Condition Monitoring of Cable Structures using Digital Twin

电缆结构广泛应用于各种运输系统中，例如：电气化铁路的架空接触线(OCL)、缆车以及桥梁中电缆的各种应用。与传统结构相比，缆索结构的状态监测有不同的挑战需要考虑：(I)缆索对腐蚀高度敏感，可能会意外失效；(Ii)缆索的固有振动频率与结构的“刚性”元素(如桥面)显著不同；(Iii)缆索和缆索结构具有复杂和弯曲的几何形状；(Iv)在某些情况下，缆索受到移动荷载(缆车和OCLS)。本项目旨在开发一种新的方法来对这些缆索结构进行状态监测。这将包括明智的结构仪器与机器学习模型相结合，该模型使用基于物理的系统振动行为模型进行训练--一对“数字孪生兄弟”。一个基于物理的模型将使用现场测试数据进行校准和验证。它将被用来建立一个快速查找的数字孪生兄弟，使用服务中的数据和带有参数变化的模拟。数字双胞胎将设计用于实时状态监测；它将用于快速和自动检测和诊断结构中正在发生的故障，并启动安全程序(例如，关闭协议或标记区域以进行紧急维护)。该项目的关键研究领域是：(I)传感器设置的优化-所使用的传感技术及其位置都需要仔细选择并整合到诊断模型中。(Ii)为缆基结构的数字孪生结构开发物理启用的人工智能技术，对广泛的固有频率和代表性参数变化有效。(Iii)开发诊断方法以实时识别异常振动行为，使安全关键情况下能够快速启动适当的干预。

Cable structures are used in a wide variety of transport systems - some examples are:overhead contact lines (OCLs) for electrifying railways, cable-cars and various applicationsof cables in bridges. There are different challenges to consider for condition monitoring ofcable structures compared to conventional structures: (i) cables are highly sensitive tocorrosion and can fail unexpectedly; (ii) natural frequencies of vibration of cables differsignificantly to the 'rigid' elements of the structure (e.g the bridge deck); (iii) cables and cablestructures have complex and curved geometries; (iv) in some cases cables are subject tomoving loads (cable-cars and OCLS).This project aims to develop a novel approach to condition monitoring of these cablestructures. This will consist of judicious instrumentation of the structure in combination with amachine learning model trained using a physics-based model of the systems vibrationbehaviour - a 'digital-twin'. A physics-based model will be calibrated and validated using fieldtest data. It will be used to build a fast-lookup digital twin using both in-service data andsimulations with parametric variations. The digital-twin will be designed for real-timecondition monitoring; it will be used to rapidly and automatically detect and diagnosedeveloping faults in the structure and initiate safety procedures (e.g. shut down protocol orflag the area for urgent maintenance). Patterns in the occurrence of specific faults can beused to inform regular maintenance guidance and planning.The key research areas of the project are:(i) optimization of sensor set-ups - both the sensing technologies used and their placementswill need to be carefully chosen and integrated into the diagnostic models.(ii) development of physics-enabled AI technologies for digital twins of cable-basedstructures, effective with a wide range of natural frequencies and representative parametricvariations.(iii) developing diagnostic methods to identify abnormal vibration behaviour in real-time,enabling rapid initiation of the appropriate intervention in safety-critical situations.