskä & Esko Juuso, 2019. "Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1563-1579, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1341-3
    DOI: 10.1007/s10845-017-1341-3
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    References listed on IDEAS

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    1. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
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    5. Ahmed Ragab & Mohamed-Salah Ouali & Soumaya Yacout & Hany Osman, 2016. "Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 943-958, October.
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    Cited by:

    1. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.

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