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.
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