Authors:
Natasha Hamilton
1
;
Jim Harkin
1
;
Liam McDaid
1
;
Junxiu Liu
1
and
Eoghan Furey
2
Affiliations:
1
School of Computing, Engineering and Intelligent Systems, Ulster University, Northland Road, Derry, U.K.
;
2
Department of Computing, Atlantic Technological University, Port Road, Donegal, Ireland
Keyword(s):
Structural Health Monitoring, Feature Extraction, Spiking Neural Networks, Classification.
Abstract:
Failure of large complex structures such as buildings and bridges can have monumental repercussions such as human mortality, environmental destruction and economic consequences. It is therefore paramount that detection of structural damage or anomalies are identified and managed early. This highlights the need to develop automated Structural Health Monitoring (SHM) systems that can continuously allow the safety status of structures to be determined, even in the worst and most isolated conditions, to ultimately help prevent destruction and save lives. Signal processing is a crucial step to detecting structural anomalies and recent work demonstrates the opportunities for neural networks, however the encoding of data for SHM requires the extraction of features due to often, noisy data. This paper focuses on feature extraction methods for artificial neural networks (ANNs) and spiking neural networks (SNNs) and aims to identify bespoke features which enable SNNs to encode data and perform
the classification of anomalies. Results show that extraction of particular features in large real-world applications improve the classification accuracy of SNNs.
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