Actuator placement optimization for guided waves based structural health monitoring using fibre Bragg grating sensors

Structural health monitoring (SHM) systems have a potential to reduce lifecycle costs of structures. They may be used for maintenance planning which reduces the maintenance cost as well as for lifetime extension. As a result, there is a lot of active research in the area for SHM of civil and mechanical structures. The SHM system should be low cost, suitable for continuous monitoring, able to detect small levels of damage. Guided waves (GW) based SHM techniques allow monitoring of large plate-like structures with few sensors and have been identified as the most promising of techniques for SHM. Several different actuators and sensors have been developed and used for the GW based SHM. FBG sensors due to their low weight, and ability to be multiplexed have been long thought to be an ideal sensors for SHM. The recent development of the edge filtering approach has increased their sensitivity to GW sensing and made them ideal sensors. Unfortunately the FBG sensors are passive sensors and show directional sensitivity. These operational constraints make extension of the earlier developed GW based SHM techniques for FBG sensors difficult. Recently the authors developed a technique for damage detection specifically designed for a network with FBG sensors. This paper develops a methodology for a design of an actuator-sensor (AS) network for improving the damage assessment capability using the developed method. The paper develops a two-step methodology for the optimization of actuator placement for an AS network with FBG sensors. In the first step the number of actuators needed for the optimization are determined based on actuator densities. Once the number of actuators is known, a genetic algorithm (GA) is developed for the optimization of the their positions. The cost function is developed based on two new metrics (namely coverage2—coverage with at least 2 AS pairs and coverageR—radial coverage based on edge reflections) which are defined by the application demand. The optimized placement is then used to successfully detect and localize the damage. The study also shows the merit in the use of the specific metrics and the sufficiency of the metrics developed for improving the damage detection capability of the specific method.