Data-Driven Anomaly Detection Framework for Complex Degradation Monitoring of Aero-Engine

Abstract

Data analysis is an important part of aero engine health management. In order to complete accurate condition monitoring, it is necessary to establish more effective analysis tools. Therefore, an integrated algorithm library dedicated for engine anomaly detection is established, which is PyPEFD (Python Package for Engine Fault Detection). Different algorithms for baseline modeling, anomaly detection and trend analysis are presented and compared. In this paper, the simulation data are used to verify the function of the anomaly detection algorithms, successfully completing the detection of multiple faults and comparing the accuracy algorithm under different conditions.