Abstract
This work provides a comparative analysis of the most popular and widely used classification algorithms applied in industrial processes, in order to tackle the issue of incident detection of abnormal situations. The proposed analysis is based on actual datasets from derived by the operation of a chemical process system situated at the premises of CERTH/CPERI. The evaluation of the tested methods is based on cross-validation using a series of Monte-Carlo simulations among several free parameters of tested classifiers and finally the application of the Adaptive Boosting technique. The experimental results are highly reliable as the accuracy reaches 98% and the F-measure metric achieves a score over 97%. Therefore, the detection of potential malfunctions is achieved using the proposed machine learning techniques.
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This work has been partially supported by the European Commission through the project HORIZON 2020-INNOVATION ACTIONS (IA)-636302-SATISFACTORY.
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Tziroglou, G., Vafeiadis, T., Ziogou, C., Krinidis, S., Voutetakis, S., Tzovaras, D. (2018). Incident Detection in Industrial Processes Utilizing Machine Learning Techniques. In: Burduk, A., Mazurkiewicz, D. (eds) Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. ISPEM 2017. Advances in Intelligent Systems and Computing, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-64465-3_5
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DOI: https://doi.org/10.1007/978-3-319-64465-3_5
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