A Novel Single‐Encounter‐Emphasise‐Attention Neural Network for Predictive Maintenance in Urban Infrastructure

ABSTRACT

The Riyadh Metro represents a significant project in Saudi Arabia, designed to transform urban transportation and reduce traffic congestion within the city. With six metro lines and 85 stations, the network is expected to serve millions of passengers daily, necessitating innovative digitally driven maintenance approaches to ensure reliable efficient operation. We hypothesise that integrating Internet of things (IoT) sensors, digital twin technology, and a temporal graph neural network (TempGNN) enables real‐time health monitoring. This paper introduces a real‐time digital health monitoring framework that incorporates IoT sensors, digital twin technology, a proposed TempGNN and a new module single encounter‐emphasise attention neural network (SEEA‐NN). TempGNN exploits both temporal patterns and spatial relationships in these sensor data to feed SEEA‐NN with its required data. SEEA‐NN is designed to detect and assess the importance of rare single‐occurrence failure precursors, often overlooked by conventional models, alongside recurring patterns. The proposed framework contains an input gating module to filter out noisy sensor data, a multihead encounter attention module that employs a specific attention head to isolate and enhance the signal from anomalous single‐encounter events, and a temporal synthesis layer that combines these emphasised features with sequential data for thorough analysis. This enables the framework to predict potential failures with high accuracy of approximately 98%, allowing for proactive maintenance scheduling and decreased unplanned downtime by more than 30% and maintenance expenses by around 25%, whilst also enhancing energy efficiency and passenger satisfaction in simulations. This approach demonstrates a scalable model for the digital transformation of large‐scale transport infrastructure and for implementing intelligent predictive maintenance in metro systems worldwide and in logistics fleets, container handling systems and freight rail networks.