Prognostics for Electromagnetic Relays using Deep Learning

Lucas Kirschbaum, Valentin Robu, Jonathan Swingler, David Flynn

Research output: Contribution to journalReview articleScientificpeer-review

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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.

Original languageEnglish
Pages (from-to)4861-4895
Number of pages35
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • Electromagnetic relay
  • Monte-Carlo dropout
  • artificial intelligence
  • deep learning
  • predictive maintenance
  • prognostics
  • prognostics and health management
  • remaining useful life
  • temporal convolutional networks

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