Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98557
Title: Genetic algorithm-support vector regression for high reliability SHM system based on FBG sensor network
Authors: Zhang, Xiao Li
Liang, Da Kai
Zeng, Jie
Asundi, Anand Krishna
Keywords: DRNTU::Engineering::Mechanical engineering
Issue Date: 2011
Source: Zhang, X. L., Liang, D. K., Zeng, J., & Asundi, A. K. (2011). Genetic algorithm-support vector regression for high reliability SHM system based on FBG sensor network. Optics and lasers in engineering, 50(2), 148-153.
Series/Report no.: Optics and lasers in engineering
Abstract: Structural Health Monitoring (SHM) based on Fiber Bragg Grating (FBG) sensor network has attracted considerable attention in recent years. However, FBG sensor network is embedded or glued in the structure simply with series or parallel. In this case, if optic fiber sensors or fiber nodes fail, the fiber sensors cannot be sensed behind the failure point. Therefore, for improving the survivability of the FBG-based sensor system in the SHM, it is necessary to build high reliability FBG sensor network for the SHM engineering application. In this study, a model reconstruction soft computing recognition algorithm based on genetic algorithm-support vector regression (GA-SVR) is proposed to achieve the reliability of the FBG-based sensor system. Furthermore, an 8-point FBG sensor system is experimented in an aircraft wing box. The external loading damage position prediction is an important subject for SHM system; as an example, different failure modes are selected to demonstrate the SHM system's survivability of the FBG-based sensor network. Simultaneously, the results are compared with the non-reconstruct model based on GA-SVR in each failure mode. Results show that the proposed model reconstruction algorithm based on GA-SVR can still keep the predicting precision when partial sensors failure in the SHM system; thus a highly reliable sensor network for the SHM system is facilitated without introducing extra component and noise.
URI: https://hdl.handle.net/10356/98557
http://hdl.handle.net/10220/13671
DOI: 10.1016/j.optlaseng.2011.09.015
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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