Graeme West

I am a Senior Lecturer in the department of Electronic and Electrical Engineering, having been appointed through the Chancellor's Fellowship scheme.  My research interests lie in the area of intelligent decision support, primarily for applications in the Energy Industry, and with a particular focus on through lifetime management of nuclear power generation assets.  This covers a broad range of disciplines ranging from artificial intelligence, machine learning, & data analytics through to image and video processing.  Application areas include inspection, condition monitoring, diagnostics and prognostics of plant items, both from individual asset and fleet wide perspectives.  I am an academic lead in the University’s Advanced Nuclear Research Centre (ANRC).

My current projects at the University include improving understanding of the graphite reactor cores of Advanced Gas-cooled Reactors (AGR) through analysis of refuelling data (EDF Energy), Automated sizing and classification of defects in CANDU reactor pressure tubes (Bruce Power), improved visual inspection of AGR fuel channel bricks (EDF Energy) and visual inspection of steel pipe work in the nuclear industry (NNL, Sellafield, WideBlue, Inspecta-hire).

• Intelligent Systems and Artificial Intelligence
• Data Analytics and Machine Learning
• Condition Monitoring, Diagnostics and Prognostics
• Nuclear power generation instrumentation and control

I am 2nd Year Adviser of Studies for the BEng/MEng Electronic and Electrical Engineering undergraduate degree courses.

i am module registrar for EM501: Fifth Year Group Projects for the MEng Electrical and Mechanical Engineering Students.

I am responsible for the 2nd year EE/EM271 Sensor and Signal Processing Laboratory where the students design and build an instrument for measuring the thickness of steel using an ultrasonic transducer.

I am module registrar for the 3rd Year EM310: Signal and Systems course which covers the concepts and analysis of signals in both the time and frequency domains in the context of both analogue and discrete (digital) domains, baoth in terms of mathematical analysis and practical systems.

I teach a 5-week module on "Intelligent Condition Monitoring" as part of ME507 Machinery Diagnosis and Condition Monitoring.

My research interests are in the design and application of artificial intelligence techniques to support the management of key engineering assets in a number of industries.  I have a particular focus in nuclear power generation applications and my research supports EDF Energy, who run and maintain the UK's Civil nuclear power stations, in analysing data that comes from their graphite reactor cores.