An incremental clustering method for anomaly detection in flight data

• Main Authors: Zhao, Weizun, Li, Lishuai, Alam, Sameer, Wang, Yanjun
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Engineering::Aeronautical engineering; Air Traffic Management; Gaussian Mixture Model
• Online Access: https://hdl.handle.net/10356/155007
• Institution: Nanyang Technological University

Safety is a top priority for civil aviation. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot operations and detect any risks from such flight data. However, all existing anomaly detection methods are offlline learning - the models are trained once...