In this paper, bear fault is automatically diagnosed by using pattern recognition. To improve the resolution of
lower frequency part, we introduce scale factors to discrete wavelet composition (DWT). The modified DWT combined
with high order cumulates are used for vibration signal feature extraction. Besides we use principle component analysis to
reduce dimension of the feature data. This feature extraction method has a lower dimension and a higher resolution for
lower frequency parts. Therefore it can not only reveal the characteristics of non-linear relationship between amounts of
features, but also help to improve the speed and accuracy of classification. Finally neural network algorithm is used for
fault classification. Result shows that our method can accurately and efficiently identify the type of bearing failures.