Author: Wu, Bing
Title: Optimization of sparse sensor networks for damage identification using binary particle swarm optimization
Degree: M.Sc.
Year: 2014
Subject: Structural health monitoring
Sensor networks
Hong Kong Polytechnic University -- Dissertations
Department: Faculty of Engineering
Pages: ix, 70 leaves : ill. (some col) ; 30 cm.
Language: English
Abstract: The sparse active sensor network is a thriving technique to facilitate the realization of guided-wave-based structural health monitoring (SHM). To achieve a minimal consumption of sensors with their respective optimal locations in a sparse sensor network but not at the cost of sacrificing adequate detection resolution, a sensor network design approach was developed in this study, based on binary particle swarm optimization (BPSO). To start with, Lamb waves were activated and acquired with an active sensor network first, with a series of damage scenarios sequentially appearing (mono-damage for each scenario). The optimization of the sensor network was projected to an objective function, and the minimal number of sensors requested to detect the mono-damage in each damage scenario and their respective locations were achieved by minimizing such an objective function using the BPSO. A delay-and-sum imaging algorithm was additionally introduced to present the identification results in intuitive images. Notably, the proposed optimization algorithm has been demonstrated capable of handling the sensor number and sensor location simultaneously. To validate the algorithm experimentally, the approach was used to detect up to ten stochastic damage cases in an irregular aluminum plate (to simulate a typical airplane wing section), and the results have shown the effectiveness of the approach.
Rights: All rights reserved
Access: restricted access

Files in This Item:
File Description SizeFormat 
b2748967x.pdfFor All Users (off-campus access for PolyU Staff & Students only)1.93 MBAdobe PDFView/Open

Copyright Undertaking

As a bona fide Library user, I declare that:

  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.

By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

Show full item record

Please use this identifier to cite or link to this item: