Development and optimization of structural monitoring and damage detection in massive elements using piezoelectric transducers and smart aggregates

使用压电传感器和智能骨料开发和优化大块元件的结构监测和损伤检测

• 批准号: 448696650
• 负责人: Professorin Dr.-Ing. Tamara Nestorovic
• 金额: --
• 依托单位: Arbeitsgruppe Mechanik adaptiver Systeme
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/448696650?language=en
• 关键词: Development; optimization; structural; monitoring; damage

Not only the identification of the damage presence plays an important role in structural health monitoring (SHM), but primarily its precise locating. Damage index (DI) represents a qualitative indicator of the damage presence, yet the methods that rely only on DI cannot detect exact location of the damage. The goal of this research proposal is to develop efficient SHM methods for damage detection in massive structural elements and strucutres using piezoelectric actuators and sensors. The hybrid methods which combine the advantages of the hybridized approaches in order to increase their effect are in the focus of this research. A hybrid approach for the damage detection in 2D elements based on wave propagation and acquisition using piezoceramic transducers and smart aggregates proposed by the applicant should be further pursued and theoretically developed in order to reach to an appropriate approach applicable to 3D problems i.e. to 3D massive structural elements. A new 3D DI should be developed, analyzed and implemented within a hybrid method. This hybrid method combines the 3D DI with the method based on time-of-flight of propagating elastic ultrasound waves. The findings from the fundamental research should be verified by experimental investigation using available adaptable experimental setup with an ultrasonic laser for structural response acquisition with high resolution and precision. In addition, special piezoelectric transducers – smart aggregates will be further developed and implemented. Through a systematic approach it will be investigated which configurations and constellations of actuators and sensors would fulfill the requirement that both the computational burden and the design costs can be reduced, by increasing at the same time the damage detection efficiency. These criteria require multi-objective structural optimization, which should be tackled by implementation of deep learning (DL) neural networks for optimization problems. Based on the results from the previous research phase the signal features for efficient classification of the structural health should be identified. In addition, the optimal experiments for damage detection should be designed using DL based on numerical models and further implemented for experimental investigation.

在结构健康监测 (SHM) 中，不仅识别损伤存在起着重要作用，而且首先是其精确定位。损伤指数（DI）代表损伤存在的定性指标，但仅依靠 DI 的方法无法检测损伤的确切位置。本研究提案的目标是开发有效的 SHM 方法，使用压电执行器和传感器来检测大型结构元件和结构的损伤。混合方法结合了混合方法的优点以提高其效果是本研究的重点。申请人提出的基于使用压电陶瓷换能器和智能聚合体的波传播和采集的 2D 元件损伤检测的混合方法应该进一步追求和理论上开发，以便达到适用于 3D 问题（即 3D 块状结构元件）的适当方法。应采用混合方法开发、分析和实施新的 3D DI。这种混合方法将 3D DI 与基于传播弹性超声波的飞行时间的方法相结合。基础研究的结果应通过实验研究进行验证，使用可用的适应性强的实验装置和超声波激光器进行高分辨率和高精度的结构响应采集。此外，特殊的压电传感器——智能聚合体将得到进一步开发和实施。通过系统方法，将研究执行器和传感器的哪些配置和星座能够满足通过同时提高损伤检测效率来减少计算负担和设计成本的要求。这些标准需要多目标结构优化，这应该通过实施深度学习（DL）神经网络来解决优化问题。根据先前研究阶段的结果，应确定结构健康有效分类的信号特征。此外，应使用基于数值模型的深度学习设计损伤检测的最佳实验，并进一步实施实验研究。