Rolling bearing fault diagnosis method based on GJO-VMD, multiscale fuzzy entropy, and GSABO-BP neural network

Abstract

Accurate fault diagnosis of rolling bearings is hindered by the weak nature of early fault signals and the limited availability of labeled data, especially under small-sample conditions. To overcome these challenges, this paper proposes a novel method combining golden jackal optimization (GJO) with improved variational mode decomposition (VMD), enhanced feature extraction, and optimized classification. First, GJO is used to optimally determine the key decomposition parameters of VMD, thereby improving the accuracy of vibration signal decomposition. A comprehensive discrimination factor algorithm then selects fault-sensitive intrinsic mode functions, and the signal is reconstructed to enhance fault characteristics. Multiscale fuzzy entropy is calculated from the reconstructed signals at multiple scales to build distinct state feature vectors. These vectors are fed into a back-propagation neural network optimized via the golden sine subtraction-average-based optimizer for precise fault classification. The method’s effectiveness is verified through simulation and experimental data. Compared with conventional approaches, it shows superior performance in extracting weak fault features and maintaining high diagnostic accuracy under small-sample scenarios. This integrated framework presents a robust solution for rolling bearing fault diagnosis.

Keywords

Rolling bearing / Fault diagnosis / Variational mode decomposition / Golden jackal optimization / Multiscale fuzzy entropy / Golden sine subtraction-average-based optimizer-back-propagation neural network

Funding

None.

Declaration of competing interest

The authors declare they have no competing interests.

References

[1] Song X, Wang H, Liu Y, Wang Z, Cui Y. A fault diagnosis method of rolling element bearing based on improved PSO and BP neural network. J Intell Fuzzy Syst. 2022; 43(5):5965-5971 https://doi.org/10.3233/JIFS-213485

[2] Hu R, Zhang M, Xiang Z, Mo J. Guided deep subdomain adaptation network for fault diagnosis of different types of rolling bearings. J Intell Manuf. 2023; 34(5):2225-2240 https://doi.org/10.1007/s10845-022-01910-7

[3] Lei Y, Li N, Guo L, Li N, Yan T, Lin J. Machinery health prognostics: a systematic review from data acquisition to RUL prediction. Mech Syst Signal Process. 2018;104:799- 834 https://doi.org/10.1016/j.ymssp.2017.11.016

[4] Liu R, Yang B, Zio E, Chen X. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mech Syst Signal Process. 2018;108:33- 47 https://doi.org/10.1016/j.ymssp.2018.02.016

[5] Zhou Y, Jin Z, Zhang Z, Geng Z, Zhou L. Adversarial subdomain adaptation method based on multi-scale features for bearing fault diagnosis. Math Found Comput. 2024; 7(4):485-511 https://doi.org/10.3934/mfc.2023024

[6] Kanneg D, Wang W. A wavelet spectrum technique for machinery fault diagnosis. J Signal Inf Process. 2011; 2(4):322-329 https://doi.org/10.4236/jsip.2011.24046

[7] Feng H, Chen R, Wang Y. Feature extraction for fault diagnosis based on wavelet packet decomposition: An application on linear rolling guide. Adv Mech Eng. 2018; 10(8):1687814018796367. https://doi.org/10.1177/1687814018796367

[8] Zhou X, Wang X, Wang H, et al. Method for denoising the vibration signal of rotating machinery through VMD and MODWPT. Sensors (Basel). 2023; 23(15):6904 https://doi.org/10.3390/s23156904

[9] Huang NE, Shen Z, Long SR, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Eng Sci. 1998; 454(1971):903-995 https://doi.org/10.1098/rspa.1998.0193

[10] Wu Z, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal. 2009; 1(01):1-41 https://doi.org/10.1142/S1793536909000047

[11] Zhou H, Jia M. EMD Hilbert [Analysis of rolling bearing fault diagnosis based on EMD and kurtosis Hilbert envelope demodulation]. Mechatron Eng. 2014; 31(9) https://doi.org/10.3969/j.issn.1001-4551.2014.09.008

[12] Yao F, Yang X, Ding F, Zhao M, Li S. EMD-AR [Fault diagnosis of rolling bearings based on wavelet threshold denoising, EMD-AR spectrum analysis, and extreme learning machine]. , [Manuf Technol Mach Tool.] 2023;(7):16-20 https://doi.org/10.19287/j.mtmt.1005-2402.2023.07.002

[13] Qin YF, Fu X, Li XK, Li HJ. ADAMS simulation and HHT feature extraction method for bearing faults of coal shearer. Processes. 2024; 12(1):164 https://doi.org/10.3390/pr12010164

[14] Zhang F, Gao S, Zhang W, Li G. Improved EEMD and overlapping group sparse second-order total variation. J Braz Soc Mech Sci Eng. 2024; 46(6):386 https://doi.org/10.1007/s40430-024-04965-0

[15] Damine Y, Bessous N, Pusca R, et al. A new bearing fault detection strategy based on combined modes ensemble empirical mode decomposition, KMAD, and an enhanced deconvolution process. Energies. 2023; 16(6):2604 https://doi.org/10.3390/en16062604

[16] Zhou X, Wang X, Wang H, et al. Rotor fault diagnosis method based on VMD symmetrical polar image and fuzzy neural network. Appl Sci (Basel). 2023; 13(2):1134 https://doi.org/10.3390/app13021134

[17] Dragomiretskiy K, Zosso D. Variational mode decomposition. IEEE Trans Signal Process. 2014; 62(1-4):531-544 https://doi.org/10.1109/TSP.2013.2288675

[18] Wei W, He G, Yang J, Li G, Ding S. Tool wear monitoring based on the gray wolf optimized variational mode decomposition algorithm and Hilbert-Huang transformation in machining stainless steel. Machines. 2023; 11(8):806 https://doi.org/10.3390/machines11080806

[19] Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A. Grasshopper optimization algorithm: theory, variants, and applications. IEEE Access. 2021;9:50001- 50024 https://doi.org/10.1109/ACCESS.2021.3067597

[20] Gandomi AH, Yang XS, Alavi AH. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput. 2013;29:17-35. https://doi.org/10.1007/s00366-011-0241-y

[21] Chopra N, Ansari MM. Golden jackal optimization: a novel nature-inspired optimizer for engineering applications. Expert Syst Appl. 2022;198:116924 https://doi.org/10.1016/j.eswa.2022.116924

[22] Chai X, Liu W. CEEMDAN [Gas valve fault diagnosis based on improved CEEMDAN and multi-scale fuzzy entropy]. [Mod Mach Tool Manuf Technol.] 2020;(10):140-143 https://doi.org/10.13462/j.cnki.mmtamt.2020.10.033

[23] Huang Z, Xie Y. Fault diagnosis method of rolling bearing based on BP neural network. In: 2009 International Conference on Measuring Technology and Mechatronics Automation. Vol 1. IEEE; 2009:647-649 https://doi.org/10.1109/ICMTMA.2009.246

[24] Chen G, Lu X, Liu T. [Rolling bearing fault diagnosis based on BWO-optimized VMD and singular spectrum entropy]. [Equip Manuf Technol.] 2023. https://www.fx361.com/page/2023/1124/22807868.shtml

[25] Shan YT, Liu T, Chu W, et al. [Application of genetic algorithm-optimized variational mode decomposition in bearing fault feature extraction]. [Noise Vib Control.] 2024; 44(1):148-153. https://nvc.sjtu.edu.cn/CN/Y2024/V44/I1/148

[26] Zhou X, Zhang Y, Wang Y, Ma F. VMD AR [Application of VMD-based AR mode and correlation dimension in fault feature extraction for gear]. Manuf Technol Mach Tool. 2021;(1):91-95.

[27] Zahid M, Ud Din F, Shah K, Abdeljawad T. Fuzzy fixed point approach to study the existence of solution for Volterra type integral equations using fuzzy Sehgal contraction. PLoS One. 2024; 19(6):e0303642 https://doi.org/10.1371/journal.pone.0303642

[28] Trojovský P, Dehghani M. Subtraction-average-based optimizer: a new swarm-inspired metaheuristic algorithm for solving optimization problems. Biomimetics. 2023; 8(2):149 https://doi.org/10.3390/biomimetics8020149

[29] Antoni J, Randall RB. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech Syst Signal Process. 2006; 20(2):308-331 https://doi.org/10.1016/j.ymssp.2004.09.002