Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61744
Title: A sparse sensor network topologized for cylindrical wave-based identification of damage in pipeline structures
Authors: Wang, Q
Hong, M
Su, Z 
Keywords: Cylindrical waves
Nondestructive damage evaluation
Pipeline structures
Sparse sensor network
Issue Date: 2016
Publisher: Institute of Physics Publishing
Source: Smart materials and structures, 2016, v. 25, no. 7, 075015 How to cite?
Journal: Smart materials and structures 
Abstract: A sparse sensor network, based on the concept of semi-decentralized and standardized sensing, is developed, to actively excite and acquire cylindrical waves for damage identification and health monitoring of pipe structures. Differentiating itself from conventional 'ring-style' transducer arrays which attempt to steer longitudinal axisymmetric cylindrical waves via transducer synchronism, this sparse sensor network shows advantages in some aspects, including the use of fewer sensors, simpler manipulation, quicker configuration, less mutual dependence among sensors, and an improved signal-to-noise ratio. The sparse network is expanded topologically, aimed at eliminating the presence of 'blind zones' and the challenges associated with multi-path propagation of cylindrical waves. Theoretical analysis is implemented to comprehend propagation characteristics of waves guided by a cylindrical structure. A probability-based diagnostic imaging algorithm is introduced to visualize damage in pixelated images in an intuitive, prompt, and automatic manner. A self-contained health monitoring system is configured for experimental validation, via which quantitative identification of mono- and multi-damage in a steel cylinder is demonstrated. The results highlight an expanded sensing coverage of the sparse sensor network and its enhanced capacity of acquiring rich information, avoiding the cost of augmenting the number of sensors in a sensor network.
URI: http://hdl.handle.net/10397/61744
ISSN: 0964-1726
EISSN: 1361-665X
DOI: 10.1088/0964-1726/25/7/075015
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