IoT Based Predictive Maintenance Tactics, Techniques and Procedure for Aircraft Engine

In the realm of aerospace engineering, ensuring the reliability and safety of aircraft engines is paramount. With the exponential rise of technologies built on Internet of Things (IoT), predictive maintenance has emerged as a promising approach to mitigate unexpected failures and optimize maintenance schedules. This paper presents a brief review of IoT-based predictive maintenance techniques specifically tailored for aircraft engines, utilizing the NASA generated Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. The review encompasses various data-driven methodologies including machine learning algorithms, deep learning models, and hybrid approaches, which exploit sensor data collected from aircraft engines to predict impending failures. Furthermore, the review highlights the challenges associated with implementing predictive maintenance strategies for mitigating aerospace related conflicts in real-time and proposes potential developments for that future research could bring to enhance the effectiveness and efficiency of predictive maintenance systems for aircraft engines.