The Helicopter Turboshaft Engine’s Reconfigured Dynamic Model for Functional Safety Estimation

Author:

Vladov Serhii1ORCID,Kovtun Viacheslav2ORCID,Sokurenko Valerii3,Muzychuk Oleksandr3,Vysotska Victoria45ORCID

Affiliation:

1. Department of Scientific Work Organization and Gender Issues, Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6 Peremohy Street, 39605 Kremenchuk, Ukraine

2. Internet of Things Group, Institute of Theoretical and Applied Informatics Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland

3. Kharkiv National University of Internal Affairs, 27 L. Landau Avenue, 61080 Kharkiv, Ukraine

4. Information Systems and Networks Department, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine

5. Institute of Computer Science, Osnabrück University, 1 Friedrich-Janssen-Street, 49076 Osnabrück, Germany

Abstract

This research substantiates the necessity for developing and implementing structural reconfiguration methods for automatic control systems in the event of a parametric sensor failure to enhance the helicopter turboshaft engine’s overall reliability and safety. The research aim is the substantiation of the helicopter turboshaft engine’s mathematically reconfigured automatic control system in the event of the failure of a standard sensor, which will ensure the helicopter turboshaft engine’s stable operation under failure conditions, minimizing the impact on engine control and performance. A theorem was developed and proven concerning the reconfiguration of the helicopter turboshaft engine’s automatic control system structure, defining the system’s new mathematical form using nonlinear thermogas-dynamic parameters. A method was proposed to determine the values of these parameters that keep the reconfigured control system stable. This method uses numerical optimization to find the best thermogas-dynamic parameters to ensure system stability. Experimental results showed that for slow changes, using parameters from the previous step works best, while for fast changes, restarting is more effective due to significant differences in the system states. The accuracy of the proposed mathematical model for the reconfigured control system was confirmed through mean square error analysis (within 0.4% and 0.77% under white noise), regression analysis (with a determination coefficient of 0.986), and cross-validation (with a metric deviation from the maximum mean square error of 3.88%).

Publisher

MDPI AG

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