Smart Aircraft Maintenance: A Web-Based Predictive Monitoring and Alert System Using Embedded Systems and IoT
Abstract
Aircraft Health Monitoring Systems (AHMS) have transformed the aviation industry by monitoring important aircraft parts and providing quick real-time feedback. This, in turn, gives real-time insight into aircraft performance, minimizing downtime and enhancing safety through predictive maintenance. This paper introduces a web-based Aircraft Health Monitoring and Alert System (AHMAS), which integrates embedded systems, edge computing, and network communication protocols to monitor aircraft health parameters, detect potential failures, and provide instant alerts to maintenance teams.
The proposed system uses a wide variety of sensors to track temperature, pressure, vibration, fuel levels, and structural integrity, ensuring that aircraft components operate within a safe limit. Data collected from these sensors is transmitted using efficient network protocols such as MQTT, HTTP REST, and WebSockets, thus providing seamless communication between the aircraft and ground control. An edge computing layer enables real-time data processing, reducing latency and bandwidth consumption while improving predictive maintenance capabilities.
This paper also explores potential security challenges, including cyber threats and data integrity risks. It also discusses future advancements such as digital twin technology, 5G connectivity, and AI-driven anomaly detection. Implementing a cloud-based web service ensures easy access to aircraft health metrics, empowering maintenance teams to take immediate action when needed. The paper's flowcharts, tables, and diagrams illustrate how this system can revolutionize aircraft maintenance and improve operational efficiency.
How to Cite This Article
Arjun Agaram Mangad, San Jose (2024). Smart Aircraft Maintenance: A Web-Based Predictive Monitoring and Alert System Using Embedded Systems and IoT . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 5(1), 1481-1485. DOI: https://doi.org/10.54660/.IJMRGE.2024.5.1.1481-1485