A soft self-repairing for FBG sensor network in SHM system based on PSO–SVR model reconstruction

Abstract

Structural health monitoring (SHM) system takes advantage of an array of sensors to continuously monitor a structure and provide an early prediction such as the damage position and damage degree etc. Such a system requires monitoring the structure in any conditions including bad condition. Therefore, it must be robust and survivable, even has the self-repairing ability. In this study, a model reconstruction predicting algorithm based on particle swarm optimization-support vector regression (PSO–SVR) is proposed to achieve the self-repairing of the Fiber Bragg Grating (FBG) sensor network in SHM system. Furthermore, an eight-point FBG sensor SHM system is experimented in an aircraft wing box. For the damage loading position prediction on the aircraft wing box, six kinds of disabled modes are experimentally studied to verify the self-repairing ability of the FBG sensor network in the SHM system, and the predicting performance are compared with non-reconstruction based on PSO–SVR model. The research results indicate that the model reconstruction algorithm has more excellence than that of non-reconstruction model, if partial sensors are invalid in the FBG-based SHM system, the predicting performance of the model reconstruction algorithm is almost consistent with that no sensor is invalid in the SHM system. In this way, the self-repairing ability of the FBG sensor is achieved for the SHM system, such the reliability and survivability of the FBG-based SHM system is enhanced if partial FBG sensors are invalid.