Limitation of the Lateral Angled Broadband Low Frequency Impact Excitation on the Non-Destructive Condition Assessment of the Timber Utility Poles
- Publisher:
- IJoAT Foundation
- Publication Type:
- Journal Article
- Citation:
- International Journal of Advancements in Technology, 2017, 8, (4), pp. 1-8
- Issue Date:
- 2017-01-01
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Timber utility poles play a significant role in the infrastructure of Australia as well as many other countries for power
distribution and communication networks. Due to the advanced age of Australia’s timber pole infrastructure, substantial
efforts are undertaken on maintenance and asset management to avoid any failures of the utility lines. Nevertheless,
the lack of reliable tools for assessing the condition of in-service poles seriously jeopardizes the maintenance and
asset management. For instance, each year approximately 300,000 poles are replaced in the Eastern States of
Australia with up to 80% of them still being in a very good condition, resulting in major waste of natural resources and
money. Non-destructive testing (NDT) methods based on stress wave propagation can potentially offer simple and
cost-effective tools for identifying the in-service condition of timber poles. Nonetheless, most of the currently available
methods are not appropriate for condition assessment of timber poles in-service due to presence of uncertainties
such as complicated material properties, environmental conditions, interaction of soil and structure, and an impact
excitation type. In order to address these complexities, advanced digital signal processing methodologies are needed
to be employed. Deterministic signal separation, blind signal separation, and frequency-wavenumber velocity filtering
are the three groups of methodologies, which could most probably provide solutions. In this paper applicability and
effectiveness of the blind signal separation methods is investigated through a numerical data obtained from of a timber
pole modelled with both isotropic and orthotropic material properties. Principal Component Analysis (PCA), Singular
Value Decomposition (SVD), and K-means clustering algorithms are the blind signal separation methodologies that
are employed in this research work.
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