By Topic

HOS-based nonparametric and parametric methodologies for machine fault detection

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
$33 $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

2 Author(s)
T. W. S. Chow ; Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, China ; H. -Z. Tan

A framework for the detection and identification of machine faults through vibration measurements and higher order statistics (HOS) analysis is presented. As traditional signal processing techniques are based on the nonparametric magnitude analysis of vibration signals, in this paper, two different state-of-the-art HOS-based methods, namely, a nonparametric phase-analysis approach and a parametric linear or nonlinear modeling approach are used for machine fault diagnostic analysis. The focus of this paper is on the application of the techniques, not on the underlying theories. Each technique is described briefly and is accompanied by an experimental discussion on how it can be applied to classify the synthetic mechanical and electrical faults of induction machines compared with their normality. Promising results were obtained which show that the presented methodologies are possible approaches to perform effective preventive maintenance in rotating machinery

Published in:

IEEE Transactions on Industrial Electronics  (Volume:47 ,  Issue: 5 )