A machine‐learning approach to load‐monitoring based on guided waves

A machine‐learning approach to load‐monitoring based on guided waves The research field of structural health monitoring (SHM) describes the way to assess the structural integrity. The research objectives are broadly diversified, which are all addressing the improvement of SHM technologies. It is important to optimise the capability of sensor networks by applying intelligent signal processing models. These intelligent systems predict the health state based on acquired data in a real‐time environment. Piezoelectric sensor networks and guided‐wave based analyses are combined with machine learning to create an efficient load‐monitoring routine. This load‐monitoring approach is chosen to track any changes in the environmental conditions, which causes most of the problems in reference signal based damage detection. Therefore, the proposed load‐monitoring method can be used to compensate the influence of external changes on the acquired signals and improve the performance and robustness of future SHM systems. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings in Applied Mathematics & Mechanics Wiley

A machine‐learning approach to load‐monitoring based on guided waves

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2017 Wiley Subscription Services
ISSN
1617-7061
eISSN
1617-7061
D.O.I.
10.1002/pamm.201710123
Publisher site
See Article on Publisher Site

Abstract

The research field of structural health monitoring (SHM) describes the way to assess the structural integrity. The research objectives are broadly diversified, which are all addressing the improvement of SHM technologies. It is important to optimise the capability of sensor networks by applying intelligent signal processing models. These intelligent systems predict the health state based on acquired data in a real‐time environment. Piezoelectric sensor networks and guided‐wave based analyses are combined with machine learning to create an efficient load‐monitoring routine. This load‐monitoring approach is chosen to track any changes in the environmental conditions, which causes most of the problems in reference signal based damage detection. Therefore, the proposed load‐monitoring method can be used to compensate the influence of external changes on the acquired signals and improve the performance and robustness of future SHM systems. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Journal

Proceedings in Applied Mathematics & MechanicsWiley

Published: Jan 1, 2017

References

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