**6. Conclusions**

Bearings are easily damaged in harsh industrial environments, so the study of bearing fault feature extraction methods is particularly important. To solve the problems of low efficiency and residual noise in EEMD and FEEMD and extract the fault frequencies on the basis of obtaining effective IMFs, a fast, complementary ensemble empirical mode decomposition (FCEEMD) energy kurtosis mean filtering-based fault feature extraction method was proposed. By analyzing the CWRU and NASA bearing datasets, the validity and superiority of the proposed method were proved, and the following conclusions can be drawn.


The proposed method has good efficiency and reliability, and it is expected to be applied to the feature extraction of rotating machinery in actual production. It is worth noting that the proposed method has a relatively long process, which is not conducive to improving the real-time reliability of fault detection. In future production, the simplification of the process and the improvement of reliability will be new research points.

**Author Contributions:** Conceptualization, C.Z. and L.X.; methodology, C.Z.; software, L.X.; validation, Y.J., S.W. and Z.Z.; formal analysis, C.Z.; investigation, C.Z.; resources, L.X.; data curation, C.Z.; writing—original draft preparation, Y.J.; writing—review and editing, C.Z.; visualization, C.Z.; supervision, Y.J. and S.W.; project administration, L.X.; funding acquisition, L.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by the National Natural Science Foundation of China (No. 51765022 and No. 41971392), the Ph.D. research startup foundation of Yunnan Normal University (No. 01000205020503131), the Fundamental Research Program of Yunnan Province (No. 202201AU070055) and the Project of Educational Commission of Yunnan Province of China.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.

**Acknowledgments:** The authors sincerely thank the team for their guidance and thank the Case West Reserve University and the University of Cincinnati for their bearing datasets. The authors also sincerely thank the reviewers for their comments and suggestions, which contributed to the improvement of the paper.

**Conflicts of Interest:** The authors declare no conflict of interest.
