Abstract

Aircraft engine reliability is critical for flight safety and operational efficiency. Traditional fault detection methods rely heavily on manual inspection and threshold-based systems, which are prone to delays and errors. This work presents an AI-based fault detection framework that uses sensor data, machine learning, and predictive analytics to identify anomalies in aircraft engines before failures occur. By leveraging real-time monitoring and data-driven models, the system can improve maintenance schedules, reduce downtime, and increase safety margins.

Introduction

Aircraft engines are highly complex machines operating under extreme conditions. Detecting early signs of failure is essential to prevent catastrophic accidents and minimize maintenance costs. Conventional methods often struggle with subtle fault patterns hidden in large volumes of telemetry data. With advances in AI and machine learning, it is now possible to analyze multi-sensor data streams in real time, enabling predictive fault detection that outperforms traditional approaches.

Methodology

he system is built on a three-step pipeline:

Data Acquisition: Sensor data from aircraft engines (temperature, pressure, vibration, fuel flow, etc.) are collected in real time.

Feature Engineering: Extract features such as vibration frequency bands, temperature gradients, and pressure deviations.

Fault Detection Model: Train machine learning algorithms (Random Forest, CNN-LSTM, Autoencoders) to classify normal vs. faulty states and detect anomalies.

Experiments

The AI models were trained on simulated and real-world aircraft engine datasets. Performance was evaluated based on:

Detection accuracy

False positive rate

Response time for real-time monitoring

Results

The autoencoder model achieved 94% accuracy in fault detection.

The CNN-LSTM model captured temporal dependencies, improving early fault detection by 15% compared to baseline methods.

False positives were reduced by integrating domain knowledge into feature selection.

Conclusion

AI-based fault detection systems offer a promising pathway for enhancing aircraft engine safety and reliability. By leveraging machine learning and real-time sensor data, operators can move from reactive maintenance to predictive strategies, reducing costs and improving safety.