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)

2) Dr Koorosh Aslansefat (University of Hull)

3) Professor Yiannis Papadopoulos (University of Hull)

Enquiries email: Z.Mian2@hull.ac.uk

Qualification type: PhD

Location: University of Hull

Funding for: UK students

Funding amount: £20,780

Hours: Full time

Closes: 9 May 2025

Subject areas

• Engineering & Technology
• Computer Sciences
• Mathematics & Statistics

Project description 

Unmanned Aerial Vehicle (UAV) e.g., drones are increasingly used for equipment anomaly and fault detection. When the drones are employed to take images, the quality of the images can be affected by several factors. For instance, images can be blurred due to the relative motion between the blades and the camera mounted on the drones. Noise can be introduced to the images due to the harsh operating conditions of drones. Noise can also be produced by various surrounding electronic devices. 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. 

Methodology
Please visit our website for an illustration that gives more information about our proposed methodology. In this method, the images taken by each drone will be loaded into the pre-processing unit and then the pre-processed data will be used as the input of the deep learning algorithm. In the next phase, the SafeML tool (a novel open-source safety monitoring tool) is used to measure the statistical difference between new images 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 have been considered; (a) if the confidence is very low, then the approach will provide notice for O&M team to do the manual inspection, (b) if the confidence is low, the approach will ask the drone to take more picture from that specific area, and (c) if the confidence is high, the approach will generate the diagnosis report with addressing the evaluated confidence. In the last scenario, the system will be permitted to proceed with the results autonomously. Note that the threshold defining for the confidence should be tuned in the design time by an expert. 

The successful student will have opportunities to attend the introductory MSc AI and Data Science modules supplied by the school, if they lack existing training or expertise. 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

Entry requirements

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

This scholarships is available to Home (UK) students only.

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