| In recent years, mathematical morphology (MM) analysis has received nore and more attention for its good feature extraction ability. By choosing roper structuring element (SE) and corresponding morphological operations, his method can be employed to extract the fault feature of a bearing. In this hesis, the influence of SE on fault feature extraction is discussed. Then, a new ignal based triangular SE is proposed and its performance in fault feature:xtraction is tested by a set of real vibration signals obtained from bearing iperation. The main contents and results of this work are as follows:1) The influence of SE on fault feature extraction is discussed. As SE has significant impact on feature extraction results, the influence of SE geometry shape, height and length) on fault feature extraction is detailed. Results show hat more fault information can be extracted when the geometry of a SE is imilar to that of a signal.2) A signal based triangular SE is proposed. Since features in signals of earing with different faults are different, the SEs used for signal analysis hould also be tailored to well extract the fault feature from a particular signal. So a signal based triangular SE is proposed according to the statistics of the magnitude of a vibration signal.3) The performance of proposed signal based triangular SE in fault feature extraction is tested in this work. A classification method based on the proposed SE is employed to identify bearing with inner race fault, ball fault and outer race fault, respectively. Results show that all faults can be detected clearly and correctly. Compared with traditional envelope analysis, this method has better performance on the fault diagnosis of bearing, especially in the identification of ball fault, the location of outer race fault and the level of fault severity. The proposed SE can also give a clear identification of bearing fault even when the signal is mixed with high level background noise. |
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