Multi-objective optimization for joint actuator and sensor placement for guided waves based structural health...

Structural Health Monitoring (SHM) systems have a potential to reduce lifecycle costs of structures. As a result, there is a lot of active research in the area for SHM of civil and mechanical structures. Guided waves (GW) based SHM techniques allow monitoring of large plate-like structures with a few sensors and have been identified as the most promising of techniques for SHM. Fibre Bragg grating (FBG) sensors due to their low weight, and ability to be multiplexed have been long thought to be 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 builds on the past work by the authors and develops a methodology for a design of an actuator-sensor (AS) network for improving the damage assessment capability of the previously proposed method. The paper develops a multi objective optimization technique for the joint optimization of actuator and sensor placement for a network with FBG sensors. The joint optimization of the actuators and sensors is necessary due to the passive nature of the FBG sensors and also incorporates the directional nature of the FBG sensors. The paper develops an integer encoded NSGA-II for the optimization of the AS network. The objectives for the optimization are derived from the specific damage detection technique tailored for the use of FBG sensors. The objective are: coverage with at least 2 AS pairs, coverage with at least 1 edge reflected path and the cost of the deployed network. The results indicate that the encoding of the objectives of the optimization is valid and indeed the damage detection capabilities of the AS network are as predicted analytically. The paper for the first time develops a joint optimization of network for FBG sensors. It is also the first attempt at a truly multi-objective optimization of the AS network and promises to have applications on real structures.