Advertisement

A Prediction Model for Fault Detection in Molding Process Based on Logistic Regression Technique

  • İnanç KabasakalEmail author
  • Fatma Demircan Keskin
  • Aydin Koçak
  • Haluk Soyuer
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Industry 4.0 is often described as a wave of transformation that enforces the digitalization of processes to create integrated and autonomous systems. In this regard, the collection of process data is a necessity to analyze data with advanced techniques for various purposes. Statistical techniques in machine learning might provide solutions for fault detection and other tasks in manufacturing processes. In our study, a learning model is proposed for a fault prediction task with the use of Logistic Regression. The data used for the analysis involve measurements from sequential processes carried out in a large-scale wheel rim manufacturer. The pre-processing and analysis of process data was introduced along with a case study. Moreover, findings of the model were presented and the potential use of the model will be discussed.

Keywords

Machine learning Logistic regression Binary classification Fault prediction Industry 4.0 

Notes

Acknowledgments

We would like to thank Maxion İnci Wheel Group and Logo Business Solutions for their support and contribution in this study.

References

  1. 1.
    Anand, A., Coltman, T., Sharma, R.: Four steps to realizing business value from digital data streams. MIS Q. Executive 15(4), 259–277 (2016)Google Scholar
  2. 2.
    Chen, H.M., Schütz, R., Kazman, R., Matthes, F.: How lufthansa capitalized on big data for business model renovation. MIS Q. Executive 16(1), 19–34 (2017)Google Scholar
  3. 3.
    Ives, B., Palese, B., Rodriguez, J.A.: Enhancing customer service through the internet of things and digital data streams. MIS Q. Executive 15(4), 279–297 (2016)Google Scholar
  4. 4.
    Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5), 616–630 (2017)CrossRefGoogle Scholar
  5. 5.
    Hashemian, H.M.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60(1), 226–236 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    O’Brien, J.A.: Introduction to Information Systems. Irwin McGraw-Hill, USA (2000)Google Scholar
  7. 7.
    Aberdeen Group: IOT and analytics - Better manufacturing decisions in the era of Industry 4.0 (2017). https://www.ibm.com/downloads/cas/ZPB2PN2G
  8. 8.
    Almada-Lobo, F.: The Industry 4.0 revolution and the future of manufacturing execution systems (MES). J. Innov. Manag. 3(4), 16–21 (2016)CrossRefGoogle Scholar
  9. 9.
    Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine learning, pp. 161–168. ACM (2006)Google Scholar
  10. 10.
    Candanedo, I.S., Nieves, E.H., González, S.R., Martín, M.T.S., Briones, A.G.: Machine learning predictive model for industry 4.0. In: Uden, L., Hadzima, B., Ting, I.-H. (eds.) KMO 2018. CCIS, vol. 877, pp. 501–510. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-95204-8_42CrossRefGoogle Scholar
  11. 11.
    Pandey, P.S.: Machine learning and IoT for prediction and detection of stress. In: Proceedings of 17th International Conference on Computational Science and its Applications (ICCSA), pp. 1–5. IEEE (2017)Google Scholar
  12. 12.
  13. 13.
    King, J.E.: Binary logistic regression. In: Osborne, J.W. (ed.) Best Practices in Quantitative Methods, pp. 358–384. Sage Publications, USA (2008)CrossRefGoogle Scholar
  14. 14.
    Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. John Wiley and Sons, New York (2000)CrossRefGoogle Scholar
  15. 15.
    de Menezes, F.S., Liska, G.R., Cirillo, M.A., Vivanco, M.J.: Data classification with binary response through the Boosting algorithm and logistic regression. Expert Syst. Appl. 69, 62–73 (2017)CrossRefGoogle Scholar
  16. 16.
    Giudici, P.: Data mining model comparison. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 641–654. Springer, Boston (2009)CrossRefGoogle Scholar
  17. 17.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Press, Second Edition, USA (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • İnanç Kabasakal
    • 1
    Email author
  • Fatma Demircan Keskin
    • 1
  • Aydin Koçak
    • 1
  • Haluk Soyuer
    • 1
  1. 1.Department of Business AdministrationEge UniversityİzmirTurkey

Personalised recommendations