A Study on Health Monitoring Machine Algorithm for Turbo-Fan Engine

The increasing complexity and safety-critical nature of aircraft engines have made effective health monitoring and predictive maintenance essential in modern aviation systems. Turbofan engines operate under extreme conditions, where unexpected component degradation can lead to serious failures, high maintenance costs, and operational downtime. Traditional maintenance strategies, such as reactive and preventive maintenance, are no longer sufficient to handle these challenges efficiently. As a result, data-driven approaches based on machine learning and deep learning have gained significant attention in prognostics and health management (PHM) applications. This review focuses on health monitoring and remaining useful life (RUL) prediction of turbofan engines using advanced deep learning techniques. It examines how sensor data collected from aircraft engines can be utilized to detect degradation patterns and estimate future engine health. Particular emphasis is placed on hybrid deep learning models that combine feature extraction and temporal sequence learning, such as convolutional neural networks, autoencoders, and long short-term memory networks. The study also discusses publicly available benchmark datasets, especially the NASA C-MAPSS dataset, which is widely used for validating RUL prediction models. Key challenges such as data imbalance, noise, model interpretability, and real-time deployment are highlighted. Overall, this review demonstrates that deep learning-based prognostic models play a crucial role in improving prediction accuracy, enhancing aviation safety, and enabling reliable condition-based maintenance strategies for turbofan engines.