Dr Graeme West

Reader

Electronic and Electrical Engineering

Personal statement

I am a Reader 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).

Expertise

Has expertise in:

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

Publications

A novel data augmentation method for improved visual crack detection using generative adversarial networks: Branikas Efstathios, Murray Paul, West Graeme; IEEE Access Vol 11, pp. 22051-22059 (2023); https://doi.org/10.1109/ACCESS.2023.3251988
Tactile, orientation, and optical sensor fusion for tactile breast image mosaicking: Hampson Rory, West Graeme, Dobie Gordon; IEEE Sensors Journal (2023); https://doi.org/10.1109/JSEN.2023.3237906
Parameterisation of domain knowledge for rapid and iterative prototyping of knowledge-based systems: Young Andrew, West Graeme, Brown Blair, Stephen Bruce, Duncan Andrew, Michie Craig, McArthur Stephen DJ; Expert Systems with Applications Vol 208 (2022); https://doi.org/10.1016/j.eswa.2022.118169
Structure-from-motion based image unwrapping and stitching for small bore pipe inspections: Zhang Dayi, Jackson William, Dobie Gordon, West Graeme, MacLeod Charles Norman; Computers in Industry Vol 139 (2022); https://doi.org/10.1016/j.compind.2022.103664
Inspection methods for small bore pipes: West Graeme, Forrester Colin, ZHANG Dayi, Dobie Gordon; (2022)
A quantile dependency model for predicting optimal centrifugal pump operating strategies: Stephen Bruce, Brown Blair, Young Andrew, Duncan Andrew, Helfer-Hoeltgebaum Henrique, West Graeme, Michie Craig, McArthur Stephen D J; Machines Vol 10 (2022); https://doi.org/10.3390/machines10070557

More publications

Teaching

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.

Research interests

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.

Professional activities

Automated detection of crack features in nuclear superheaters using deep learning with automatically labelled datasets: Contributor; 30/11/2021
AUTOMATED GENERATION OF TRAINING DATASET FOR CRACK DETECTION IN NUCLEAR POWER PLANT COMPONENTS: Contributor; 16/6/2021
Panel Session on PHM Applications in Power Generation: Speaker; 30/7/2020
Artificial Intelligence in Nuclear Power Generation Applications: Speaker; 13/7/2020
American Nuclear Society (ANS) Human Factors (External organisation): Advisor; 1/5/2020
Innovation in Automated Assessment of Pressure Tube Defects: Speaker; 9/5/2019

More professional activities

Projects

Turing Network Development Call: McArthur, Stephen (Principal Investigator) West, Graeme (Co-investigator); 01-Jan-2022 - 30-Jan-2022
NNL Gamechangers GC_142 Phase 5 (Linked to 191761) Image Processing for Enhanced Visualisation: Murray, Paul (Principal Investigator) Marshall, Stephen (Co-investigator) West, Graeme (Co-investigator) Zabalza, Jaime (Co-investigator); 18-Jan-2021 - 31-Jan-2022
KTP - ICR Integrity: West, Graeme (Principal Investigator) McArthur, Stephen (Co-investigator); 27-Jan-2020 - 26-Jan-2022
Image Processing for Enhanced Visual Inspection of Nuclear Waste Packages GC142 Phase 2: Murray, Paul (Principal Investigator) Marshall, Stephen (Co-investigator) Ren, Jinchang (Co-investigator) West, Graeme (Co-investigator) Zabalza, Jaime (Research Co-investigator); 01-Jan-2020 - 30-Jan-2021
Doctoral Training Partnership 2018-19 University of Strathclyde | Fagan, Andrew: West, Graeme (Principal Investigator) McArthur, Stephen (Co-investigator) Fagan, Andrew (Research Co-investigator); 01-Jan-2019 - 01-Jan-2023
ANRC 04 Advanced Image Processing Techniques for In-core Inspections: West, Graeme (Principal Investigator) Murray, Paul (Co-investigator); 01-Jan-2018 - 01-Jan-2021

More projects

Address

Electronic and Electrical Engineering
Royal College

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