| 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 | Size | Format | |
|---|---|---|---|---|
| b2748967x.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.93 MB | Adobe PDF | View/Open |
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