Figure 1.
Flowchart of EEMD algorithm.
Figure 2.
Original simulated signal and its spectrum.
A) waveform of original simulated signal; B) spectrum of the original simulated signal.
Figure 3.
IMF components decomposed by EEMD method.
Figure 4.
Spectra of separated signals by the proposed method.
A) spectrum of IC1; B) spectrum of IC2.
Table 1.
Cross correlation coefficient of the original simulated signal and IMFs.
Figure 5.
Flowchart of the experiment scheme.
Figure 6.
Experimental system for bearing diagnosis.
Figure 7.
Install location of the acceleration sensor.
Table 2.
Fault characteristic frequencies of rolling bearing at different speed.
Figure 8.
Original diagnosis signal waveforms at different rotating speed.
A) at 500 rpm; B) 900 rpm; C) 1300 rpm.
Figure 9.
Envelope spectra of the original signal at different rotating speed.
A) 500 rpm; B)900 rpm; B)1300 rpm.
Figure 10.
Envelope spectra of each level wavelet coefficients.
Table 3.
Cross correlation coefficient of the simulated signal and IMFs.
Figure 11.
Envelop spectra of IMF1–IMF6.
Figure 12.
Spectra of the separated signals by the proposed method at 900 rpm.
A) spectrum of the outer-race defect; B) spectrum of the unbalance fault; C) spectrum of the rollers defect.
Figure 13.
Spectra of the separated signals by the proposed method at 500 rpm.
A) Spectrum of the outer-race defect; B) Spectrum of the unbalance fault; C) Spectrum of the rollers defect.
Figure 14.
Spectra of the separated signals by the proposed method at 1300 rpm.
A) Spectrum of the outer-race defect; B) Spectrum of the unbalance fault; C) Spectrum of the rollers defect.
Table 4.
Energy ratio calculated by the different methods.