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Fault diagnosis is confronted with two problems; how to ”measure” the growth of a fault and how to predict the remaining useful lifetime of such a failing component or machine. This paper attempts to solve these two problems by proposing a model of fault prognosis using wavelet basis neural network. Gaussian radial basis functions and Mexican hat wavelet frames are used as scaling functions and wavelets, respectively. The centers of the basis functions are calculated using a dyadic expansion scheme and a k-means clustering algorithm.
How to "measure the growth of a fault and how to predict the remaining useful of such a failing component or machine. This paper attempts to solve these two problems by proposing a model of fault prognosis using Gaussian radial basis functions and Mexican hat wavelet frames are used as scaling functions and wavelets, respectively. The centers of the basis functions are calculated using a dyadic expansion scheme and a k-means clustering algorithm.