Development of a smart system for diagnosing the operating conditions of a helicopter prototype via vibrations analysis




Vibration analysis; Helicopter; Artificial Neural Network; Forward flight.


In the forward flight, wind loads affect the helicopters and cause vibration. This paper analyzes the behavior of a helicopter prototype composed by two blades when subjected to a front wind load, similar to the forwarding flight condition. An Artificial Neural Network (ANN) processes the experimental data in order to identify the pattern of its dynamic behavior. The tests led to Vibration analysis for different wind speeds. Also, the data indicates that vibration amplitude increases when the blades are subjected to the fundamental frequency and its first harmonic on tests conducted without rotor plane tilt (hover flight). On the other hand, the second test performs a 5-degree tilt on the rotor disc. In this test, the vibration amplitude decreased in the fundamental frequency, and the amplitude related to the first harmonic increased. The ANN achieved 100% efficiency in recognizing the flight conditions of the prototype.


Bramwell, A. R. S., Done, G., & Balmford, D. (2001). Bramwell’s helicopter dynamics (Butterworth-Heinemann (ed.); Second). Butterworth-Heinemann.

Cavicchi, T. J. (1999). Digital Signal Processing. In John Wiley & Sons. JOHN WILEY & SONS.

CENIPA. (2017). Helicópteros sumário estatístico 2008-2017.

Ceruti, A., Liverani, A., & Recanatesi, L. (2011). Improving Helicopter Flight Simulation with Rotor Vibrations. Proceedings of the IMProVe 2011 International Conference on Innovative Methods in Product Design, 636–645.

RTCA DO-160C, Pub. L. No. RTCA DO-160C, 33 (2014).

Damy, L. F. (2006). Análise do Espectro de Frequência de Vibração da Aeronave Esquilo AS-355 F2. Istituto Tecnológico de Aeronáutica, São José dos Campos - SP.

De Gregorio, F. (2012). Flow field characterization and interactional aerodynamics analysis of a complete helicopter. Aerospace Science and Technology.

Doebelim, E. O. (1995). Engineering experimentation: planning, execution, reporting. Mcgraw-Hill Companies.

Dong, Y. (2018). An application of Deep Neural Networks to the in-flight parameter identification for detection and characterization of aircraft icing. Aerospace Science and Technology, 77, 34–49.

EASA. (2020). Annual Safety Review 2020.

EASA. (2021). Reduction in accidents caused by failures of critical rotor and rotor drive components through improved vibration health monitoring systems, RMT. 0711 (Issue 1). RMT.0711 Issue 1.pdf

FAA. (2012a). FAA-H-8083-21A: Helicopter Flying Handbook. In U. S. D. of Transportation (Ed.), FAA-H-8083-21A. FAA.

FAA. (2012b). Helicopter Flying Handbook (Federal Aviation Administration (ed.)). Federal Aviation Administration.

Ford, T. (1999). Vibration reduction and monitoring. Aircraft Engineering and Aerospace Technology, 71(1), 21–24.

Glowacz, A. (2018). Acoustic-based fault diagnosis of commutator motor. Electronics (Switzerland), 7(11).

Glowacz, A., Glowacz, W., Glowacz, Z., & Kozik, J. (2018). Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement: Journal of the International Measurement Confederation, 113(May 2017), 1–9.

Johnson, W. (1994). HELICOPTER THEORY (3rd ed.). Dover Publications.

Nguyen, D. H., Liu, Y., & Mori, K. (2018). Experimental Study for Aerodynamic Performance of Quadrotor Helicopter. Transactions of the Japan Society for Aeronautical and Space Sciences, 61(1), 29–39.

Pereira, A., Shitsuka, D., Parreira, F., & Shitsuka, R. (2018). Metodologia da Pesquisa Científica. In Metodologia da Pesquisa Científica. Santa Maria: UAB/NTEW/UFSM. Acesso em: 28 março 2020.

Sadegh, H., Mehdi, A. N., & Mehdi, A. (2016). Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm. Tribology International, 95, 426–434.

Sanz, J., Perera, R., & Huerta, C. (2012). Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks. Applied Soft Computing Journal, 12(9), 2867–2878.

Saravanan, N., & Ramachandran, K. I. (2010). Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Systems with Applications, 37(6), 4168–4181.

Silva, I. N., Spatti, D. H., & Flauzino, R. A. (2010). Redes Neurais Artificiais Para Engenharia e Ciências Aplicadas. In ArtLiber (Ed.), São Paulo: Artliber (2o). Artliber.

Straub, F. K., Anand, V. R., Lau, B. H., & Birchette, T. S. (2018). Wind tunnel test of the SMART active flap rotor. Journal of the American Helicopter Society, 63(1), 1–16.

Stupar, S., Simonović, A., & Jovanović, M. (2012). Measurement and Analysis of Vibrations on the Helicopter Structure in Order to Detect Defects of Operating Elements. Scientific Technical Review, 62(1), 58–63.

Vance, J., Zeidan, F., & Murphy, B. (2010). Machinery Vibration and Rotordynamics. John Wiley & Sons, Inc.

Waqar, T., & Demetgul, M. (2016). Thermal analysis MLP neural network based fault diagnosis on worm gears. Measurement: Journal of the International Measurement Confederation, 86, 56–66.

Wu, J. Da, & Chan, J. J. (2009). Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network. Expert Systems with Applications, 36(5), 8862–8875.

Zagaglia, D., Zanotti, A., & Gibertini, G. (2018). Analysis of the loads acting on the rotor of a helicopter model close to an obstacle in moderate windy conditions. Aerospace Science and Technology, 78, 580–592.




How to Cite

OLIVEIRA NETO, J. M. de; OLIVEIRA, A. G. .; FIRMINO, J. V. L. de C.; RODRIGUES, M. C. .; SILVA, A. A.; CARVALHO, L. H. de. Development of a smart system for diagnosing the operating conditions of a helicopter prototype via vibrations analysis . Research, Society and Development, [S. l.], v. 10, n. 12, p. e304101220546, 2021. DOI: 10.33448/rsd-v10i12.20546. Disponível em: Acesso em: 24 feb. 2024.