Application of a modified fuzzy ARTMAP with feature-weight learning for the fault diagnosis of bearing
Considering different importance of the feature parameters to the fault conditions of bearing, a modified fuzzy ARTMAP (FAM) network model based on the feature-weight learning is presented in this paper. The features in time-domain, frequency-domain and wavelet-domain are extracted from the vibration signals to characterize the information relevant to the fault conditions of bearing. By the improved distance evaluation technique the optimal features are selected and the corresponding feature-weights which are assigned to the features to indicate their different importance to the fault conditions of bearing are obtained. Then they are combined with the modified FAM which is described by the weighted Manhattan distance and applied to the seven-class fault diagnosis of bearing. To assess the effectiveness and stability of the modified FAM network, bootstrapping method is employed to quantify the stability of the network performance statistically. Diagnosis results show that the modified FAM can more reliably and accurately recognize different fault classes.
modified fuzzy artmap fault diagnosis improved distance evaluation technique bootstrapping feature weighting