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Zhang et al. / IJOCTA, Vol.15, No.4, pp.649-669 (2025)

                1998;454(1971):903-995                            for engineering applications. Expert Syst Appl.
                https://doi.org/10.1098/rspa.1998.0193            2022;198:116924
             10. Wu Z, Huang NE. Ensemble empirical mode          https://doi.org/10.1016/j.eswa.2022.116924
                decomposition:  a noise-assisted data analysis  22. Chai X, Liu W.   CEEMDAN       [Gas valve
                method. Adv Adapt Data Anal. 2009;1(01):1-41      fault diagnosis based on improved CEEMDAN
                https://doi.org/10.1142/S1793536909000047         and multi-scale fuzzy entropy]. [Mod Mach Tool
             11. Zhou H, Jia M. EMD Hilbert [Analysis of rolling  Manuf Technol.] 2020 ;(10):140-143
                bearing fault diagnosis based on EMD and kur-     https://doi.org/10.13462/j.cnki.mmtamt.2020.
                tosis Hilbert envelope demodulation]. Mechatron   10.033
                Eng. 2014;31(9)                               23. Huang Z, Xie Y. Fault diagnosis method of rolling
                https://doi.org/10.3969/j.issn.1001-              bearing based on BP neural network. In: 2009
                4551.2014.09.008                                  International Conference on Measuring Technol-
             12. Yao F, Yang X, Ding F, Zhao M, Li S. EMD-AR      ogy and Mechatronics Automation. Vol 1. IEEE;
                [Fault diagnosis of rolling bearings based on     2009:647-649
                wavelet threshold denoising, EMD-AR spectrum      https://doi.org/10.1109/ICMTMA.2009.246
                analysis, and extreme learning machine]. , [Manuf  24. Chen G, Lu X, Liu T. [Rolling bearing fault diag-
                Technol Mach Tool.] 2023;(7):16-20                nosis based on BWO-optimized VMD and singu-
                https://doi.org/10.19287/j.mtmt.1005-             lar spectrum entropy]. . [Equip Manuf Technol.]
                2402.2023.07.002                                  2023. https://www.fx361.com/page/2023/1124/
             13. Qin YF, Fu X, Li XK, Li HJ. ADAMS simulation     22807868.shtml
                and HHT feature extraction method for bearing  25. Shan YT, Liu T, Chu W, et al.  [Application
                faults of coal shearer. Processes. 2024;12(1):164  of genetic algorithm-optimized variational mode
                https://doi.org/10.3390/pr12010164                decomposition in bearing fault feature extrac-
             14. Zhang F, Gao S, Zhang W, Li G. Improved          tion].  [Noise Vib Control.] 2024;44(1):148-153.
                EEMD and overlapping group sparse second-         https://nvc.sjtu.edu.cn/CN/Y2024/V44/I1/148
                order total variation. J Braz Soc Mech Sci Eng.  26. Zhou X, Zhang Y, Wang Y, Ma F. VMD AR
                2024;46(6):386                                    [Application of VMD-based AR mode and cor-
                https://doi.org/10.1007/s40430-024-04965-0        relation dimension in fault feature extraction for
             15. Damine Y, Bessous N, Pusca R, et al. A new bear-  gear]. Manuf Technol Mach Tool. 2021;(1):91-95.
                ing fault detection strategy based on combined  27. Zahid M, Ud Din F, Shah K, Abdeljawad T.
                modes ensemble empirical mode decomposition,      Fuzzy fixed point approach to study the exis-
                KMAD, and an enhanced deconvolution process.      tence of solution for Volterra type integral equa-
                Energies. 2023;16(6):2604                         tions using fuzzy Sehgal contraction. PLoS One.
                https://doi.org/10.3390/en16062604                2024;19(6):e0303642
             16. Zhou X, Wang X, Wang H, et al. Rotor fault diag-  https://doi.org/10.1371/journal.pone.0303642
                nosis method based on VMD symmetrical polar   28. Trojovsk´y P, Dehghani M. Subtraction-average-
                image and fuzzy neural network. Appl Sci (Basel).  based optimizer: a new swarm-inspired meta-
                2023;13(2):1134                                   heuristic algorithm for solving optimization prob-
                https://doi.org/10.3390/app13021134               lems. Biomimetics. 2023;8(2):149
             17. Dragomiretskiy K, Zosso D. Variational mode      https://doi.org/10.3390/biomimetics8020149
                decomposition. IEEE Trans Signal Process.     29. Antoni J, Randall RB. The spectral kurtosis: ap-
                2014;62(1-4):531-544                              plication to the vibratory surveillance and diag-
                https://doi.org/10.1109/TSP.2013.2288675          nostics of rotating machines. Mech Syst Signal
             18. Wei W, He G, Yang J, Li G, Ding S. Tool          Process. 2006;20(2):308-331
                wear monitoring based on the gray wolf opti-      https://doi.org/10.1016/j.ymssp.2004.09.002
                mized variational mode decomposition algorithm
                and Hilbert–Huang transformation in machining
                stainless steel. Machines. 2023;11(8):806
                https://doi.org/10.3390/machines11080806      Jingsong Zhang is a current master’s student
             19. Meraihi Y, Gabis AB, Mirjalili S, Ramdane-   at the Engineering College of Sabah University in
                Cherif A. Grasshopper optimization algorithm:  Malaysia.
                theory, variants, and applications. IEEE Access.  https://orcid.org/0009-0001-6397-3454
                2021;9:50001-50024
                https://doi.org/10.1109/ACCESS.2021.3067597
             20. Gandomi AH, Yang XS, Alavi AH. Cuckoo search
                                                              Xiaolong Zhou is a vice dean and associate pro-
                algorithm:  a metaheuristic approach to solve
                structural optimization problems. Eng Comput.  fessor of the Mechanical Engineering College of
                2013;29:17-35                                 Beihua University. He has a high level of aca-
                https://doi.org/10.1007/s00366-011-0241-y     demic expertise in the fields of mechanical engi-
             21. Chopra N, Ansari MM. Golden jackal opti-     neering and control engineering.
                mization:  a novel nature-inspired optimizer     https://orcid.org/0000-0002-0934-6046
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