An incremental clustering method for anomaly detection in flight data

• المؤلفون الرئيسيون: Zhao, Weizun, Li, Lishuai, Alam, Sameer, Wang, Yanjun
• مؤلفون آخرون: School of Mechanical and Aerospace Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Computer science and engineering; Engineering::Aeronautical engineering; Air Traffic Management; Gaussian Mixture Model
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/155007

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...