Fault diagnosis of rolling bearing based on empirical wavelet transform and fuzzy function

Abstract

The vibration signal of rolling bearing in mechanical equipment is nonlinear and nonstationary under the influence of various excitation sources. This paper combines empirical wavelet transform (EWT) with the fuzzy function and gives a method of fault signal recognition. Several modal components of the original signal can be obtained by decomposition. Components with more features of the original signal can represent some features of the original signal. The mutual information between each modal component and the original signal can be calculated. The noise and useful information can be identified according to the value of the mutual information, and then the noise of the signal can be filtered out to reconstruct the original signal The fuzzy functions of the known class signals and the signals to be identified are calculated, and the correlation coefficients of the fuzzy functions of the signals to be identified and the signals to be identified are calculated.

Research of Fault Diagnosis of Mine Rolling Bearing Based on Time-Frequency Analysis

“…In Figure 5 6 shows that the horizontal direction, where the load is applied in this test, has a significant amount of noise interference in the time-frequency distribution of vibration signals in two directions. The following fault diagnosis using time-frequency distribution will be challenging as a result [6] . Consequently, the signal is first denoised, and Figure 7…”