RISK CDT - Real-time vibration assessment for damage detection and predictive maintenance monitoring

PLEASE APPLY ONLINE TO THE SCHOOL OF ENGINEERING, PROVIDING THE PROJECT TITLE, NAME OF THE PRIMARY SUPERVISOR AND SELECT THE PROGRAMME CODE "EGPR" (PHD - SCHOOL OF ENGINEERING)

This is a project within the multi-disciplinary EPSRC and ESRC Centre for Doctoral Training (CDT) on Quantification and Management of Risk & Uncertainty in Complex Systems & Environments, within the Institute for Risk and Uncertainty. The studentship is granted for 4 years and includes, in the first year, a Master in Decision Making under Risk & Uncertainty. The project includes extensive collaboration with prime industry to build an optimal basis for employability.

Developing timely and efficient maintenance strategies of structures and systems in operating conditions is one of the current main industrial challenges. This is of particular importance for complex and critical systems such as aerostructures, nuclear power plant or disposal for radioactive waste. In fact, inspection activities are very effective to improve the reliability and safety of such systems but on the other hand they are extremely expensive.

For this reason, there is the need of developing robust predictive maintenance tools where mathematical algorithms are used to solve difficult modelling, decision making and classification problems. This will involve optimizing the number of inputs to the models, finding the minimum data requirement for accurate prediction of possible untoward events, and designing experiments to maximize the information content of the data.

This PhD project aims at developing new strategies for damage detection in operating conditions based on innovative computational approaches and supported by artificial intelligence and machine learning tools. It requires a multi-disciplinary approach combining advanced dynamic testing, multi-fidelity models, features extraction and uncertainty quantification.
The PhD project would address the following goals:
• Build a test setup to evaluate the response of a healthy structure in operation
• Develop advanced multi-fidelity models and parameter identification strategies
• Identify suitable metrics for damage identification
• Develop innovative machine learning strategies to quantify uncertainty in damage intensity and location