Abstract

The rapid advancements in urban health infrastructure necessitate robust and proactive maintenance strategies to ensure system reliability, efficiency, and safety. Predictive maintenance, powered by deep learning models, has emerged as a transformative approach, enabling real-time fault detection, failure prediction, and optimization of maintenance schedules. This chapter explores the integration of Internet of Things (IoT) sensors and multi-modal data with deep learning algorithms to address the unique challenges in anomaly detection and predictive maintenance of urban health systems. Key challenges, such as handling heterogeneous data sources, ensuring data quality, and developing hybrid deep learning models for improved accuracy, are examined in detail. Additionally, innovative solutions like data fusion techniques, real-time alert systems, and multimodal predictive frameworks are presented. The chapter emphasizes how these advancements enhance system resilience, minimize downtime, and improve operational efficiency. Through detailed analysis and practical insights, this chapter provides a comprehensive framework for leveraging deep learning in predictive maintenance, ensuring sustainable and reliable urban health infrastructure.

Introduction

The unprecedented growth in urban health infrastructure demands maintenance strategies that are not only proactive but also predictive, ensuring operational reliability and efficiency [1]. Traditional maintenance practices, often reactive in nature, result in unplanned downtime and increased operational costs, which can jeopardize the reliability of critical health systems [2]. Predictive maintenance, leveraging the capabilities of deep learning algorithms, offers a revolutionary approach to addressing these challenges [3]. By analyzing patterns, anomalies, and trends in real-time data, predictive maintenance enables the identification of potential failures beforeoccur, ensuring seamless operation of urban health systems [4]. This paradigm shift enhances both the longevity and efficiency of infrastructure, supporting the increasing demand for sustainable urban health management [5]. 

The integration of Internet of Things (IoT) sensors with predictive maintenance systems forms a cornerstone of modern urban health infrastructure [6]. IoT devices generate a wealth of data, including real-time sensor readings, equipment performance metrics, and environmental parameters [7]. this deluge of information also introduces challenges such as data heterogeneity, scalability, and security concerns [8]. Deep learning algorithms, with their ability to process and learn from complex, high-dimensional data, provide an effective solution to these challenges [9].not only facilitate the extraction of meaningful insights but also enable real-time monitoring and analysis, thus allowing urban health systems to respond dynamically to potential threats or failures [10].