Abstract
Nowadays, the development of intelligent fault diagnostics has been restricted by two aspects. The first one is the serious deficiency of typical fault data samples and the other one is the difficulty of fault feature discovery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg and National Defense Industry Press
About this chapter
Cite this chapter
Zhang, W. (2016). Fault Analysis and Diagnosis Method Based on Statistical Learning Theory. In: Failure Characteristics Analysis and Fault Diagnosis for Liquid Rocket Engines. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49254-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-662-49254-3_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49252-9
Online ISBN: 978-3-662-49254-3
eBook Packages: EngineeringEngineering (R0)