Temporal Graph Embeddings for Predictive Maintenance

Abstract

Predictive maintenance (PM) aims to anticipate equipment failures and schedule maintenance proactively, minimizing downtime and operational costs. Traditional PM approaches often rely on static data and historical failure patterns. However, equipment systems are dynamic and evolve over time, influenced by operational conditions, maintenance interventions, and component degradation. Graph neural networks (GNNs) have emerged as a powerful tool for analyzing complex systems represented as graphs, but their application to PM has been largely limited by their inability to effectively model temporal dependencies within the graph structure. This paper introduces a novel approach to graph embeddings that explicitly incorporates temporal information, leading to improved predictive maintenance accuracy. We propose a framework utilizing recurrent neural networks (RNNs) or transformers to learn embeddings that capture the dynamic evolution of node relationships and their associated attributes over time. The resulting embeddings are then used for downstream tasks such as anomaly detection and failure prediction. We demonstrate the effectiveness of our approach through theoretical analysis and a detailed explanation of the core concepts, highlighting the improvements gained compared to static graph embeddings. The key contribution lies in the ability to represent and leverage the temporal dynamics inherent in equipment systems, offering a significant advancement in PM methodologies.