Technical diagnostics and monitoring based on capabilities of wavelet transforms and relaxation neural networks
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.
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. rusavaha.ru
Publication date: 01 March 2008
- Official Journal of The British Institute of Non-Destructive Testing - includes original research and development papers, technical and scientific reviews and case studies in the fields of NDT and CM.
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Information for Advertisers
- Terms & Conditions
- Ingenta Connect is not responsible for the content or availability of external websites
Receive new issue alert- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content