A novel method of fault diagnosis for rolling element bearings based on the accumulated envelope spectrum of the wavelet packet

Abstract

Envelope analysis is an effective technique of fault diagnosis for rolling element bearings (REBs). However, envelope analysis needs to select an appropriate frequency band of the signal. Numerous selection methods have been investigated, such as spectral kurtosis (SK) and wavelet packet kurtogram (WPK), which are based on kurtosis. Nevertheless, existing approaches are sometimes unable to identify bearing faults due to the contamination of discrete and random noises. In this paper, a novel method of fault diagnosis for bearings is proposed, which accumulates all or part of sub-band signals’ envelope spectrums at a given level in wavelet packet rather than demodulates one selected frequency sub-band signal. Two simulated signals and a real signal are analyzed to test the performance of the novel method. In the first case, stochastic impulses are added into the simulated outer race fault signal. The analysis shows that the novel approach is more robust to stochastic impulses than the other two methods such as SK and WPK. In the second case, the simulated signal containing two outer race faults from different REBs is analyzed using the three methods, respectively. The results show that the novel approach captures more useful information and detects the two different REBs’ faults contained in the same signal more effectively. Furthermore, the effectiveness of the novel method is validated by identifying the characteristic fault frequency of the real REB.

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

“…However, the background noise makes it difficult to identify valid frequency component. To weaken the noise level and strengthen the signal to noise ratio, researchers have adopted some new approaches, like amplitude spectrum, power spectrum, cepstrum, and Hilbert demodulation [10][11][12][13], for bearing detection. However, the accuracy of these methods highly depends on the bearing dimensions and rotational speed [6].…”

Envelope analysis with a genetic algorithm-based adaptive filter bank for bearing fault detection

“…10 In addition, this study shows envelope power spectra obtained from 5-s raw vibration signals for each BCO, BCI, and BCR as reference cases. In 2013, Jiang et al detected bearing defects by accumulating the envelope power spectra of 16 subband signals resulting from four-level wavelet packet transform with the Coiflets 3 wavelet basis function.…”

“…In 2013, Jiang et al detected bearing defects by accumulating the envelope power spectra of 16 subband signals resulting from four-level wavelet packet transform with the Coiflets 3 wavelet basis function. 10 Figure 3 depicts envelope power spectra for BCO, BCI, and BCR.…”

Research on Gearbox Fault Detection and Diagnosis Based on Improved Spectral Kurtosis Algorithm

“…Energy operator demodulation method is adopted for envelope spectrum analysis of filtering signals. Compared with traditional Hilbert change method, energy operator demodulation method has high temporal resolution, high adaptive ability and simple computation process [2] [3] . By comparing spectral envelope with fault frequency theory values of gearbox, different fault types can be analyzed.…”