Scalable Data Pipelines for Real-time Predictive Maintenance in Edge Computing Environments

Abstract

Predictive maintenance leverages machine learning and real-time data analytics to anticipate equipment failures before they occur, thereby reducing downtime and optimizing operational efficiency. However, the deployment of such systems in edge computing environments introduces challenges related to latency, scalability, and resource constraints. This paper presents a scalable architecture for data pipelines that enables real-time predictive maintenance at the edge. We propose a modular pipeline design combining lightweight edge processing, efficient data streaming, and cloud-based model orchestration. The architecture is evaluated using industrial sensor data and edge devices in a simulated smart manufacturing environment. Our results demonstrate significant improvements in latency reduction, system scalability, and fault prediction accuracy, validating the effectiveness of the proposed approach for real-world edge deployments.