Sensor placement optimization on complex and large metallic and composite structures. (January 2020)

• Record Type: Journal Article
• Authors: Ismail, Zainab; Mustapha, Samir; Fakih, Mohammad Ali; Tarhini, Hussein
• Is Part Of: Structural health monitoring. Volume 19:Number 1(2020)
• Page Start: 262
• Page End: 280
• Publication Date: 2020-01
• Subjects: Sensor network optimization -- genetic algorithm -- piezoelectric wafers -- non-convex surfaces -- structural health monitoring; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921719841307
• Languages: English
• ISSNs: 1475-9217

Abstract

This study presents an effective solution for the optimization of piezoelectric (PZT) wafer placement in a network of convex and non-convex structures, toward the application in the field of structural health monitoring. The proposed objective function is to maximize the coverage of the monitored area, discretized by a set of control points, while minimizing the number of PZT wafers. In the optimum solution, each control point should be covered by a user-defined number of sensing paths, defined as the coverage level. The PZT locations were treated as continuous variables. Thus, during the optimization process, any location on the plate is considered as a potential position for a PZT wafer. The algorithm provides the flexibility of changing a wide range of parameters including the number of PZT wafers, the distance covered around the sensing path, the required coverage level, and the number of control points, in addition to identifying the most sensitive PZT wafer within the network. The tractability of the model proposed was improved by feeding the solver an initial solution. The model calculates the importance of each PZT wafer within the network, which allows for further reduction in the number of active PZT elements. The suggested model was solved using a genetic algorithm. Multiple sensor network configurations on composite and metallic structures were selected, including a large cargo door of an A330 airplane, and validated experimentally. The experimental validation was to evaluate the accuracy in damage localization within the optimized sensor networks. The results demonstrated the proficiency of the model developed in distributing the PZT wafers on non-convex structures and large metallic structures.