By Topic

An Enhanced Diagnostic System for Gear System Monitoring

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

1 Author(s)
Wilson Wang ; Lakehead Univ., Thunder Bay

The detection of the onset of damage in gear systems (e.g., gearboxes) is of great importance to a wide array of industries. In this paper, an enhanced diagnostic (ED) system is developed for real-time gear system condition monitoring. A neurofuzzy (NF) paradigm is adopted for pattern classification of the features from the energy, amplitude, and phase domains. The diagnostic reliability is enhanced by properly integrating predicted future machinery states that are forecast by recurrent NF predictors. An online training technique is proposed to improve the classifier's adaptive capability to accommodate different machinery conditions. The viability of this new monitoring system has been verified by experimental tests under different gear conditions. This proposed ED system has also been applied for real-time condition monitoring in multistage printing machines. The primary application has demonstrated its reliability as an effective monitoring tool for both production quality control and maintenance planning.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:38 ,  Issue: 1 )