AI-DRIVEN PREDICTIVE MAINTENANCE FOR AEROSPACE IOT SENSORS

Abstract

Predictive maintenance strategies consisting of the integration of artificial intelligence (AI) and the internet of things (IoT) have revolutionized their application in predictive maintenance as practiced in the aerospace engineering. An AI predictive maintenance framework is presented in this research to improve the reliability and also the performance of aerospace IoT sensors. The proposed framework makes use of machine learning algorithms and advanced data analytics to monitor in real time the data from the sensor and predicts the future potential of faults and optimizes the schedules for maintenance. Data fusion techniques are integrated with AL models like Convolutional neural network (CNN), long-short term memory (LSTM) to obtain good predictive capability. In addition, a strong data sharing ecosystem based on 6G communication technologies is able to provide real-time analysis and to easily integrate with current aviation maintenance systems. Adversarial attacks are also addressed by the proposed system, which provides data security and system resilience. The experimental results show that it improves the failure prediction rates by 30% over baselines resulting in 30% fewer unplanned downtime and improved operational efficiency. Digital Twin systems and advanced edge computing frameworks, emerging technologies, also improve the system performance in dynamic aerospace environments. The research shows how AI predictive maintenance solutions can achieve costs reduction, increase of safety and lifetime of critical aerospace systems. On that front, this work provides a significant contribution to existing advances in smart aviation systems in terms of being in line with Industry 4.0 principles.