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Prestigious Strathclyde/NPL PhD Studentship: Structural Health Monitoring of Critical Energy Infrastructure with Predictive Maintenance

Project Description

The lack of risk-predictive algorithms limits the ability of engineers to perform predictive maintenance of infrastructure objects. Data analytics applied to critical infrastructure systems provides insights in real-world dynamical behaviour of key elements or sub-structures of such systems. We therefore aim to develop and validate a prediction algorithm for early detection of real-time anomalies in Structural Health Monitoring (SHM) data. This is especially important for the monitoring of critical energy generation and transport infrastructure, including renewable installations, which are often located in remote, difficult-to-access areas. The project will investigate an application of the tipping point analysis within the context of SHM and data generated by a high-resolution detection system. The key objective will be to develop a method based on precision Fiber Bragg Grating (FBG) measurements using interferometry in order to sensitise the original technique, currently hampered by excessive noise. Since distributed sensing is required to be retained, the use of FBGs that are readily multiplexable is critical. Strathclyde owns a prototype lab system that uses an interferometric detection of FBG peaks, while NPL will offer insights into data analytics. Tensile experiments on various laboratory samples will be performed using interferometric detection of strain to boost S/N ratio. The sensor data from these experiments, but also from industrial field trials, will be analysed in order to develop methods for early prediction of structural damage.

Funding Notes

Entry Requirements:
A 2.1 and above degree in Physics, Engineering, or a related discipline.

These are fully funded studentships, and applications are welcomed from UK and EU applicants. An annual tax-free stipend of ca. £15000 is available to the successful candidates.

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