Machine learning model for predicting accidents at gas production facilities
DOI: 10.21293/1818-0442-2025-28-3-53-58
Abstract: Emergency situations lead to disruptions of workflows, regard-less of how quickly these emergencies are resolved, therefore the ability to predict such situations would be extremely useful in many fields. The paper implements an approach to prepro-cessing data from a SCADA database using averaging and correlation analysis. A machine learning (ML) model is then trained on the preprocessed data to classify the current state of the node as an emergency precursor to simulate a real work-flow. The model achieved high accuracy score and other metrics without fine tuning and hyperparameter optimization, thus con-firming the possibility of using ML models for the task.
Keywords: machine learning, data preprocessing, SCADA, fault detection, technological process, classifier
For citation:
Garipov E. T., Borovskoy I. G., Kruchinin V. V. Machine learning model for predicting accidents at gas production facilities. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2025, vol. 28, no. 3, pp. 53–58. DOI: 10.21293/1818-0442-2025-28-3-53-58
Authors and copyright holders:
- Garipov E. T. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
- Borovskoy I. G. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
- Kruchinin V. V. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
- 1. Zhakhina R.U., Kostylev K.A. [SCADA systems and their main components]. Actual scientific research in the modern world, 2020, no. 4–1 (60), pp. 62–65 (in Russ.).
- 2. Nychik T.Yu. [Methodology justification of the accident risk assessment for the shipping lock]. Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S.O. Makarova, 2013, no. 1, pp. 15–21 (in Russ.).
- 3. Stas G.V., Shainkman L.E., Smirnova E.V. Otsenka riska vozniknoveniya avarii pri dobyche burykh uglei [Evaluating risk of arising accidents by underground mining brown coal]. Izvestiya Tula State University. Earth sciences, 2015, no. 3, pp. 31–41 (in Russ.).
- 4. Morozova T.S. [On implementation of expert statistical method in assessment of accident and failure probability for mixing and charging machines]. Gornaya promyshlennost – Russian Mining industry, 2020, no. 4, pp. 86–91 (in Russ.).
- 5. Edamenko A.S., Yastrebinskaya A.V. [Hazard analysis and accident risk assessment at petroleum product storage facilities]. Technosphere safety technologies, no. 3(101), pp. 60–73 (in Russ.).
- 6. Nemtsov V.S. [Assessment of the risk of an accident of lifting structures during the construction of the bridge]. Natural and man-made risks (physico-mathematical and applied aspects), 2023, no. 1(45), pp. 25–33 (in Russ.).
- 7. Borovskoy I.G., Shelmina E.A., Afanasyeva I.G., Matolygin A.A. [Modeling of artificial intelligence systems for early detection of emergency situations at vital facilities]. Modeling, optimization and IT, 2022, no. 10 (3), pp. 1–16 (in Russ.).
- 8. Sukhanov A.V., Akhmedzyanov D.A. Parametricheskaya diagnostika i otsenka sostoyaniya gazoturbinnyh energeticheskih ustanovok s ispolzovaniem SCADA-tekhnologii i imitatsionnogo modelirovaniya [Parametric diagnostics and assessment of the state of gas turbine power plants using SCADAtechnologies and imitation modelling]. Vestnik USATU, 2021, vol. 25, no. 4(94), pp. 83–90 (in Russ.).
- 9. Antonov O.V., Raykova E.F., Muratov R.E., [Emergency preventing system at production technological objects based on situational mathematical model]. Vestnik of Astrakhan State Technical University. Series: Management, Computer Science and Informatics, 2022, no. 2, pp. 22–32 (in Russ.).
- 10. Zubov D.V., Lebedev D.A. Diagnostika otkazov tekhnologicheskogo oborudovaniya himicheskih proizvodstv s pomoshchyu iskusstvennogo intellekta [Diagnostics of failures of technological equipment of chemical industries using artificial intelligence]. Software Systems and Computational Methods, 2024, no. 2, pp. 30–40 (in Russ.).
- 11. Al-Ammari W.A, Sleiti A.K., Rahman M.A., RezaeiGomari S., Hassan I., Hassan R. Digital twin for leak detection and fault diagnostics in gas pipelines: A systematic review, model development, and case study. Alexandria Engineering Journal, 2025, vol. 123, pp. 91–111.
- 12. Tornyeviadzi H.M., Seidu R. Leakage detection in water distribution networks via 1D CNN deep autoencoder for multivariate SCADA data. Engineering Applications of Artificial Intelligence, 2023, vol. 122, 106062.
- 13. Hussein A.M.H., Sharafiz B.A.R., Faizal B.M., Prajindra S.K., Nawal A.B.A.J. Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks. Journal of Pipeline Science and Engineering, 2025, vol. 5, no. 1, 100223.
- 14. Bao J., Guangchao Y., Zheng X., Zhang J., Ji X. A Data Driven Model for Predicting Tool Health Condition in High Speed Milling of Titanium Plates Using Real-Time SCADA. Procedia CIRP, 2017, vol. 61, pp. 317–322.
- 15. Phong B.D., Barszcz T., Staszewski W.J. Anomaly detection of wind turbines based on stationarity analysis of SCADA data. Renewable Energy, 2024, vol. 232, 121076.
- 16. RandomForestClassifier – scikit-learn 1.7.1 documentation [Electronic resource]. – Available at: https://scikitlearn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html, free (Accessed: 04.09.2025).
- 17. StandardScaler – scikit-learn 1.7.1 documentation [Electronic resource]. – Available at: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html, free (Accessed: 04.09.2025).
- 18. accuracy_score – scikit-learn 1.7.1 documentaion [Electronic resource]. – Available at: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html, free (Accessed: 04.09.2025).
Last issue
-2026-1_%D0%BA%D0%BE%D0%BF%D0%B8%D1%8F.jpg)
Editorial office address
Executive Secretary of the Editor’s Office
Editor’s Office: 40 Lenina Prospect, Tomsk, 634050, Russia
Phone / Fax: + 7 (3822) 701-582
Viktor N. Maslennikov
Executive Secretary of the Editor’s Office
Editor’s Office: 40 Lenina Prospect, Tomsk, 634050, Russia
Phone / Fax: + 7 (3822) 51-21-21 / 51-43-02
Subscription for updates
