A Prediction Model for Fault Detection in Molding Process Based on Logistic Regression Technique
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.
KeywordsMachine learning Logistic regression Binary classification Fault prediction Industry 4.0
We would like to thank Maxion İnci Wheel Group and Logo Business Solutions for their support and contribution in this study.
- 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.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.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
- 6.O’Brien, J.A.: Introduction to Information Systems. Irwin McGraw-Hill, USA (2000)Google Scholar
- 7.Aberdeen Group: IOT and analytics - Better manufacturing decisions in the era of Industry 4.0 (2017). https://www.ibm.com/downloads/cas/ZPB2PN2G
- 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.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.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.Maxion İnci: Companies and Brands (2019). http://www.inciholding.com/en/companies-and-brands/production/maxion-inci
- 17.Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Press, Second Edition, USA (2017)Google Scholar