- Title
- Automated health condition diagnosis of in situ wood utility poles using an intelligent non-destructive evaluation (NDE) framework
- Creator
- Yu, Yang; Subhani, Mahbube; Hoshyar, Azadeh; Li, Jianchun; Li, Huan
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/174360
- Identifier
- vital:14887
- Identifier
-
https://doi.org/10.1142/S021945542042002X
- Identifier
- ISBN:0219-4554 (ISSN)
- Abstract
- Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney. © 2020 World Scientific Publishing Company.; This research is supported by Australian Research Council via Linkage Project (LP110200162) and Industrial Transforming Research Hub for Nanoscience Based Construction Materials Manufacturing (IH150100006) as well as Ausgrid. The authors greatly appreciate the ¯nancial and technical supports from the funding bodies.
- Publisher
- World Scientific
- Relation
- International Journal of Structural Stability and Dynamics Vol. 20, no. 10 (2020), p.
- Rights
- Metadata is freely available under a CCO license
- Rights
- Copyright @ World Scientific Publishing Company
- Rights
- Open Access
- Subject
- 0905 Civil Engineering; 0913 Mechanical Engineering; Advanced signal processing; Guided wave; Machine learning; Non-destructive evaluation; Wood electricity pole
- Full Text
- Reviewed
- Funder
- This research is supported by Australian Research Council via Linkage Project (LP110200162) and Industrial Transforming Research Hub for Nanoscience Based Construction Materials Manufacturing (IH150100006) as well as Ausgrid. The authors greatly appreciate the ¯nancial and technical supports from the funding bodies.
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