Abstract
Aiming at the problem that the bearing fault signal is weak and usually interfered by the strong background noise, which makes the fault feature extraction very difficult, an enhanced variational mode decomposition (EVMD) technique is proposed. First, the autoregressive (AR) model was utilized to eliminate the stationary components in the signal in advance to reduce the noise interference and the maximum kurtosis of the residual signal was set as the target. Second, the maximum frequency-domain correlated kurtosis was adopted as the fitness value, and the decomposition modes K and quadratic penalty factor α in the VMD approach were adaptively selected by the whale optimization algorithm. Third, the reconstruction signal was acquired, then the enhanced envelope spectrum was employed to weaken the interference of irrelevant frequency components and the fault features of rolling element bearing could be extracted accurately. The results of simulation and experimental analysis show that the proposed algorithm can significantly reduce the noise interference and avoid the blindness selection of VMD parameters. The comparison with fix-parameter VMD and fast kurtogram approaches shows that the proposed technique can improve the effectiveness of defect signature extraction, which has a certain value for engineering application.
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References
Antoni J (2007) Fast computation of the kurtogram for the detection of transient faults. Mech Syst Signal Process 21(1):108–124
Antoni J (2016) The infogram: entropic evidence of the signature of repetitive transients. Mech Syst Signal Process 74:73–94
Gu X, Yang S, Liu Y et al (2016) Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis. Meas Sci Technol 27:125019
Moshrefzadeh A, Fasana A (2018) The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis. Mech Syst Signal Process 105:294–318
Xu Y, Tian W, Zhang K et al (2018) Application of enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis. Meas Sci Technol 30:035001
Zhang C, Liu Y (2020) A two-step denoising strategy for early-stage fault diagnosis of rolling bearings. IEEE Trans Instrum Meas 99:1–12
Wang L, Liu Z, Cao H et al (2020) Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis. Mech Syst Signal Process 142:106755
Gao Y, Karimi M, Kudreyko AA et al (2017) Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems. ISA Trans 78:98–104
Zhang L, Cai B, Xiong G et al (2020) Multistage fault feature extraction of consistent optimization for rolling bearings based on correlated kurtosis. Shock Vib 7:1–16
Yang R, Li H, Wang C et al (2018) Rolling element bearing weak feature extraction based on improved optimal frequency band determination. ARCHIVE Proc Inst Mech Eng C J Mech Eng Sci 2018:1–12
Li Y, Cheng G, Liu C (2020) Research on bearing fault diagnosis based on spectrum characteristics under strong noise interference. Measurement 169:108509
Xu Y, Cai Z, Cai X et al (2019) An enhanced multipoint optimal minimum entropy deconvolution approach for bearing fault detection of spur gearbox. J Mech Sci Technol 33:3–4
Buzzoni M, Antoni J, D’Elia G (2018) Blind deconvolution based on cyclostationarity maximization and its application to fault identification. J Sound Vib 432:569–601
Wang X, Yan X, He Y (2020) Weak fault detection for wind turbine bearing based on ACYCBD and IESB. J Mech Sci Technol 34(4):1399–1413
Miao Y, Zhao M, Lin J et al (2017) Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings. Mech Syst Signal Process 92:173–195
Zhu D, Zhang Y, He W et al (2020) Compound faults diagnosis of rolling element bearing using adaptive CYCBD and cross-correlation spectrum, shock and vibration. J Vib Shock 39(11):116–122
Grover C, Turk N (2020) Rolling element bearing fault diagnosis using empirical mode decomposition and Hjorth parameters. Proc Comput Sci 167:1484–1494
Xu Y, Cai Z, Ding K (2018) An enhanced bearing fault diagnosis method based on TVF-EMD and a high-order energy operator. Meas Sci Technol 29:095108
Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adva Adapt Data Anal 1(1):1–41
Chen W, Xiao Y (2019) An improved ABC algorithm and its application in bearing fault diagnosis with EEMD. Algorithms 12(4):72
Han T, Liu Q, Li Z et al (2019) Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD. Measurement 138:400–408
Bouhalais ML, Djebala A, Ouelaa N et al (2017) CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed. Int J Adv Manuf Technol 94:2475–2489
Zhao H, Li L (2016) Fault diagnosis of wind turbine bearing based on variational mode decomposition and teager energy operator. IET Renew Power Gener 11(4):453–460
Li H, Xu Y, An D et al (2019) Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings. J Low Freq Noise Vib Active Control 39(2):335–351
Yan X, Jia M (2019) Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings. Mech Syst Signal Process 122:56–86
Gu R, Chen J, Hong R et al (2020) Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator. Measurement 149:106941
Wang H, Jiang X, Guo W et al (2020) An enhanced VMD with the guidance of envelope negentropy spectrum for bearing fault diagnosis. Complexity 2020:1–23
Hua L, Tao L, Xing W et al (2020) An optimized VMD method and its applications in bearing fault diagnosis. Measurement 166:108185
Shankar KP, Annamalai KL, Kumar LS (2018) Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis. Trans Inst Meas Control 41:1923–1932
Mirjalili S, Lewis A et al (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
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Zhu, D., Liu, G., He, W. et al. Fault feature extraction of rolling element bearing based on EVMD. J Braz. Soc. Mech. Sci. Eng. 43, 567 (2021). https://doi.org/10.1007/s40430-021-03295-9
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DOI: https://doi.org/10.1007/s40430-021-03295-9