Abstract:

The non-stationary characteristics of the bearing fault signal is strong, and the signal is easy to be contaminated by the gear and other noise sources, the fault feature information is weak, especially when the rolling bearing is at the early fault stage , the above problem is particularly serious. To solve the problem above, a method of singular point recognition and feature extraction for the incipient bearing fault is presented based on instantaneous envelope scalogram entropy(IESE). The envelope signal is decomposed by reassigned wavelet scalogram, and the power spectrum entropy of each moment is calculated to obtain the instantaneous envelope scalogram entropy of the signal. The distortion position of the IESE curve of the signal is the most obvious time of bearing fault characterization. And then the optimal fault characterization phase(OFCP) of bearing fault signal could be extracted, and the OFCP could be used to extract the characteristic frequency of bearing fault. The results showed that the method could effectively extract the weak fault feature information of the signal of early fault bearing.

Key words: envelope scalogram, incipient fault, instantaneous envelope scalogram entropy, optimal fault characterization phase, rolling bearings