Print Email Facebook Twitter Prognostics for Electromagnetic Relays using Deep Learning Title Prognostics for Electromagnetic Relays using Deep Learning Author Kirschbaum, Lucas (Heriot-Watt University) Robu, Valentin (TU Delft Algorithmics; Centrum Wiskunde & Informatica (CWI)) Swingler, Jonathan (Heriot-Watt University) Flynn, David (Heriot-Watt University) Date 2022 Abstract 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. Subject Electromagnetic relayMonte-Carlo dropoutartificial intelligencedeep learningpredictive maintenanceprognosticsprognostics and health managementremaining useful lifetemporal convolutional networks To reference this document use: http://resolver.tudelft.nl/uuid:f1647fa3-9571-4b44-8345-8b790e1cc3ac DOI https://doi.org/10.1109/ACCESS.2022.3140645 ISSN 2169-3536 Source IEEE Access, 10, 4861-4895 Part of collection Institutional Repository Document type review Rights © 2022 Lucas Kirschbaum, Valentin Robu, Jonathan Swingler, David Flynn Files PDF Prognostics_for_Electroma ... arning.pdf 8.82 MB Close viewer /islandora/object/uuid:f1647fa3-9571-4b44-8345-8b790e1cc3ac/datastream/OBJ/view