Development of Advanced Technologies for Structural Damage Identification and Prediction

结构损伤识别和预测先进技术的开发

• 批准号: RGPIN-2021-03356
• 负责人: Wu, Nan
• 金额: $ 2.33万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760315
• 关键词: Development; Advanced; Technologies; Structural; Damage

Highly efficient damage identification methodologies are essential to ensuring the operational efficiency and reliability of a wide range of engineering structures (e.g. beam, plate, or shell elements in vehicle frames and civil infrastructure). These methods must be sensitive, accurate, fast, and easy to operate and must provide detection, evaluation, and localization of damage. Many challenges have yet to be addressed to develop efficient identification techniques especially for structures with damages at initial stages under varying load conditions. An efficient condition monitoring system for mechanical structures will require advanced technologies and methods, which can identify and predict the occurrence and propagation of structural damage at its earliest stages. Real-time operation of the monitoring system is also essential for its adoption as a practical application. The proposed work will explore and develop new methods in structural static and dynamic analysis, advanced signal processing algorithm, new sensing network, and machine learning technique. First, a new load transfer analysis method will be developed and combined with stress calculations to provide a clear sign of possible damage at an early stage. Second, a novel tunable Entropy algorithm with a proper noise filter will be created that can reliably identify damage under practical noisy working conditions. A flexible piezoelectric nanocomposite paint will be introduced to create a new sensor network design that can cover a large area of monitored objects. With both time and space domain signals from the piezoelectric sensor, it will be possible to localize the damages. To attain real time structural analysis for damage identification, proper machine learning algorithms will be introduced and developed to learn and estimate the load transfer path and Entropy results quickly and accurately. Finally, all of the components listed above will be combined to produce a comprehensive damage identification system. Outcomes of this research will lead to comprehensive damage identification platforms and practical (easy to operate and maintain) engineering solutions to warn impending structural damage and failure by detecting them in the initial stages. The proposed research will ensure the reliability, integrity and safety of different types of engineering structures through practical applications as a long-term goal. The proposed damage assessment methods will be inexpensive and easy to apply in industrial settings for the direct benefit of Canadian industries. Highly qualified personnel trained under this program will gain the theoretical and practical expertise necessary to promote and advance superior mechanical structural condition monitoring in Canada.

高效的损伤识别方法对于确保各种工程结构（例如车辆框架和民用基础设施中的梁、板或壳单元）的运行效率和可靠性至关重要。这些方法必须灵敏、准确、快速、易于操作，并且必须提供损伤的检测、评估和定位。开发有效的识别技术还面临许多挑战，特别是对于在不同荷载条件下初始阶段就有损伤的结构。一个有效的机械结构状态监测系统需要先进的技术和方法，能够在早期阶段识别和预测结构损伤的发生和传播。监控系统的实时运行也是其实际应用的必要条件。本研究将探索和发展结构静力与动态分析的新方法、先进的信号处理算法、新的传感网络和机器学习技术。首先，将开发一种新的载荷传递分析方法，并将其与应力计算相结合，以便在早期阶段提供可能损坏的清晰迹象。其次，提出了一种新的可调熵算法，该算法具有适当的噪声滤波器，可以在实际噪声工作条件下可靠地识别损伤。一种柔性压电纳米复合涂料将被引入，以创建一种新的传感器网络设计，可以覆盖大面积的监测对象。利用压电传感器的时域和空域信号，可以实现损伤的局部定位。为了实现对损伤识别的实时结构分析，将引入和开发适当的机器学习算法，以快速准确地学习和估计载荷传递路径和熵结果。最后，将上面列出的所有组件组合起来，形成一个全面的损伤识别系统。本研究的结果将导致全面的损伤识别平台和实用（易于操作和维护）的工程解决方案，通过在初始阶段检测来预警即将发生的结构损伤和失效。所提出的研究将通过实际应用确保不同类型工程结构的可靠性、完整性和安全性作为长期目标。拟议的损害评估方法价格低廉，易于在工业环境中应用，直接有利于加拿大工业。在该项目下培训的高素质人员将获得促进和推进加拿大高级机械结构状态监测所需的理论和实践专业知识。