On self-organising diagnostics in impact sensing networks

Mikhail Prokopenko*, Peter Wang, Andrew Scott, Vadim Gerasimov, Nigel Hoschke, Don Price

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
EditorsRajiv Khosla, Robert J. Howlett, Lakhmi C. Jain
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Number of pages9
Volume3684 LNAI
ISBN (Print)354028897X, 9783540288978
Publication statusPublished - 2005
Externally publishedYes
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: 14 Sept 200516 Sept 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3684 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


Other9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005


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