Year: 2025
Type: article
Abstract: : Aircraft engine performance monitoring is crucial to ensure flight safety, operational efficiency, and preventive maintenance. This paper presents a comprehensive review and discussion on the application of machine learning techniques to detect anomalies using N1 (fan speed) and N2 (core speed) parameters. These indicators are key for identifying irregularities in engine behavior during different flight conditions. Through a review of recent studies and implementation strategies, this paper demonstrates how data-driven models can improve anomaly detection and support predictive maintenance in modern aviation systems.
Authors: Sudeep S Shetty, Trupti Shripad Tagare
Institution: Dayananda Sagar College of Engineering
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410205362