Email this article Predictive Analytics System for the Technical Condition of a Sinter Extractor Using Artificial Intelligence Methods
- Authors: Chernukhin A.V.1, Bogdanova E.A.2, Savitskaya T.V.1, Kulakov D.G.1, Pavlov I.R.1
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Affiliations:
- D. I. Mendeleev Russian University of Chemical Technology
- M. V. Lomonosov Moscow State University
- Issue: No 3 (2024)
- Pages: 87-103
- Section: Intelligent Planning and Control
- URL: https://journal-vniispk.ru/2071-8594/article/view/265361
- DOI: https://doi.org/10.14357/20718594240307
- EDN: https://elibrary.ru/HZKRKY
- ID: 265361
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Abstract
The article describes approaches to building a software system that allows predicting possible failures and malfunctions of industrial equipment based on data on its condition, which will significantly affect the safety of work and the effective functioning of the enterprise. For the task of predicting equipment failures, a model based on "soft voting" between three algorithms with different approaches to classification is proposed: convolutional neural network, logistic regression and the support vector method. A model based on an isolating forest algorithm and an LSTM-based neural network is proposed to predict failures. A web service has been developed that implements the main functions of a predictive analytics system based on artificial intelligence methods: monitoring the technical condition of the excavators in real time, statistical analysis of malfunctions, fault prediction and model training.
About the authors
Artyom V. Chernukhin
D. I. Mendeleev Russian University of Chemical Technology
Author for correspondence.
Email: chernukhin.a.v@muctr.ru
Postgraduate Student, Department of Cybernetics of Chemical and Technological Processes
Russian Federation, MoscowElizaveta A. Bogdanova
M. V. Lomonosov Moscow State University
Email: eabogdanova.bioinf@gmail.com
Postgraduate Student, Department of Bioengineering
Russian Federation, MoscowTatiana V. Savitskaya
D. I. Mendeleev Russian University of Chemical Technology
Email: savitskaia.t.v@muctr.ru
Doctor of Technical Sciences, Professor, Department of Cybernetics of Chemical and Technological Processes
Russian Federation, MoscowDmitry G. Kulakov
D. I. Mendeleev Russian University of Chemical Technology
Email: dimacreator1998@gmail.com
Programmer Engineer, Master’s Student, Department of Information Computer Technology
Russian Federation, MoscowIlya R. Pavlov
D. I. Mendeleev Russian University of Chemical Technology
Email: ilyapavlo667@gmail.com
Programmer Engineer, Master’s Student, Department of Information Computer Technology
Russian Federation, MoscowReferences
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