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Identification of Critical States of Technological Processes Based on Predictive Analytics Methods

  • CONTROL IN TECHNICAL SYSTEMS
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Abstract

The paper proposes a predictive approach to assessing special classes of dangerous states in the development of technological processes in order to make proactive decisions. The developed approach is based on a hybrid model based on the combination of an evidence-based classifier, fuzzy logic, and a Dempster–Shafer probabilistic scheme for evidence combination. The article presents a formal description of the predictor of critical states of the technological process. The resulting approach is universal and applicable in the automation of any complex technical systems. As an example, this article considers the application of the developed approach to solve the problem for assessing the safety of the technological process of shunting trains on a hump yard. The presented example shows the high efficiency and practical usefulness of the developed approach.

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Authors and Affiliations

  1. Rostov Branch of JSC “Scientific Research and Design Institute of Informatization, Automation and Communication in Railway Transport”, Rostov, Russia

    S. M. Kovalev, A. V. Sukhanov, I. A. Olgeyzer & K. I. Kornienko

  2. Rostov State Transport University, Rostov, Russia

    S. M. Kovalev, A. V. Sukhanov & I. A. Olgeyzer

  3. Emperor Alexander I St. Petersburg State Transport University, St. Petersburg, Russia

    K. I. Kornienko

Authors
  1. S. M. Kovalev
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  2. A. V. Sukhanov
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  3. I. A. Olgeyzer
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  4. K. I. Kornienko
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Corresponding authors

Correspondence to S. M. Kovalev, A. V. Sukhanov, I. A. Olgeyzer or K. I. Kornienko.

Additional information

This paper was recommended for publication by M.F. Karavai, a member of the Editorial Board

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Kovalev, S.M., Sukhanov, A.V., Olgeyzer, I.A. et al. Identification of Critical States of Technological Processes Based on Predictive Analytics Methods. Autom Remote Control 84, 424–433 (2023). https://doi.org/10.1134/S0005117923040100

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  • DOI: https://doi.org/10.1134/S0005117923040100

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