Abstract
The faults of rolling bearings frequently occur in rotary machinery, therefore the rolling bearings fault diagnosis is a very important research project. In this paper, a method of pattern recognition for fault diagnosis of rolling bearing is proposed, which is based on wavelet packet transformation combined with Statistics. Firstly, the wavelet packet analysis is utilized to divide the dynamic signal of rolling bearings, and the features information of rolling bearing’s dynamic signal is picked up, secondly, the extracted features are classified into several categories, and databases are built for each category. Finally, the new picked-up signals are compared with the standard signals in database, and then whether the rolling bearings have defects is diagnosed.
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