Abstract
Acoustic emission-based techniques are promising for nondestructive inspection of mechanical systems. For reliable automatic fault monitoring, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. In this paper we propose the application of the Kohonen network for this purpose. The principal components of the short-time Fourier transforms of the data were applied input of the network. The clustering results confirm the capability of the Kohonen network for reliable source identification of acoustic emission signals, assuming enough care has been taken in implementing the training algorithm of the network.
| Original language | English (US) |
|---|---|
| Title of host publication | Design and Implementation of Signal Processing SystemNeural Networks for Signal Processing Signal Processing EducationOther Emerging Applications of Signal ProcessingSpecial Sessions |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3891-3894 |
| Number of pages | 4 |
| ISBN (Electronic) | 0780362934 |
| DOIs | |
| State | Published - 2000 |
| Event | 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 - Istanbul, Turkey Duration: Jun 5 2000 → Jun 9 2000 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| Volume | 6 |
| ISSN (Print) | 1520-6149 |
Other
| Other | 25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 |
|---|---|
| Country/Territory | Turkey |
| City | Istanbul |
| Period | 6/5/00 → 6/9/00 |
Bibliographical note
Publisher Copyright:© 2000 IEEE.