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Non-Invasive Monitoring of the Technical Condition of Power Units Using the FAM-C and FDM-A Electrical Methods

Author

Listed:
  • Mariusz Zieja

    (Logistic Support Department, Air Force Institute of Technology, 6 Ksiecia Boleslawa Street, 01-495 Warsaw, Poland)

  • Andrzej Gębura

    (Department of Avionics, Air Force Institute of Technology, 6 Ksiecia Boleslawa Street, 01-495 Warsaw, Poland)

  • Andrzej Szelmanowski

    (Department of Avionics, Air Force Institute of Technology, 6 Ksiecia Boleslawa Street, 01-495 Warsaw, Poland)

  • Bartłomiej Główczyk

    (Logistic Support Department, Air Force Institute of Technology, 6 Ksiecia Boleslawa Street, 01-495 Warsaw, Poland)

Abstract

This article presents the selected results of analytical and structural work conducted at the Air Force Institute of Technology (pl. ITWL) in the field of building a measuring apparatus for non-invasive monitoring of the technical condition of aircraft power units. Presented innovative FAM-C and FDM-A methods allow for observation of frequency modulation parameters as well as identification and diagnostic classification of particular mechanical subassemblies supplying the on-board generator and thus enable non-invasive monitoring of technical condition of the aircraft power unit and the aircraft propulsion system. The main purpose of this article is to present the measurement apparatus errors that occur in the signal conditioning system in the FAM-C and FDM-A methods. In spite of the fact that the measuring instrument was built on the basis of digital technology and that it uses typical solutions of electronic frequency measurement, due to the specificity of the applied diagnostic method there occur specific measuring errors which are presented in this article.

Suggested Citation

  • Mariusz Zieja & Andrzej Gębura & Andrzej Szelmanowski & Bartłomiej Główczyk, 2021. "Non-Invasive Monitoring of the Technical Condition of Power Units Using the FAM-C and FDM-A Electrical Methods," Sustainability, MDPI, vol. 13(23), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13329-:d:693069
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    References listed on IDEAS

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    1. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    2. Ahmad, Wasim & Khan, Sheraz Ali & Islam, M M Manjurul & Kim, Jong-Myon, 2019. "A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 67-76.
    3. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
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