Sensor placement algorithm for structural health monitoring with redundancy elimination model based on sub-clustering strategy

Considering the limitation of selecting several neighbor sensors in a local region similar to just single one, namely redundant information, a sensor placement algorithm for structural health monitoring is proposed based on sub-clustering strategy, in order to improve the performance of sensor configuration with less redundancy. According to the significance of redundancy, the proposed novel redundancy elimination model considers global and local effect to overcome the previous limitations in sensor distribution. Based on the sub-clustering strategy, the redundancy elimination model reflects the sensor configuration in each sub-clustering and overall structural field. The presented sub-clustering strategy for sensor placement includes three main procedures: sub-clustering algorithm, its check step and smallest enclosing circle method, thus the accuracy can be guaranteed. Combining the effective independence method with normalization and weighting factor, the proposed sensor placement algorithm can balance performance and redundancy by using genetic algorithm, which is more competitive to reduce the order difference between the two objectives. Finally, the effectiveness of the proposed redundancy elimination model is verified by a simple example, and another two engineering numerical examples including space solar power satellite and re-usable launch vehicle are applied to demonstrate the validity of the proposed sensor placement algorithm respectively.