Application of the Integral Energy Criterion and Neural Network Model for Helicopter Turboshaft Engines’ Vibration Characteristics Analysis

Serhii Vladov, Maryna Bulakh (), Denys Baranovskyi, Eduard Kisiliuk, Victoria Vysotska, Maksym Romanov and Jan Czyżewski
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Serhii Vladov: Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine
Maryna Bulakh: Faculty of Mechanics and Technology, Rzeszow University of Technology, 4 Kwiatkowskiego Street, 37-450 Stalowa Wola, Poland
Denys Baranovskyi: Faculty of Mechanics and Technology, Rzeszow University of Technology, 4 Kwiatkowskiego Street, 37-450 Stalowa Wola, Poland
Eduard Kisiliuk: Management of the Scientific Activity Organization, Department of Education, Science and Sports, Ministry of Internal Affairs of Ukraine, 10 Akademika Bohomoltsia Street, 01601 Kyiv, Ukraine
Victoria Vysotska: Information Systems and Networks Department, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine
Maksym Romanov: Organizational and Scientific Department, Department of Education, Science and Sports, Ministry of Internal Affairs of Ukraine, 10 Akademika Bohomoltsia Street, 01601 Kyiv, Ukraine
Jan Czyżewski: Faculty of Mechanics and Technology, Rzeszow University of Technology, 4 Kwiatkowskiego Street, 37-450 Stalowa Wola, Poland

Energies, 2024, vol. 17, issue 22, 1-19

Abstract: This article presents a vibration signal analysis method to diagnose helicopter turboshaft engine defects such as bearing imbalance and wear. The scientific novelty of the article lies in the development of a comprehensive approach to diagnosing helicopter turboshaft engine defects based on the vibration signals amplitude and frequency characteristics integral analysis combined with a neural network for probabilistic defect detection. Unlike existing methods, the proposed approach uses the energy criterion for the vibration characteristics. It averages the assessment of unique signal processing algorithms, which ensures reliable defect classification under flight vibration conditions. The method is based on representing vibration signals as a sum of harmonic oscillations supplemented by noise components, which helps to identify deviations from typical values. The developed method includes a state function in which the amplitudes and frequency characteristics from nominal parameters estimate deviations. When the critical threshold is exceeded, the function signals possible malfunctions. A multilayer neural network is used to classify defect types, providing high classification accuracy (from 0.985 to 0.994). Computer experiments on the developed seminaturalistic modeling stand confirm that the method can detect increased vibration levels, which is the potential failure indicator. Comparative analysis shows the proposed method’s accuracy and noise resistance superiority, emphasizing the importance of introducing modern technologies to improve aircraft operation reliability and safety.

Keywords: helicopter turboshaft engine; integral energy criterion; vibration velocity; neural network; diagnostic; defect (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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