Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes15-14651
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions. 
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Volume 15 article 465 pages: 426 - 432

Alexey Grigoryevich Bulgakov
Southwest State University, Kursk, Russia

Tatyana Nikolaevna Kruglova
South-Russian State Polytechnic University, Novocherkassk, Rusia

In this article is proposed an intelligent method for diagnosing a technical condition, which makes it possible to distinguish a true malfunction of object from changing the parameters of its operating mode. As a result of numerous experiments has been revealed the dependence of measurement of wavelet transformation coefficients on the characteristic scales of a serviceable and faulty engine under different loading regimes. On the basis of the received information has been developed a neural classification network which makes it possible to reveal the current state of the object. Further studies have shown that any parent wavelet can be used to implement the proposed method. The study of the state of the drive under various loads confirms the correctness of the theoretical calculations and the adequacy of the model.

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