Cart (Loading....) | Create Account
Close category search window
 

Time-Frequency Manifold as a Signature for Machine Health Diagnosis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Qingbo He ; Dept. of Precision Machinery & Precision Instrum., Univ. of Sci. & Technol. of China, Hefei, China ; Yongbin Liu ; Qian Long ; Jun Wang

Time-frequency analysis can reveal an intrinsic signature for representing nonstationary signals for machine health diagnosis. This paper proposes a novel time-frequency signature, called time-frequency manifold (TFM), by addressing manifold learning on generated time-frequency distributions (TFDs). The TFM is produced in three steps. First, the phase space reconstruction (PSR) is employed to reconstruct the inherent dynamic manifold embedded in an analyzed signal. Second, the TFDs are calculated to represent the nonstationary information in the phase space. Third, manifold learning is conducted on the TFDs to discover the intrinsic time-frequency structure of the manifold. The TFM combines nonstationary information and nonlinear information and may thus provide a better representation of machine health pattern. By evaluating the characteristics of top two TFMs, a synthetic TFM signature is further proposed to improve the time-frequency structure. The effectiveness of the TFM signature is verified by means of simulation studies and applications to diagnosis of gear fault and bearing defects. Results indicate the excellent merits of the new signature in noise suppression and resolution enhancement for machine fault signature analysis and health diagnosis.

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:61 ,  Issue: 5 )

Date of Publication:

May 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.