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Proactive Maintenance Alerts for ATG and Dispensers Based on Usage Patterns

Advanced Software Solutions for Fuel System Optimization

Authors:

Rohith Varma Vegesna

(Software Engineer 2)

Texas, USA

Email: rohithvegesna@gmail.com

Abstract

Proactive maintenance in fuel stations is crucial to minimizing operational disruptions, ensuring consistent fuel quality, and optimizing the functionality of dispensers and ATG systems. Fuel station operations heavily depend on the seamless functioning of Automatic Tank Gauging (ATG) systems and fuel dispensers, making real-time monitoring and predictive maintenance imperative to minimize downtime and optimize resource allocation. This paper presents an innovative software-driven framework that integrates AWS IoT, SNS, and machine learning-based anomaly detection to facilitate proactive maintenance. The proposed system continuously monitors and analyzes ATG and dispenser data, enabling early detection of anomalies such as slow fuel flow rates, erratic fuel consumption trends, and potential mechanical failures before they escalate into critical issues. The real-time alerts generated through AWS SNS empower maintenance teams to take immediate corrective actions, significantly reducing unplanned downtime and preventing financial losses. Furthermore, this framework incorporates automated fuel level tracking, allowing for intelligent scheduling of fuel deliveries based on real-time demand forecasting, ensuring uninterrupted fuel station operations. By leveraging advanced cloud computing and predictive analytics, the solution enhances operational efficiency and provides a scalable, data-driven approach to fuel station maintenance. The study demonstrates how cloud-based anomaly detection and automated alerts can transform maintenance workflows, making them more responsive, cost-effective, and reliable.

Keywords: Fuel dispenser monitoring, ATG anomaly detection, proactive maintenance, AWS IoT, real-time alerting, predictive analytics, automated fuel delivery.