Time Series Analysis Methods and Applications for Flight Data

• Κύριοι συγγραφείς: Zhang, Jianye (Συγγραφέας), Zhang, Peng (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2017.
• Θέματα: Engineering.; Data mining.; Artificial intelligence.; Computational intelligence.; Aerospace engineering.; Astronautics.; Aerospace Technology and Astronautics.; Computational Intelligence.; Data Mining and Knowledge Discovery.; Artificial Intelligence (incl. Robotics).

 This book focuses on different facets of flight data analysis, including the basic goals, methods, and implementation techniques. As mass flight data possesses the typical characteristics of time series, the time series analysis methods and their application for flight data have been illustrated from several aspects, such as data filtering, data extension, feature optimization, similarity search, trend monitoring, fault diagnosis, and parameter prediction, etc. An intelligent information-processing platform for flight data has been established to assist in aircraft condition monitoring, training evaluation and scientific maintenance. The book will serve as a reference resource for people working in aviation management and maintenance, as well as researchers and engineers in the fields of data analysis and data mining. 

 Introduction -- Preprocessing of Flight Data -- Typical Time Series Analysis for Flight Data -- Similarity Search for Flight Data -- Condition Monitoring and Trend Prediction Based on Flight Data -- Design and Implementation Of flight Data Mining System.