DOI: 10.1109/ACCESS.2022.3140645 Electromagnetic Relays (Electromagnetic Relay (EMR)s) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate the State of Health or the Remaining Useful Life emphasises the limited analysis and understanding of expressive EMR […]
Tag: Deep Learning
Deep Learning Pipeline for State-of-Health Classification of Electromagnetic Relays
The transition of AI into industrial maintenance faces significant challenges due to the inherent complexities of industrial operations, such as variability in components due to manufacturing, integration, dynamic operating environments and variable loading conditions. Therefore, AI in critical industrial systems requires more advanced capabilities such as robustness, scalability and verifiability. Our paper presents the first […]
AI Safety 2021 Best Paper Award
We would like to congratulate our colleagues at Heriot-Watt, Liverpool University and DSTL, namely Zhao, X., Huang, W., Banks, A., Cox, V., Flynn, D., Schewe, S., & Huang, X, on winning the best paper award at AI Safety 2021 for our research on: Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational […]
New Publication: Prediction of voltage distribution using deep learning and identified key smart meter locations
The energy landscape for the Low-Voltage (LV) networks is undergoing rapid changes. These changes are driven by the increased penetration of distributed Low Carbon Technologies, both on the generation side (i.e. adoption of micro-renewables) and demand side (i.e. electric vehicle charging). The previously passive ‘fit-and-forget’ approach to LV network management is becoming increasing inefficient to […]