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 Deep Learning (DL) based strategy for the classification of the State-Of-Health (SOH) of Electromagnetic Relays (EMR). The DL strategy scales with high-volumes of multivariate time-series data whilst automating labour intensive feature extraction requirements. Our pipeline is trained and evaluated on data generated from EMR life-cycle tests. We report a high classification accuracy and discriminatory power of the EMR-SOH classifier. The findings from our paper demonstrate the potential of AI pipelines for maintenance decision making of components in critical applications, providing a transferable AI based predictive maintenance solution that scales with large data quantities.
Deep Learning Pipeline for State-of-Health Classification of Electromagnetic Relays | IEEE Conference Publication | IEEE Xplore