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

• Published in: Mechanical systems and signal processing 2019-06, Vol.124, p.369-387
• Main Authors: Yang, Chen, Liang, Ke, Zhang, Xuepan, Geng, Xinyu
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Configuration management; Configurations; Effective independence method; Genetic algorithms; Mathematical models; Performance enhancement; Placement; Redundancy; Redundancy elimination model; Sensor placement algorithm; Sensors; Smallest enclosing circle; Solar generators; Solar power satellites; Strategy; Structural health monitoring; Sub-clustering strategy
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2019.01.057

•The significance and limitations of redundancy in sensor placement are investigated.•A novel redundancy elimination model considers global and local sensor distribution.•The strategy includes sub-clustering algorithm and smallest enclosing circle method.•The placement algorithm is with sub-clustering strategy, combined objective and GA.•The obtained dispersed sensor configuration can balance performance and redundancy. 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.