Development of a portable NDE system with advanced signal processing and machine learning for health condition diagnosis of in-service timber utility poles

Yu, Y., Li, J., Dackermann, U. and Subhani, M. 2017, Development of a portable NDE system with advanced signal processing and machine learning for health condition diagnosis of in-service timber utility poles, in ACMSM24 : Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials : Advancements and Challenges, CRC Press, Abingdon, Eng., pp. 1547-1552.

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Title Development of a portable NDE system with advanced signal processing and machine learning for health condition diagnosis of in-service timber utility poles
Author(s) Yu, Y.
Li, J.
Dackermann, U.
Subhani, M.ORCID iD for Subhani, M. orcid.org/0000-0001-9565-3271
Conference name Mechanics of Structures and Materials. Conference (24th : 2016 : Perth, Western Australia)
Conference location Perth, Western Australia
Conference dates 2016/12/06 - 2016/12/09
Title of proceedings ACMSM24 : Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials : Advancements and Challenges
Editor(s) Hao, H.
Zhang, C.
Publication date 2017
Conference series Mechanics of Structures and Materials Conference
Start page 1547
End page 1552
Total pages 6
Publisher CRC Press
Place of publication Abingdon, Eng.
Summary Aiming at current shortcomings of non-destructive evaluation (NDE) in health condition esti-mation of timber utility poles, this paper put forward a novel testing method via combination of a portable NDE system, advanced signal processing and machine learning techniques. Primarily, the multi-sensing strat-egy is employed and incorporated in current NDE technique to capture reflected stress wave signals, avoiding difficult interpretation of complicated wave propagation by only one sensor. Secondly, advanced signal pro-cessing methods, such as ensemble empirical mode decomposition (EEMD) and principal component analysis (PCA), are introduced to extract effective wave patterns that are sensitive to structural damage. Moreover, based on captured signal features, the state-of-the-art machine learning techniques are applied to implement the condition assessment. Finally, field testing results of 26 decommissioned timber poles at Mason Park in Sydney are used to validate the effectiveness of the proposed method.
ISBN 9781138029934
Language eng
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2017, Taylor & Francis Group
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090178

Document type: Conference Paper
Collection: School of Engineering
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