PhD Studentship - SafeML-based Confidence Generation and Explainability for UAV-based Anomality Detection of Blades Surface in Offshore Wind Turbines

Supervisor(s)

1. Dr Zhibao Mian, University of Hull, Z.Mian2@hull.ac.uk
2. Dr Koorosh Aslansefat, University of Hull
3. Professor Yiannis Papadopoulos, University of Hull

Enquiries email: auracdt@hull.ac.uk

Unmanned Aerial Vehicles (UAV), e.g. drones, are increasingly used for equipment anomaly and fault detection in offshore wind turbines. When the drones are employed to take images, the quality of the images can be affected by several factors. As a result, the decision made based on these images could be affected depending on the quality of the images. For this reason, the aim of this project is to propose a methodology to generate confidence in such decision, potentially reducing maintenance costs and down-time for offshore wind energy production.

The images taken by each drone are loaded into the pre-processing unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical difference between new images captured by drone and the trusted datasets (the datasets that the deep learning model has been trained with and validated by an expert in the design time) to generate the confidence. Having generated the confidence, three scenarios are considered:

• For very low, the approach will provide notice for O&M team to do the manual inspection
• For low, the approach will ask the drone to take more picture from that specific area
• For high, the approach will generate the diagnosis report with addressing the evaluated confidence.

The approach is also capable of providing deep learning explainability and interpretability.

Training & Skills

You will benefit from a taught programme, giving you a broad understanding of the breadth and depth of current and emerging offshore wind sector needs. This begins with an intensive six-month programme at the University of Hull. It is supplemented by Continuing Professional Development (CPD), which is embedded throughout your 4-year research scholarship.

In addition, opportunity to attend introductory MSc AI and Data Science modules. The supervisor group will deliver a Safe AI module and the student should join this module at the second year as a custom training scheme. This will provide effective digital and data science research skills training, ensuring that candidate is prepared for employment or further research in data science and safe AI, and to address future technological challenges.

Eligibility requirements

If you have received a First-class Honours degree, or a 2:1 Honours degree and a Masters, or a Distinction at Masters level with any undergraduate degree (or the international equivalents) in engineering, computer science or mathematics and statistics, we would like to hear from you.

If your first language is not English, or you require a Student Visa to study, you will be required to provide evidence of your English language proficiency level that meets the requirements of our academic partners. This course requires academic IELTS 7.0 overall, with no less than 6.0 in each skill.

For more information, please see the project page on the EPSRC CDT in Offshore Wind Energy Sustainability and Resilience website.

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