Genetic algorithm-support vector regression for high reliability SHM system based on FBG sensor network |
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Authors: | XiaoLi Zhang DaKai LiangJie Zeng Anand Asundi |
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Institution: | a State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Baixia district, Nanjing 210016, China b School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore |
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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. |
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Keywords: | Structural health monitoring Genetic algorithm Support vector regression FBG sensor network Reliability |
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