Structural Health Monitoring
Structural Health Monitoring is the use of permanently installed sensors to assess the integrity of a structure or system. The data from SHM systems may be integrated into ‘smart’ structural integrity diagnostics and prognostics through the Digital Twin concept. SHM research at the Cincinnati Centre for Non-Destructive Evaluation covers all aspects of SHM, from developing the fundamental design principles of SHM systems through to the structural integrity assessment.
Potential Drop Creep Monitoring of Power Station Components
Creep is the dominant life-limiting mechanism of high-pressure, high-temperature power station components. The Center has developed a permanently installable potential drop monitoring system suitable for monitoring power station components. The lab-based measurement system is commercialized through Material Monitoring Systems and the power station system is being trialed in UK power stations.
J. Corcoran, C. M. Davies, P. Cawley, and P. B. Nagy, IEEE Trans. Instrum. Meas. 69, 1313-1326, 2020
J. Corcoran, PB Nagy, and P Cawley, Int. J. Press. Vess. Piping. 153, 15-25, 2017
J Corcoran, P Hooper, C Davies, PB Nagy, and P Cawley, Int. J. Mech. Sci. 110, 190-200, 2016
Permanently Installable Guided Wave Tomography
Accurate thickness mapping of large engineering structures is critical to assess the integrity and residual life of mechanical components subject to erosion or corrosion damage. However, in many industrial settings, it is not possible to access the region of interest directly, e.g. because of remote location or due to the presence of physical obstacles. Guided ultrasonic waves offer a promising approach to remote wall thickness loss estimation thanks to their ability to propagate over a long distance along a structure. Our research focuses on the development of a highly sensitive guided wave tomography system based on an innovative array technology and advanced inversion schemes. This technology is now being commercialized through Cincinnati NDE, Ltd. a start-up company from UC.
Guided ultrasonic wave tomography of a pipe bend exposed to environmental conditions: A long-term monitoring experiment
F Simonetti and MY Alqaradawi, NDT & E International 105, 1-10, 2019
AJ Brath, F Simonetti, PB Nagy, and G Instanes, IEEE Trans. Ultras. Ferr. Freq. Contr. 64, 847-858, 2017
A digital twin is a digital representation of a real engineering asset. The digital twin is updated to reflect the state of the real asset. Research into digital twins at the UCNDE focusses on two main areas: probabilistic structural integrity assessments and NDE data integration. Structural integrity assessments must be probabilistic to acknowledge the inherent uncertainty and stochastic nature of damage progression. We must then use operational data and NDE/SHM to validate and refine those structural integrity predictions, honing into highly accurate assessments. The Center researches state-of-the-art probabilistic structural integrity assessments and develops methods to integrate NDE/SHM data to provide real-time, uncertainty-quantified, diagnosis and prognosis.
J Corcoran, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 473, 2017
Model Assisted Probability of Detection of SHM Systems
Quantifying the uncertainty of the detection capabilities of SHM systems is essential if they are to be implemented with confidence. Assessing the detection capabilities of SHM systems is challenging in part due to the fact that sensors are permanently attached to the component meaning a limited number of experimental trials are possible. Finite Element based model assisted methods are therefore necessary. The detection capabilities of permanently installed sensors are dependent on a number of different factors including defect and sensor location and orientation, defect morphology, sensor coupling etc. In order to capture the full range of detection capabilities all combinations of factors need to be evaluated leading to an exceptionally large number of required calculations. A combination of advanced modelling techniques together with state-of-the art uncertainty quantification techniques brings model assisted probability of detection assessments within reach.