Prognostics for electromagnetic relays using deep learning

Electromagnetic Relays (EMRs) 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 EMR-State of Health (SOH) or -Remaining Useful Life (RUL) emphasises the limited analysis and understanding of expressive EMR-Degradation Indicators (DIs), as well as accessibility and use of EMR life cycle data sets. Our research prioritises an analysis of the state-of-the-art literature and how to overcome these open challenges. A Deep Learning (DL) 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 (RUA) forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact Voltage (CV), Contact Current (CI) and Coil Current (CC)). Monte-Carlo Dropout (MCD) 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 (MAPE) of ±12 % over the course of the entire EMR life.