| Bearing condition monitoring and fault diagnosis have been studied for many years. Popular techniques include those through advanced signal processing and pattern recognition technologies. Recently, some interesting results were published using pattern recognition for bearing diagnosis by means of features extracted from vibration signals through time domain and frequency domain analyses [Sun, et al, 1998]. In this work, segmentation parameters are proposed to further improve the sensitivity and reliability of the technique. Parameters extracted from the segmentation analysis reflect the variation of vibration signals associated with the bearing dynamics. A three-layered artificial neural network is applied to accomplish the non-linear mapping from the feature space to the two dimensional classification space. The mapping is conducted to create the best cluster effect for training samples belonging to the same class. Successful non-linear mapping through the neural network eliminates infra-class transformations as used in [Sun, et al, 1998]. Numerical experiments are performed to illustrate the effectiveness of the method. |
