Technology of preflight intellectual engine diagnostics of unmanned aircraft

Authors

  • Віталій Юрійович Ларін National Aviation University, 03058, Kyiv, Kosmonavta Komarova 1, Ukraine

DOI:

https://doi.org/10.15587/2312-8372.2013.12955

Keywords:

unmanned aircraft, engine, vibroacoustic signal, informative frequencies, attribute system

Abstract

The article relates to the question of unmanned aviation, and aims to develop the technology of preflight intellectual diagnostics of engines of unmanned aircraft. On the basis of mathematical tools of fuzzy logic and artificial neural networks the signals converted using the discrete Fourier or wavelet transformations make up an attribute vector of a diagnosed engine. On the basis of the attribute vectors we form the information standards, which are the averaged characteristic of the state of the object. After that, we adapt the structure of the fuzzy neural network, which based on the analysis of the engine state by comparing the current data with the data of acoustic and vibration certificates, indicates a fault in a particular engine mount. The promising area is the use of data of vibration and acoustic certificates to correct the readings of course magnetometric sensors of navigation system of the unmanned aircraft with the engine diagnosed.

Author Biography

Віталій Юрійович Ларін, National Aviation University, 03058, Kyiv, Kosmonavta Komarova 1

Doctor of Technical Sciences, Professor

Department of Air Navigation Systems

References

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Published

2013-03-29

How to Cite

Ларін, В. Ю. (2013). Technology of preflight intellectual engine diagnostics of unmanned aircraft. Technology Audit and Production Reserves, 2(1(10), 27–30. https://doi.org/10.15587/2312-8372.2013.12955

Issue

Vol. 2 No. 1(10) (2013): Technology audit and production reserves

Section

Technology audit

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Copyright (c) 2016 Віталій Юрійович Ларін

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