Prognostics for electromagnetic relays using deep learning

Kirschbaum, L., Robu, V., Swingler, J. and Flynn, D. (2022) Prognostics for electromagnetic relays using deep learning. IEEE Access, 10, pp. 4861-4895. (doi: 10.1109/ACCESS.2022.3140645)

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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 degradation indicators, as well as accessibility and use of EMR life cycle data sets. Prioritising these open challenges, a deep learning pipeline is presented in a prognostic context termed Electromagnetic Relay Useful Actuation Pipeline (EMRUA). Leveraging the attributes of causal convolution, a Temporal Convolutional Network (TCN) based architecture integrates an arbitrary long sequence of multiple features to produce a remaining useful switching actuations forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact-Voltage, Contact-Current). Monte-Carlo Dropout is utilised to estimate uncertainty during inference. The TCN hyperparameter space, as well as various methods to select and analyse long sequences of multivariate time series data are investigated. Subsequently, our results demonstrate improvements using the developed statistical feature-set over traditional, time-based features, commonly found in literature. EMRUA achieves an average forecasting mean absolute percentage error of ±12 % over the course of the entire EMR life.

Item Type:Articles
Additional Information:This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Embedded Intelligence, U.K., under Grant EP/L014998/1; in part by Baker Hughes; and in part by the Lloyds Register Foundation under Grant AtRI100015.
Glasgow Author(s) Enlighten ID:Flynn, Professor David
Authors: Kirschbaum, L., Robu, V., Swingler, J., and Flynn, D.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Access
ISSN (Online):2169-3536
Published Online:06 January 2022
Copyright Holders:Copyright © The Author(s) 2022
First Published:First published in IEEE Access 10:4861-4895
Publisher Policy:Reproduced under a Creative Commons Licence

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