Time Series Analysis Methods and Applications for Flight Data
Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Science & Nature, Technology, Aeronautics & Astronautics
| Author: | Jianye Zhang, Peng Zhang | ISBN: | 9783662534304 |
| Publisher: | Springer Berlin Heidelberg | Publication: | December 22, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | Jianye Zhang, Peng Zhang |
| ISBN: | 9783662534304 |
| Publisher: | Springer Berlin Heidelberg |
| Publication: | December 22, 2016 |
| Imprint: | Springer |
| Language: | English |
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.
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.
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