# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 6 Discussion and Conclusions
### Conclusions  
(1) The simulation signals of strong background noise, the relatively pure CWRU data set of the experimental environment, the NASA dataset with weak noise and the NASA dataset for SNR=10 dB Gaussian white noise were selected for subsequent analysis. Under background noises of different intensities, the fault characteristic frequencies and harmonic characteristics extracted by the proposed method are more effective and obvious. In contrast, MEWT, FK, EMD and other methods are sensitive to noise, and the extraction effect is feeble or even invalid.  
(2) In a practical environment, due to the influence of background noise, parameterless clustering scale-space histogram will divide the spectrum into a variety of narrow bands. When an ASN is used to select the center frequency, the high value will appear in numerous bands or even a misjudgment. In this paper, the correlation coefficient is used as an adaptive factor to modify the ASN, and the information fusion between them improves the selection accuracy. Through the experimental  
Figure 29: Results using spectral kurtosis: (**a**) kurtogram and (**b**) Hilbert envelope spectrum.  
verification of different intensity noise backgrounds, the AASN can effectively locate the fault center frequency and show better universality and robustness.  
(3) A large number of experiments show that when the frequency band is set too narrow, the fault information will be weakened or even unable to be extracted. When the frequency band is too wide, more serious noise interference will be introduced, and the feature information will be submerged by the noise signal. In this paper, the correlation coefficient is selected as the judgment index of frequency band merging. If the correlation coefficient of the adjacent mode is greater than 0.25, it is merged with the central mode. In other words, the correlation coefficient is equivalent to the adaptive selection of the frequency band according to the noise intensity; therefore, the feasibility of band merging is verified in all experiments. In our future work, we will consider more reasonable approaches to optimize the parameters of the proposed method and add other denoising techniques.  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] provided guidance and recommendations for research; [PERSON] contributed to the contents and writing of the manuscript. All authors have read and approved the final manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos. 51765022 and 61663017) and Science & Research Program of Yunnan Province (No. 2015 ZC005).  
The author sincerely thanks the team for their guidance, and thanks the Case West Reserve University and the University of Cincinnati for their bearing datasets. The author sincerely expresses thanks to the reviewers for taking the time to review the paper in a busy schedule.  
The authors declare no conflict of interest.