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Dr Gordon Lindsay is a Lecturer in Industrial Automation in the Department of Engineering and is the leader for multiple undergraduate and postgraduate taught modules which focus on control and instrumentation technologies. Dr Lindsay’s experience in this field are also applied to ongoing industrial research collaborations (Knowledge Transfer Partnerships) which focus on industrial automation, condition monitoring and data analysis.
Before joining Glasgow Caledonian University in 2022, he spent 13 years working for TÜV SÜD National Engineering Laboratory (NEL), where as technical lead he was responsible for the design, build and commissioning of the control and instrumentation systems for the UK’s national standard for flow measurement. This included full facility instrumentation specification and wiring, Programmable Logic Controller (PLC) coding for control, automation and safety interlocking, signal conditioning, digital networking, and data acquisition (DAQ) software development. Gordon was the department head of Digital Metrology at NEL between 2018 and 2022. During this time, he and his team undertook research and consultancy projects to develop machine learning systems for condition-based monitoring applications.
The focus of Gordon’s doctorate was the detection and compensation of ambient temperature-induced density calculation errors in Coriolis mass flow meters. He conducted his research while working at NEL in collaboration with Coventry University and a flow meter manufacturer industrial partner. Before joining NEL, Gordon was an electronic engineer with robotic manipulator manufacturer Clansman Dynamics from 2007 to 2009. During this time, he travelled to forge and foundry sites worldwide to integrate and commission their robotic systems.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 7 Affordable and Clean Energy
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Collaborations and top research areas from the last five years
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Development of an IIoT platform for remote monitoring and proactive maintenance of mechanical assets
Niculita, I.-O. (PI), McGlinchey, D. (CoI) & Lindsay, G. (CoI)
Project: Knowledge Exchange
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Development of a proof of concept for an IoT/AI Infrastructure supporting sustainability goals for commercial retail building management applications
Niculita, I.-O. (PI), McGlinchey, D. (CoI) & Lindsay, G. (CoI)
11/08/25 → 8/09/25
Project: Knowledge Exchange
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A robust uncertainty quantification framework for machine learning–based wet-gas flow metering
Hosseini, S., Chinello, G., Lindsay, G. & McGlinchey, D., 14 Apr 2026, In: Measurement: Journal of the International Measurement Confederation. 269, 22 p., 120670.Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Citations (Scopus)101 Downloads (Pure) -
Machine learning-driven multiphase flow prediction for wet gas: a temporal data perspective incorporating fluid property analysis and explainable AI
Hosseini, S., Chinello, G., Lindsay, G. & McGlinchey, D., 30 Jan 2026, In: Measurement: Journal of the International Measurement Confederation. 258, Part B, 26 p., 119077.Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Citations (Scopus)144 Downloads (Pure) -
Transfer learning for data-driven wet gas flow metering: enhancing generalisation in digital measurement systems
Hosseini, S., Chinello, G., Lindsay, G., Loweimi, E., Ansari, M. A. & McGlinchey, D., Mar 2026, In: Flow Measurement and Instrumentation . 108, 37 p., 103146.Research output: Contribution to journal › Article › peer-review
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Advancements in predictive maintenance modelling for industrial electrical motors: integrating machine learning and sensor technologies
Hanifi, S., Alkali, B., Lindsay, G., Waters, M. & McGlinchey, D., May 2025, In: Measurement: Sensors. 38, 4 p., 101473.Research output: Contribution to journal › Article › peer-review
Open AccessFile8 Citations (Scopus)91 Downloads (Pure) -
Multiphase flow measurement of wet gas flow using machine learning modelling algorithms
Hosseini, S., Chinello, G., Lindsay, G., Smith, S. & McGlinchey, D., May 2025, In: Measurement: Sensors. 38, Supplement, 7 p., 101556.Research output: Contribution to journal › Article › peer-review
Open AccessFile7 Citations (Scopus)34 Downloads (Pure)