Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10368839 | Mechanical Systems and Signal Processing | 2005 | 16 Pages |
Abstract
The ability to give a prognosis for failure of a system is a valuable tool and can be applied to electric motors. In this paper, three wavelet-based methods have been developed that achieve this goal. Wavelet and filter bank theory, the nearest-neighbour rule, and linear discriminant functions are reviewed. A framework for the development of a fault detection and classification algorithm based on the coefficients calculated from the discrete wavelet transform and using clustering is described. An experimental set-up based on RT-Linux is described and results from testing are presented, verifying the analysis.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Wesley G. Zanardelli, Elias G. Strangas, Hassan K. Khalil, John M. Miller,