Towards CyberSHM: autonomous acousto-ultrasonic health monitoring of operational composite structures

对在役安全关键结构进行连续监测以实时评估其运行健康状况正受到极大关注，并且是一个非常热门的研究领域。这可以归因于以下两个因素：i）下一代复杂复合材料结构的快速发展及其作为轻质结构在从航空航天到海上/陆上能源基础设施的若干行业中的普遍使用，汽车和核工业。ii）数据-丰富的环境和巨大的胃口，工业利用其利益，以改变其传统的，往往是干预，对下一代复杂复合材料结构的重大研究投资（例如正在进行的EPSRC赠款EP/T011653/1）及其在工业用途中的快速吸收已经引起了对这些结构的监测的关注和挑战。对这些结构对微妙、几乎不可见的损坏（例如隐藏的脱粘、纤维/基体开裂）的敏感性的调查表明，后者可能会显着危及结构完整性并可能导致灾难性故障。最近，世界上最大的飞机制造商制造的飞机在客运航班上发生了多起灾难性事故，这无疑加强了对这些结构的安全性，可维修性和适用性的审查。这与采用更绿色和可持续结构的目标相结合（以满足2016年巴黎气候协定中承诺的全球排放目标），并在不影响安全的情况下降低与检查和维护相关的运营成本。同时，随着工业4.0中无处不在的工业物联网的范式转变，结合先进的传感和通信技术的普适计算，开发安全关键工程结构的结构健康监测（SHM）解决方案已经成为必要，可以从这种智能、数据丰富的自动化环境中获益，并能够无缝融入其中。该提案旨在对轻质复合材料结构的监测进行基础科学研究和技术实施，以弥合SHM构想的未来愿景与评估结构健康的现有干预实践之间的差距。本项目的目标是解决实时声-超声监测的挑战（类似于“监听损伤”和/或结构响应的变化）和使用多管齐下的方法的操作结构的在线损伤识别，其中关键部件是-a）在各种操作/环境条件下的结构超声波波导行为的物理驱动的基础模型或数字等效物，B）提取，损伤事件结构声指纹的同步与利用（例如工具掉落、分层、裂纹），如由机载传感网络收集的，用于数据驱动的训练和损伤事件的分类，以及c）实时损伤识别工具箱（识别位置，类型和严重性），这两个数据驱动（现场传感器数据）和模型通知（基于物理的结构波导的理解），以给出初始损伤的量化指标沿着及其估计置信度。该项目采用了一种新的方法，将基于物理的表征结构声学特性与来自混合被动-主动声-超声监测和被监测结构的询问的数据进行同化，以进行在役结构的网络物理监测或网络SHM。

Continuous monitoring of in-service safety-critical structures for real-time assessment of their operational health is receiving significant attention and is a highly topical area of research. This is can be attributed, among others, to the following two factors.i) The rapid evolution of next-generation complex composite structures and their ubiquitous use as lightweight structures in several industries ranging from aerospace to offshore/onshore energy infrastructure, automotive and nuclear industry.ii) The significant advancement of automation within a data-rich environment and the immense appetite of industries to leverage its benefits for transforming their traditional, often interventionist, practices.Significant research investment into next-generation complex composite structures (such as the ongoing EPSRC grant EP/T011653/1) and their rapid uptake in industrial usage has brought to the fore concerns and challenges around monitoring of these structures. Investigations into the susceptibility of these structures to subtle, barely visible damages (like hidden debonding, fibre/matrix-cracking) reveal that the latter can significantly jeopardize the structural integrity and can lead to catastrophic failures. The recent multiple catastrophic accidents in the passenger flights involving aeroplanes manufactured by the world's erstwhile largest planemaker has rightly enhanced the scrutiny on the safety, serviceability and suitability of such structures for public use. This is coupled with objectives for employing greener and sustainable structures (to meet the global emissions target as pledged in the Paris Climate Agreement 2016) and reducing operational costs associated with their inspection and maintenance without compromising on safety.Concurrently, with a paradigmatic shift towards industrial internet of things within Industry4.0 with ubiquitous, pervasive computing coupled with advanced sensing and communication technologies, it has become a necessity to develop structural health monitoring (SHM) solutions of safety-critical engineering structures which are abreast of, can reap the benefits of and are able to fit seamlessly into this intelligent, data-rich environment of automation. The proposal is aimed at fundamental scientific investigation into and the technological implementation of monitoring of lightweight composite structures to bridge the gap between the conceived futuristic vision of SHM and the existing interventionist practices of evaluating structural health. The objective of this project is to address the challenge of real-time acousto-ultrasonic monitoring (akin to "listening for damages" and/or changes in structural response) and online damage identification of operational structures using a multi-pronged approach with the key components being -a) physics-driven underlying model or digital equivalent of structural ultrasonic waveguides behaviour under various operational/ambient conditions,b) the extraction, synchronization and utilization of structural acoustic fingerprints of damage events (such as tool drop, delamination, cracks) as collected by the onboard sensory network for data-driven training and classification of damage events andc) a real-time damage identification toolbox (identifying the location, type and severity) which is both data-driven (in-situ sensor data) and model-informed (physics-based understanding of structural waveguides) to give quantified metrics of incipient damage along with their estimated confidence. The project takes the novel approach of assimilating physics-based characterization structural acoustic characteristics with data from hybrid passive-active acousto-ultrasonic monitoring and interrogation of the monitored structures for a cyberphysical monitoring or CyberSHM of in-service structures.