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Technical diagnostics and monitoring based on capabilities of wavelet transforms and relaxation neural networks

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A technology for technical diagnostics based on the multivariate analysis of system status is under consideration. It combines the capabilities of wavelet transforms and relaxation neural networks. Important practical advantages of the proposed technique include the capability to recognise damage using only a few training patterns and the possibility to apply the method for non-stationary input diagnostic signals. Recognition of damage in dynamic suppressors attached to an aircraft panel is presented as an example.
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Keywords: Technical diagnostics; aircraft damage; non-stationary signals; relaxation neural networks; wavelet transforms

Document Type: Research Article

Affiliations: Russian Aviation Co, Research Group, 33/50 Klara Tsetkin Street, 125130 Moscow, Russia and the Moscow State University of Psychology and Education, 29 Sretenka Street, 127051 Moscow, Russia.

Publication date: March 1, 2008

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