Optimization of PZT Wafers' placement with enhanced level of robustness using multiple interacting networks. (15th July 2023)

• Record Type: Journal Article
• Authors: Ismail, Zainab; Mustapha, Samir; Tarhini, Hussein
• Is Part Of: Mechanical systems and signal processing. Volume 195(2023)
• Publication Date: 2023-07-15
• Subjects: Sensor network optimization -- Guided waves -- Structural health monitoring -- Robustness -- Piezoelectric wafers; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2023.110283
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Continuous monitoring and in particular structural health monitoring (SHM) of structures is used, nowadays, by asset managers to ensure a high level of safety and extend the lifetime of their assets. One of the main bottlenecks faced in the development of an SHM system is the design and implementation of sensor networks. This includes selecting the sensor type and optimizing the number and positions of sensors. This study proposes a novel approach to account for the potential loss in sensing elements within the network, by having multiple independent networks but at the same time interacting for enhanced performance. The developed model considers the PZT locations as continuous variables where any point on the surface can be a sensor location. The model can handle complex structures and customize to different wave characteristics and coverage levels. The objective function in the developed model aims at maximizing a weighted sum of two dependent terms: the coverage which is the percentage of covered control points within the network, and the robustness which is the average number of networks covering each covered control point. The proposed model was solved using a genetic algorithm and was tested on several geometries. To mimic a faulty sensor within the network, a complete network was removed in each case, and it was found that the coverage was not impacted. The effect of the partitioning of large sections on the performance of the optimizer was explored. Experimental validations were carried out to evaluate the model's accuracy in damage localization within the optimized sensor networks and to emphasize the importance of each of the coverage and robustness. The results demonstrated the proficiency of the model developed in distributing the piezoelectric ceramics (PZTs) on the tested specimens, and its ability to detect damages more precisely in more robust configurations where multiple networks participate in covering wide regions.