Structural Health Monitoring for Maintaining Aging Civil Infrastructure

维护老化的民用基础设施是全球面临的一个重大挑战。随着结构的老化，它们的退化增加，需要更复杂和频繁的维护来确保结构的完整性。在确保正常运行时间的同时，还需要延长基础设施的使用寿命和运行能力，这对基础设施管理构成了重大挑战。因此，需要对结构进行智能监控，以指导、计划和预测维修计划。MISTRAS集团有限公司是全球领先的资产管理公司，使用声发射（AE）监测全球众多大型资产和结构。声发射是当结构发生损伤时，通过结构传播的应力波的自发释放。通过在结构上安装传感器，可以定位声发射源并监测其活动。然而，在实践中，由于来自其他声源的声发射，如结构内部运动的摩擦、交通噪声和维护操作的承担，这提出了一个真正的挑战。虽然现有的方法可以区分声源机制，但这些方法都有缺点，并且通常需要了解不同声发射源的特征。此外，所使用的方法三十多年来没有改变。因此，需要开发一种改进的信号采集和数据分析方法，以更好地获取声发射数据，分析数据并将声发射信号与发展中的损伤程度相关联。该项目将要求学生：研究并了解声发射采集、信号处理和最新数据处理方法；研究信号分解的人工智能（AI）方法；探索确定声发射瞬态发生的统计方法；在时域和频域工作审查在最先进的边缘处理技术中应用开发方法的方法计划并进行实验室调查，以在实际条件下收集数据将实验室开发的方法应用于从实际资产收集的数据探索方法，如声学断层扫描，以关联和量化监测的损害程度

Maintaining aging civil infrastructure poses a significant challenge across the world. As structures age, their degradation increases requiring more complex and frequent maintenance to ensure structure integrity. This combined with the requirement to extend operational life and prolong the operational capabilities of infrastructure all while ensuring uptime poses significant challenges in infrastructure management. Therefore, there is a demand for intelligent monitoring of the structures to direct, schedule and predict maintenance programs. MISTRAS Group Ltd. are a world-leader in asset management and monitors numerous large assets and structures throughout the world using Acoustic Emission (AE).AE is the spontaneous release of a stress wave that propagates through a structure when damage occurs. By mounting sensors on a structure, the source of the AE can be located and monitored for its activity. In practice however this presents a real challenge due to AE originating from other acoustic sources such as rubbing from movement within the structure, traffic noise and the undertaking of maintenance operations. Although methods exist that work to differentiate acoustic source mechanisms, these have short comings and often require the features of the different AE sources to be known. Furthermore, the methodology that is used has not changed for over thirty years. Therefore, there is a requirement to develop an improved signal acquisition and data analysis approach to better acquire AE data, analyse the data and correlate the AE signals with the extent of the developing damage.This project will require the student to:Research and gain understanding of the AE acquisition, signal processing and state-of-the-art data processing approachesInvestigate artificial intelligence (AI) methods for signal decompositionExplore statistical methods for determining the occurrence of AE transients, working both in the time and frequency domainsReview approaches for applying developed methodologies in state-of-the-art edge processing technologiesPlan and conduct laboratory investigations to gather data under realistic conditionsApply methodologies developed in the laboratory to data gathered from real assetsExplore methods, such as acoustic tomography, for correlating and quantifying the extent of the monitored damage