Исследование вибрации микроавиадвигателей на основе метода аналитического анализа вейвлет-преобразования
Ключевые слова:
микроавиадвигатель, вибрационный сигнал, аналитическое вейвлет-преобразование, неисправность, диагностика
Аннотация
Для всестороннего и эффективного анализа сигналов вибрации микроавиадвигателя необходимо обеспечить диагностику неисправностей модели оборудования. С помощью метода анализа вейвлет-преобразования проведено глубокое изучение вибрационной ситуации миниатюрного авиационного двигателя для достижения цели управления здоровьем. С учетом того, что аналитическое вейвлет-преобразование принято в качестве теоретической основы, соответствующий инструмент обработки сигналов разработан с помощью платформы Labview2022, используемой для анализа собранных сигналов вибрации. Успешно реализовав поставленные цели, данные продукты применяются в инженерной практике для реализации концепции дизайна.
Литература
1. Magnuson S. Small Drone Threat Grows More Complex, Deadly as Tech Advances // National Defense. 2023. V. 8(23).
2. Romesis Ch., Aretakis N., Mathioudakis K. Model-assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis // Aerospace. 2024. V. 11(11). Pp. 913—913.
3. Patil Ch. e. a. Investigation of Logarithmic Signatures for Feature Extraction and Application to Marine Engine Fault Diagnosis // Eng. Appl. Artificial Intelligence. 2024. V. 138. Pp. 109299—109299.
4. Bao Mengyao, Ding Shuiting, Li Guo. Identification of Key Factors Affecting the Failure of Aviation Piston Engine Turbochargers Based on an Improved Correspondence Analysis-polar Angle-based Classification // Chinese J. Aeronautics. 2021. V. 34(6). Pp. 1—26.
5. Jamali H.U. e. a. Frictional Power Loss in Journal Bearing Considering Parabolic Shape for the Bearing Edges under Misalignment // Advances in Mechanical Eng. 2024. V. 16(9). Pp. 1—9.
6. Eickhoff M. e. a.Compensating Thermal Bending of the Runner Disk in Hydrodynamic Thrust Bearings: Simple Approach for Passively Improving the Performance of Gas Bearings // Tribology Intern. 2024. V. 196. P. 109632.
7. Zhang Yingqiang e. a. Study on the Transition Mechanism of Vibrating Low-pressure Turbine Blades Based on Large Eddy Simulation // Aerospace Sci. and Technol. 2024. V. 155(2). Pp. 109695—109695.
8. Balitskii O.I. e. a. Fatigue Fracture of the Blades of Gas-turbine Engines Made of a New Refractory Nickel Alloy // Materials Sci. 2022. V. 57(4). Pp. 475—483.
9. Jian Wei e. a. Study on the Prediction of High-speed Rotary Lip Seal Wear in Aero-engine Based on Heat-fluid-solid Coupling // Industrial Lubrication and Tribology. 2024. V. 76(2). Pp. 167—177.
10. Dragos F.C. e. a.Condition Monitoring of a Three-phase AC Asynchronous Motor Based on the Analysis of the Instantaneous Active Electrical Power in No-load Tests // Appl. Sci. 2024. V. 14(14). Pp. 6124—6124.
11. Aahan Tyagi, Vivek Kumar Singh, Ram Bilas Pachori. FBSE-EWT Technique-based Complex-valued Signal Analysis // Circuits, Systems, and Signal Proc. 2025. V. 44. Pp. 1349—1370.
12. Dada Saheb Ramteke e. a. Automated Gearbox Fault Diagnosis Using Entropy-based Features in Flexible Analytic Wavelet Transform (FAWT) Domain // J. Vibration Eng. & Technol. 2021. V. 9(7). Pp. 1—11.
13. Bruni V., Pelosi F., Vitulano D. Fractal Properties of 4-point Interpolatory Subdivision Schemes and Wavelet Scattering Transform for Signal Classification // Appl. Numerical Math. 2025. V. 208. Pp. 256—270.
14. Hao Bai e. a. Explainable Incremental Learning for High-impedance Fault Detection in Distribution Networks // Computers and Electrical Eng. 2025. V. 122. P. 110006.
15. Shihang Yu. e. a. ANC-Net: a Novel Multi-scale Active Noise Cancellation Network for Rotating Machinery Fault Diagnosis Based on Discrete Wavelet Transform // Expert Syst. with Appl. 2025. V. 265. P. 125937.
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Для цитирования: Май Синь,Хуэй Цинь, Чжифэн Е. Исследование вибрации микроавиадвигателей на основе метода аналитического анализа вейвлет-преобразования // Вестник МЭИ. 2025. № 4. С. 112—122. DOI: 10.24160/1993-6982-2025-4-112-122
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Исследование выполнено при поддержке Национального фонда естественных наук Китая
Авторы выражают благодарность за поддержку, оказанную Нанкинским университетом аэронавтики и астронавтики
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Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
#
1. Magnuson S. Small Drone Threat Grows More Complex, Deadly as Tech Advances. National Defense. 2023;8(23).
2. Romesis Ch., Aretakis N., Mathioudakis K. Model-assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis. Aerospace. 2024;11(11):913—913.
3. Patil Ch. e. a. Investigation of Logarithmic Signatures for Feature Extraction and Application to Marine Engine Fault Diagnosis. Eng. Appl. Artificial Intelligence. 2024;138:109299—109299.
4. Bao Mengyao, Ding Shuiting, Li Guo. Identification of Key Factors Affecting the Failure of Aviation Piston Engine Turbochargers Based on an Improved Correspondence Analysis-polar Angle-based Classification. Chinese J. Aeronautics. 2021;34(6):1—26.
5. Jamali H.U. e. a. Frictional Power Loss in Journal Bearing Considering Parabolic Shape for the Bearing Edges under Misalignment. Advances in Mechanical Eng. 2024;16(9):1—9.
6. Eickhoff M. e. a.Compensating Thermal Bending of the Runner Disk in Hydrodynamic Thrust Bearings: Simple Approach for Passively Improving the Performance of Gas Bearings. Tribology Intern. 2024;196:109632.
7. Zhang Yingqiang e. a. Study on the Transition Mechanism of Vibrating Low-pressure Turbine Blades Based on Large Eddy Simulation. Aerospace Sci. and Technol. 2024;155(2):109695—109695.
8. Balitskii O.I. e. a. Fatigue Fracture of the Blades of Gas-turbine Engines Made of a New Refractory Nickel Alloy. Materials Sci. 2022;57(4):475—483.
9. Jian Wei e. a. Study on the Prediction of High-speed Rotary Lip Seal Wear in Aero-engine Based on Heat-fluid-solid Coupling. Industrial Lubrication and Tribology. 2024;76(2):167—177.
10. Dragos F.C. e. a.Condition Monitoring of a Three-phase AC Asynchronous Motor Based on the Analysis of the Instantaneous Active Electrical Power in No-load Tests. Appl. Sci. 2024;14(14):6124—6124.
11. Aahan Tyagi, Vivek Kumar Singh, Ram Bilas Pachori. FBSE-EWT Technique-based Complex-valued Signal Analysis. Circuits, Systems, and Signal Proc. 2025;44:1349—1370.
12. Dada Saheb Ramteke e. a. Automated Gearbox Fault Diagnosis Using Entropy-based Features in Flexible Analytic Wavelet Transform (FAWT) Domain. J. Vibration Eng. & Technol. 2021;9(7):1—11.
13. Bruni V., Pelosi F., Vitulano D. Fractal Properties of 4-point Interpolatory Subdivision Schemes and Wavelet Scattering Transform for Signal Classification. Appl. Numerical Math. 2025;208:256—270.
14. Hao Bai e. a. Explainable Incremental Learning for High-impedance Fault Detection in Distribution Networks. Computers and Electrical Eng. 2025;122:110006.
15. Shihang Yu. e. a. ANC-Net: a Novel Multi-scale Active Noise Cancellation Network for Rotating Machinery Fault Diagnosis Based on Discrete Wavelet Transform. Expert Syst. with Appl. 2025;265:125937
---
For citation: Mai Xin, Hui Qin, Zhifeng Ye. A Study of Micro Aircraft Engine Vibration Based on the Analytical Wavelet Transform Analysis Method. Bulletin of MPEI. 2025;4:112—122. (in Russian). DOI: 10.24160/1993-6982-2025-4-112-122
---
The Study was Supported by the National Natural Science Foundation of China
The Authors would Like to Express their Gratitude for the Support Provided by Nanjing University of Aeronautics and Astronautics
---
Conflict of interests: the authors declare no conflict of interest
2. Romesis Ch., Aretakis N., Mathioudakis K. Model-assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis // Aerospace. 2024. V. 11(11). Pp. 913—913.
3. Patil Ch. e. a. Investigation of Logarithmic Signatures for Feature Extraction and Application to Marine Engine Fault Diagnosis // Eng. Appl. Artificial Intelligence. 2024. V. 138. Pp. 109299—109299.
4. Bao Mengyao, Ding Shuiting, Li Guo. Identification of Key Factors Affecting the Failure of Aviation Piston Engine Turbochargers Based on an Improved Correspondence Analysis-polar Angle-based Classification // Chinese J. Aeronautics. 2021. V. 34(6). Pp. 1—26.
5. Jamali H.U. e. a. Frictional Power Loss in Journal Bearing Considering Parabolic Shape for the Bearing Edges under Misalignment // Advances in Mechanical Eng. 2024. V. 16(9). Pp. 1—9.
6. Eickhoff M. e. a.Compensating Thermal Bending of the Runner Disk in Hydrodynamic Thrust Bearings: Simple Approach for Passively Improving the Performance of Gas Bearings // Tribology Intern. 2024. V. 196. P. 109632.
7. Zhang Yingqiang e. a. Study on the Transition Mechanism of Vibrating Low-pressure Turbine Blades Based on Large Eddy Simulation // Aerospace Sci. and Technol. 2024. V. 155(2). Pp. 109695—109695.
8. Balitskii O.I. e. a. Fatigue Fracture of the Blades of Gas-turbine Engines Made of a New Refractory Nickel Alloy // Materials Sci. 2022. V. 57(4). Pp. 475—483.
9. Jian Wei e. a. Study on the Prediction of High-speed Rotary Lip Seal Wear in Aero-engine Based on Heat-fluid-solid Coupling // Industrial Lubrication and Tribology. 2024. V. 76(2). Pp. 167—177.
10. Dragos F.C. e. a.Condition Monitoring of a Three-phase AC Asynchronous Motor Based on the Analysis of the Instantaneous Active Electrical Power in No-load Tests // Appl. Sci. 2024. V. 14(14). Pp. 6124—6124.
11. Aahan Tyagi, Vivek Kumar Singh, Ram Bilas Pachori. FBSE-EWT Technique-based Complex-valued Signal Analysis // Circuits, Systems, and Signal Proc. 2025. V. 44. Pp. 1349—1370.
12. Dada Saheb Ramteke e. a. Automated Gearbox Fault Diagnosis Using Entropy-based Features in Flexible Analytic Wavelet Transform (FAWT) Domain // J. Vibration Eng. & Technol. 2021. V. 9(7). Pp. 1—11.
13. Bruni V., Pelosi F., Vitulano D. Fractal Properties of 4-point Interpolatory Subdivision Schemes and Wavelet Scattering Transform for Signal Classification // Appl. Numerical Math. 2025. V. 208. Pp. 256—270.
14. Hao Bai e. a. Explainable Incremental Learning for High-impedance Fault Detection in Distribution Networks // Computers and Electrical Eng. 2025. V. 122. P. 110006.
15. Shihang Yu. e. a. ANC-Net: a Novel Multi-scale Active Noise Cancellation Network for Rotating Machinery Fault Diagnosis Based on Discrete Wavelet Transform // Expert Syst. with Appl. 2025. V. 265. P. 125937.
---
Для цитирования: Май Синь,Хуэй Цинь, Чжифэн Е. Исследование вибрации микроавиадвигателей на основе метода аналитического анализа вейвлет-преобразования // Вестник МЭИ. 2025. № 4. С. 112—122. DOI: 10.24160/1993-6982-2025-4-112-122
---
Исследование выполнено при поддержке Национального фонда естественных наук Китая
Авторы выражают благодарность за поддержку, оказанную Нанкинским университетом аэронавтики и астронавтики
---
Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
#
1. Magnuson S. Small Drone Threat Grows More Complex, Deadly as Tech Advances. National Defense. 2023;8(23).
2. Romesis Ch., Aretakis N., Mathioudakis K. Model-assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis. Aerospace. 2024;11(11):913—913.
3. Patil Ch. e. a. Investigation of Logarithmic Signatures for Feature Extraction and Application to Marine Engine Fault Diagnosis. Eng. Appl. Artificial Intelligence. 2024;138:109299—109299.
4. Bao Mengyao, Ding Shuiting, Li Guo. Identification of Key Factors Affecting the Failure of Aviation Piston Engine Turbochargers Based on an Improved Correspondence Analysis-polar Angle-based Classification. Chinese J. Aeronautics. 2021;34(6):1—26.
5. Jamali H.U. e. a. Frictional Power Loss in Journal Bearing Considering Parabolic Shape for the Bearing Edges under Misalignment. Advances in Mechanical Eng. 2024;16(9):1—9.
6. Eickhoff M. e. a.Compensating Thermal Bending of the Runner Disk in Hydrodynamic Thrust Bearings: Simple Approach for Passively Improving the Performance of Gas Bearings. Tribology Intern. 2024;196:109632.
7. Zhang Yingqiang e. a. Study on the Transition Mechanism of Vibrating Low-pressure Turbine Blades Based on Large Eddy Simulation. Aerospace Sci. and Technol. 2024;155(2):109695—109695.
8. Balitskii O.I. e. a. Fatigue Fracture of the Blades of Gas-turbine Engines Made of a New Refractory Nickel Alloy. Materials Sci. 2022;57(4):475—483.
9. Jian Wei e. a. Study on the Prediction of High-speed Rotary Lip Seal Wear in Aero-engine Based on Heat-fluid-solid Coupling. Industrial Lubrication and Tribology. 2024;76(2):167—177.
10. Dragos F.C. e. a.Condition Monitoring of a Three-phase AC Asynchronous Motor Based on the Analysis of the Instantaneous Active Electrical Power in No-load Tests. Appl. Sci. 2024;14(14):6124—6124.
11. Aahan Tyagi, Vivek Kumar Singh, Ram Bilas Pachori. FBSE-EWT Technique-based Complex-valued Signal Analysis. Circuits, Systems, and Signal Proc. 2025;44:1349—1370.
12. Dada Saheb Ramteke e. a. Automated Gearbox Fault Diagnosis Using Entropy-based Features in Flexible Analytic Wavelet Transform (FAWT) Domain. J. Vibration Eng. & Technol. 2021;9(7):1—11.
13. Bruni V., Pelosi F., Vitulano D. Fractal Properties of 4-point Interpolatory Subdivision Schemes and Wavelet Scattering Transform for Signal Classification. Appl. Numerical Math. 2025;208:256—270.
14. Hao Bai e. a. Explainable Incremental Learning for High-impedance Fault Detection in Distribution Networks. Computers and Electrical Eng. 2025;122:110006.
15. Shihang Yu. e. a. ANC-Net: a Novel Multi-scale Active Noise Cancellation Network for Rotating Machinery Fault Diagnosis Based on Discrete Wavelet Transform. Expert Syst. with Appl. 2025;265:125937
---
For citation: Mai Xin, Hui Qin, Zhifeng Ye. A Study of Micro Aircraft Engine Vibration Based on the Analytical Wavelet Transform Analysis Method. Bulletin of MPEI. 2025;4:112—122. (in Russian). DOI: 10.24160/1993-6982-2025-4-112-122
---
The Study was Supported by the National Natural Science Foundation of China
The Authors would Like to Express their Gratitude for the Support Provided by Nanjing University of Aeronautics and Astronautics
---
Conflict of interests: the authors declare no conflict of interest
Опубликован
2025-06-24
Выпуск
Раздел
Турбомашины и поршневые двигатели (технические науки) (2.4.7)
