Sage Journals: Discover world-class research

Abstract

In view of the difficulty in accurately extracting composite fault features of rolling bearings, this paper started with three aspects of impact, complexity and cyclostationarity to set up a new fault feature index of signal (Kurtosis-SES_E-complexity) based on kurtosis, square envelope spectrum negentropy (SES_E) and complexity parameter to embody comprehensive description of fault information contained in the signals. Meanwhile, the decomposition level of variational mode decomposition (VMD) was determined with the maximum of Kurtosis-SES_E-complexity as criterion. The two modal components, IMF_max1 and IMF_max2, corresponding to maximum and sub-maximum of Kurtosis-SES_E-complexity index were chosen to determine the sensitive component signals which can better reflect the fault information, and prevent the loss of fault information. Finally, the chosen component signals were reconstructed in the form of equal weight, and the fault characteristics of rolling bearings were extracted by using the frequency spectrum of autocorrelation function of reconstructed signals for fault identification. For verify the validity of the presented method, the bearing fault data collected under multiple states was verified and analyzed and the presented method was contrasted with the widespread method of adaptively determining the decomposition level of VMD based on kurtosis. The result of the comparison has proved that the Kurtosis-SES_E-complexity index set up in the paper can represent bearing fault information more comprehensively, adaptively determine the decomposition level of VMD, and extract compound fault information of bearing effectively.

Keywords

kurtosis complexity parameter square envelope spectrum negative entropy VMD fault diagnosis

Get full access to this article

View all access options for this article.

References

Singh

Kumar

Dwivedi

. Compound fault prediction of rolling bearing using multimedia data. Multimed Tool Appl 2017; 76: 18771–18788.

Zhao

Fan

, et al. Rolling bearing composite fault diagnosis method based on EEMD fusion feature. J Mech Sci Technol 2022; 36: 4563–4570.

Gilles

. Empirical wavelet transform. IEEE Trans Signal Process 2013; 61(16): 3999–4010.

Yang

. Adaptive compound fault diagnosis of rolling bearings. J Vib Shock 2016; 35(10): 145–150.

Fan

, et al. The rolling bearing fault feature extraction based on the LMD and envelope demodulation. Math Probl Eng 2015; 2015: 1–13.

Qiao

, et al. A study on the extraction of characteristics of compound faults of rolling bearings based on ITD-AF-CAF. J vibroeng 2021; 23(7): 13–1590.

Liu

, et al. Composite fault diagnosis for rolling bearing based on parameter-optimized VMD. Measurement 2022; 201: 111637.

Chen

, et al. Negentropy spectrum decomposition and its application in compound fault diagnosis of rolling bearing. Entropy 2019; 21(5): 490.

Chen

Zhao

. Early fault feature extraction of rolling bearing based on optimized VMD and improved threshold denoising. J Vib Shock 2021; 40(13): 146–153.

10.

Dragomiretskiy

Zosso

. Variational mode decomposition. IEEE Trans Signal Process 2013; 62(3): 531–544.

11.

Guo

Fang

, et al. Intershaft bearing compound faults identification by using VMD and a new index: the activity parameter in Hjorth parameters. Nonlinear Dyn 2022; 110: 2657–2672.

12.

Chen

Hong

, et al. Early fault diagnosis of rolling bearings based on adaptive variational mode decomposition and the teager energy operator. J Vib Shock 2020; 39(08): 1–7.

13.

Zhu

Wang

, et al. Adaptive variational mode decomposition based on artificial fish swarm algorithm for fault diagnosis of rolling bearings. Proc IME C J Mech Eng Sci 2017; 231(4): 635–654.

14.

Babiker

Yan

, et al. Initial fault time estimation of rolling element bearing by backtracking strategy, improved VMD and infogram. J Mech Sci Technol 2021; 35: 425–437.

15.

Chang

Bao

Cheng

, et al. Improved VMD-KFCM algorithm for the fault diagnosis of rolling bearing vibration signals. IET Signal Process 2021; 15: 238–250.

16.

Zhang

Zhou

. Application of variational mode decomposition and permutation entropy for rolling bearing fault diagnosis. Int J Acoust Vib 2019; 24: 303–311.

17.

Zheng

Yue

. Fault feature extraction method of rolling bearing based on parameter optimized VMD. J Vib Shock 2021; 40(01): 86–94.

18.

Peng

. Fault diagnosis of rolling bearings based on VMD with adaptive parameters. Noise And Vib Control 2021; 41(05): 139–146.

19.

Hjorth

. EEG analysis based on time-domain properties. Electroencephalogr Clin Neurophysiol 1970; 29(3): 06–310.

20.

Liu

, et al. Application of the infogram and parameter optimization CYCBD in rolling bearing composite fault feature separation. J Vib Shock 2022; 41(10): 55–65.

21.

Liu

, et al. Application of optimized variational mode decomposition based on kurtosis and resonance frequency in bearing fault feature extraction. Trans Inst Meas Control 2020; 42(3): 518–527.

22.

Cong

Chen

, et al. Feature extraction of rotor-stator rubbing faults based on harmonic fusion vector bispectrum, ITD and Hjorth parameters. Proc Inst Mech Eng G J Aerosp Eng 2023; 237(3): 600–613.

23.

Fang

. Feature extraction of rolling bearing multiple faults based on correlation coefficient and Hjorth parameter. ISA Trans 2022; 129: 442–458.

24.

Fang

, et al. Feature extraction of mixed faults of intershaft bearing based on homologous information and Hjorth parameters. Measurement 2022; 196: 111253.

25.

Gao

. Incipient gear fault detection using adaptive impulsive wavelet filter based on spectral negentropy. Chin J Mech Eng 2022; 35: 10.

26.

Guo

Gao

. Identification of rotor–stator rub-impact faults based on dual complexity parameters. J Vib Control 2023; 29(19–20): 4526–4540.

27.

Zhou

, et al. An adaptive VMD method based on improved Goa to extract early fault feature of rolling bearings. Intern J of Innov Comp, Inform and Con 2019; 15(4): 1485–1505.

A diagnosis method of compound faults of rolling bearings based on VMD optimized from multi-dimensions

Abstract

Keywords

Get full access to this article

References