Condition Monitoring of Cable Structures using Digital Twin

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 applications
of cables in bridges. There are different challenges to consider for condition monitoring of
cable structures compared to conventional structures: (i) cables are highly sensitive to
corrosion and can fail unexpectedly; (ii) natural frequencies of vibration of cables differ
significantly to the 'rigid' elements of the structure (e.g the bridge deck); (iii) cables and cable
structures have complex and curved geometries; (iv) in some cases cables are subject to
moving loads (cable-cars and OCLS).
This project aims to develop a novel approach to condition monitoring of these cable
structures. This will consist of judicious instrumentation of the structure in combination with a
machine learning model trained using a physics-based model of the systems vibration
behaviour - a 'digital-twin'. A physics-based model will be calibrated and validated using field
test data. It will be used to build a fast-lookup digital twin using both in-service data and
simulations with parametric variations. The digital-twin will be designed for real-time
condition monitoring; it will be used to rapidly and automatically detect and diagnose
developing faults in the structure and initiate safety procedures (e.g. shut down protocol or
flag the area for urgent maintenance). Patterns in the occurrence of specific faults can be
used 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 placements
will need to be carefully chosen and integrated into the diagnostic models.
(ii) development of physics-enabled AI technologies for digital twins of cable-based
structures, effective with a wide range of natural frequencies and representative parametric
variations.
(iii) developing diagnostic methods to identify abnormal vibration behaviour in real-time,
enabling rapid initiation of the appropriate intervention in safety-critical situations.