On self-organising diagnostics in impact sensing networks

Authors:
Mikhail Prokopenko;Peter Wang;Andrew Scott;Vadim Gerasimov;Nigel Hoschke;Don Price
Affiliations:
CSIRO Information and Communication Technology Centre, North Ryde, Australia;CSIRO Information and Communication Technology Centre, North Ryde, Australia;CSIRO Industrial Physics, North Ryde, Australia;CSIRO Information and Communication Technology Centre, North Ryde, Australia;CSIRO Industrial Physics, North Ryde, Australia;CSIRO Industrial Physics, North Ryde, Australia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

Citing 3
Cited 0

Self-organising impact boundaries in ageless aerospace vehicles

AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Robust clustering of acoustic emission signals using the Kohonen network

ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
A hybrid classification approach to ultrasonic shaft signals

AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Structural health management (SHM) of safety-critical structures requires multiple capabilities: sensing, assessment, diagnostics, prognostics, repair, etc. This paper presents a capability for self-organising diagnosis by a group of autonomous sensing agents in a distributed sensing and processing SHM network. The diagnostics involves acoustic emission waves emitted as a result of a sudden release of energy during impacts and detected by the multi-agent network. Several diagnostic techniques identifying the nature and severity of damage at multiple sites are investigated, and the self-organising maps (Kohonen neural networks) are shown to outperform the standard k-means algorithm in both time- and frequency domains.