ments LabVIEW.РAn ensemble approach based on wavelet packet entropy and clustering analysis is presented to diagnose faults in the rolling bearing vibration signal research. In order to certify the validity of the proposed method, the wavelet packet entropy and clustering analysis approach were applied in bearing fault diagnosis experiment of West Reserve University. The experimental results show that the recognition rate of the proposed approach is much higher than the K-means in rolling bearing fault recognition . Combined with the characteristics of fault diagnosis, a remote Rolling bearing fault diagnosis system was designed by the virtual instrument software LabVIEW and MATLAB toolbox.РKey words: Wavelet packet entropy; Subtractive clustering; K-means; Fault diagnosis; Rolling bearing
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