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condition monitoring PhD Projects, Programmes & Scholarships
We have 25 condition monitoring PhD Projects, Programmes & Scholarships
We have 25 condition monitoring PhD Projects, Programmes & Scholarships
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Holistic Condition Monitoring Methodology for Power System Assets using Machine Learning
Applications will remain open until a suitable candidate has been identified. Early applications are encouraged. Fulfilling the complex and numerous requirements of modern power systems is becoming increasingly more challenging, one of the primary contributors being the ageing infrastructure. Read more
Development of condition monitoring techniques for SF6 alternatives in high voltage plant
Manchester is home to one of the largest electrical and electronic engineering departments in the country. Our High Voltage Laboratory makes us a place renowned for applied research, a place ranked in the top three UK universities for research impact. Read more
Artificial intelligence for structural health monitoring of tidal turbines
Tidal turbines could provide over 10% of the UK’s electricity, but issues with reliability and cost are hampering their commercial viability. Read more
Machine Learning Approach to Identify Environmentally Friendly Alternatives to SF6 for UK Electricity Networks
Manchester is home to one of the largest electrical and electronic engineering departments in the country. Our High Voltage Laboratory makes us a place renowned for applied research, a place ranked in the top three UK universities for research impact. Read more
Investigation of Greenhouse Gas Alternatives for Electrical Insulation
Manchester is home to one of the largest electrical and electronic engineering departments in the country. Our High Voltage Laboratory makes us a place renowned for applied research, a place ranked in the top three UK universities for research impact. Read more
Structural Health Monitoring of Li-ion batteries using ultrasonics techniques
Lithium-ion batteries are widely used as the power supply for transportation power sources. However, they are prone to experience gradual degradation (capacity-fade and resistance-increase) or catastrophic failures. Read more
Digitalisation for Facilitating Sustainable Manufacturing Practices in Industry
The University of Exeter Business School (UEBS) trains world-class researchers who will shape our understanding of and responses to the most important societal challenges. Read more
Machine learning for fault and anomaly detection in food and drink manufacturing
The Industry 4.0 revolution is allowing huge quantities of process data from large scale manufacturing operations (such as those in the food industry) to be collected and analysed, and Digital Twins (i.e., highly accurate models) of these processes to be constructed. Read more
AI approaches and automatic uncertainty for robust, reliable, and trustful digital twin: application to sustainable critical infrastructure and systems
Motivation. Digital twins are virtual life replica of components and systems and they are becoming increasingly popular tools to predict, monitor and control the behaviour of complex systems. Read more
Scalable continuous Machine Learning for non-Stationary Systems
Nonlinear time series prediction attracts researchers from many different disciplines such as engineering systems condition monitoring and control, financial markets prediction and energy management, etc. Read more
Enhancing the performance, condition, safety and reliability of offshore wind energy infrastructure systems using optimal design, inspections, structural health monitoring and structural control
. Wind turbines are exposed to frequent structural damage increasing the cost of wind energy generation. To drive these costs down, this project will develop new automated methods for detection of structural damage to wind turbines by examining vibration responses measured by sensors attached to the structure. Read more
Experimental Nuclear Physics PhD in Enhanced nuclear fuel monitoring with a novel beta-gamma coincidence detector system
This project spans between fundamental nuclear structure physics research and precision measurement of industrially relevant radionuclides. Read more
Regenerating chalk stream environments: Monitoring the effectiveness of the River Anton Restoration Study
Supervisory Team. Paul Kemp and Andrew Vowles. Project description. Climate change and biodiversity loss are two of the greatest global challenges faced by humanity. Read more
Ambulatory monitoring of Raynaud’s phenomenon and systemic sclerosis associated microcirculation
Systemic sclerosis (SSc), a microvascular autoimmune disease, causes damage to the small blood vessels (microvasculature). Although rare it has the highest mortality of all rheumatological conditions and causes significant morbidity. Read more
AI enabled Remote Health Monitoring (PHDCEPS2122002)
This project is to design and develop AI enabled remote health monitoring systems. The main aim is to adopt IoT based sensory platforms and apply AI techniques to remotely monitor the patient/individuals. Read more
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