Investigation of Distributed Data Processing Techniques for Predictive Maintenance in Fleet Operations

Abstract

Predictive maintenance in fleet operations is critical for minimizing downtime, reducing operational costs, and enhancing safety. However, the massive volume, velocity, and variety of data generated by vehicle sensors pose significant challenges for traditional data processing systems. This paper investigates distributed data processing techniques as a scalable and efficient solution for predictive maintenance in large-scale fleet operations. We explore frameworks such as Apache Hadoop, Spark, and Flink, which enable parallel processing, real-time analytics, and fault tolerance. Key topics include data acquisition, preprocessing, predictive modeling, and resource optimization in distributed environments. Mathematical formulations, such as speedup analysis via Amdahl's Law and stochastic gradient descent for model training, are integrated to quantify performance gains and algorithmic efficiency. The paper also addresses challenges in distributed systems, including load balancing, data partitioning, and latency minimization. By synthesizing theoretical foundations with practical implementations, this study provides a comprehensive framework for leveraging distributed computing to enhance predictive maintenance workflows. Results suggest that distributed techniques significantly improve scalability and computational efficiency, enabling real-time anomaly detection and failure prediction across fleets. This research contributes to the optimization of maintenance schedules, resource allocation, and operational reliability in transportation networks.