Health condition monitoring of bearings based on multifractal spectrum feature with modified empirical mode decomposition-multifractal detrended fluctuation analysis
Restricted accessResearch articleFirst published online November, 2022
Health condition monitoring of bearings based on multifractal spectrum feature with modified empirical mode decomposition-multifractal detrended fluctuation analysis
Multifractal detrended fluctuation analysis (MFDFA) is proved to be a powerful tool for fault diagnosis of rotating machinery due to its ability to reveal multifractal structures hidden in nonstationary and nonlinear vibration signals. To overcome the discontinuity of the fitting scale-dependent trend and the poor adaptability of this algorithm, Empirical Mode Decomposition-Multifractal Detrended Fluctuation Analysis (EMD-MFDFA) is introduced. However, EMD-MFDFA runs into difficulties in reverse segmentation and the selection of the expected Intrinsic Mode Functions (IMFs). Aiming at solving these deficiencies, a Modified EMD-MFDFA (MEMD-MFDFA) approach with IMF selection strategy and Step-Moving Window (SMW) segmentation method is proposed in this paper. In MEMD-MFDFA, a metric for distinguishing deterministic and random components is established to select expected IMF components by scaling exponent. Meanwhile, SMW segmentation method is exploited to reduce the estimated errors caused by reverse segmentation. The robustness of the proposed method is investigated through comparing MEMD-MFDFA, MFDFA, and EMD-MFDFA by multifractality of simulated signals with different Signal-to-Noise Ratio (SNR). Furthermore, the proposed approach is applied to three bearing run-to-failure datasets containing three types of faults, and the results show that the multifeatures of the multifractal spectrum obtained by MEMD-MFDFA have the ability to simultaneously identify early fault and assess performance degradation of bearings.
LeeJWuFZhaoW, et al.Prognostics and health management design for rotary machinery systems-reviews, methodology and applications. Mech Syst Sig Process2014; 42(1-2): 314–334.
2.
WangJQiaoLYeY, et al.Fractional envelope analysis for rolling element bearing weak fault feature extraction. IEEE/CAA J Autom Sin2017; 4(2): 353–360.
3.
CerradaMSanchezR-VLiC, et al.A review on data-driven fault severity assessment in rolling bearings. Mech Syst Sig Process2018; 99: 169–196.
4.
HeWMiaoQAzarianM, et al.Health monitoring of cooling fan bearings based on wavelet filter. Mech Syst Sig Process2015; 64-65: 149–161.
5.
PeetersCAntoniJHelsenJ. Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring. Mech Syst Sig Process2020; 138: 106556.
6.
Machado de AzevedoHDAraujoAMBouchonneauN. A review of wind turbine bearing condition monitoring: State of the art and challenges. Renew Sust Energ Rev2016; 56: 368–379.
7.
HouBWangDXiaT, et al.Investigations on quasi-arithmetic means for machine condition monitoring. Mech Syst Sig Process2021; 151: 107451.
8.
SoualhiAMedjaherKZerhouniN. Bearing health monitoring based on hilbert-huang transform, support vector machine, and regression. IEEE Trans Instrum Meas2015; 64(1): 52–62.
9.
MengJYanCChenG, et al.Health indicator of bearing constructed by RMS-CUMSUM and GRRMD-CUMSUM with multi-features of envelope spectrum. IEEE Trans Instrum Meas2021; 70: 1–16.
10.
FengZLiangMChuF. Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples. Mech Syst Sig Process2013; 38(1): 165–205.
11.
NassserMMohammadiM. Condition monitoring using wavelet transform and fuzzy logic by vibration signals. Life Sci J2012; 9(4): 5680–5685.
12.
MiaoHHeD. Deep learning based approach for bearing fault diagnosis. IEEE Trans Ind Appl2017; 53(3): 3057–3065.
13.
TongQCaoJHanB, et al.A fault diagnosis approach for rolling element bearings based on RSGWPT-LCD bilayer screening and extreme learning machine. IEEE Access.2017; 5: 5515–5530.
14.
HuangNEShenZLongSR, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Roy Soc Lond A Math Phys Eng Sci1998; 454: 903–995.
15.
de MouraEPSoutoCRSilvaAA, et al.Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses. Mech Syst Sig Process2011; 25(5): 1765–1772.
16.
JiangXLiSWangY. Study on nature of crossover phenomena with application to gearbox fault diagnosis. Mech Syst Sig Process2017; 83: 272–295.
17.
LinJChenQ. A novel method for feature extraction using crossover characteristics of nonlinear data and its application to fault diagnosis of rotary machinery. Mech Syst Sig Process2014; 48(1–2): 174–187.
18.
LiuHZhangJChengY, et al.Fault diagnosis of gearbox using empirical mode decomposition and multi-fractal detrended cross-correlation analysis. J Sound Vib2016; 385: 350–371.
19.
KantelhardtJWZschiegnerSAKoscielny-BundeE, et al.Multifractal detrended fluctuation analysis of nonstationary time series. Phys A Stat Mech Appl2002; 316(1–4): 87–114.
20.
LinJChenQ. Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion. Mech Syst Sig Process2013; 38(2): 515–533.
21.
XiongQZhangWLuT, et al.A fault diagnosis method for rolling bearings based on feature fusion of multifractal detrended fluctuation analysis and alpha stable distribution. Shock Vib.2016; 2016: 1–12.
22.
LiuHWangXLuC. Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis. Mech Syst Sig Process2015; 60-61: 273–288.
23.
YuanZPengTAnD, et al.Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method. J Low Freq Noise V A2020; 39(4): 968–986.
24.
LiuYWangJLiY, et al.Feature extraction method based on VMD and MFDFA for fault diagnosis of reciprocating compressor valve. J Vibroeng2017; 19(8): 6007–6020.
25.
HuJZhangLLiangW. Dynamic degradation observer for bearing fault by MTS-SOM system. Mech Syst Sig Process2013; 36(2): 385–400.
26.
ChenGYanCMengJ, et al.A novel two-stage feature extraction based on multi-fractal spectrum with CI-VMD for early fault diagnosis of bearing. In: Proceedings of IEEE International Conference Prognostics and System Health Management, Jinan, China, 23–25 October, 2020, 2020, pp. 261–269.
27.
IhlenEAF. Multifractal analyses of response time series: a comparative study. Beh Res Meth2013; 45(4): 928–945.
28.
DuWKangMPechtM. Fault diagnosis using adaptive multifractal detrended fluctuation analysis. IEEE Trans Ind Electron2020; 67(3): 2272–2282.
29.
GuG-FZhouW-X. Detrending moving average algorithm for multifractals. Phys Rev E2010; 82(1): 011136.
30.
SchumannAYKantelhardtJW. Multifractal moving average analysis and test of multifractal model with tuned correlations. Phys A Stat Mech Appl2011; 390(14): 2637–2654.
31.
DuWGuoZGongX, et al.Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis. Proc Inst Mech Eng C: J Mech Eng Sci2017; 231(22): 4139–4149.
32.
MohantySGuptaKKRajuKS. Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD. Measurement.2018; 117: 200–220.
33.
MertAAkanA. Detrended fluctuation thresholding for empirical mode decomposition based denoising. Digit Signal Process2014; 32: 48–56.
34.
PengCKBuldyrevSVHavlinS, et al.Mosaic organization of DNA nucleotides. Phys Rev E1994; 49(2): 1685–1689.
35.
DongXLiGJiaY, et al.Non-iterative denoising algorithm for mechanical vibration signal using spectral graph wavelet transform and detrended fluctuation analysis. Mech Syst Sig Process2021; 149.
36.
RillingGFlandrinP. One or two frequencies: the empirical mode decomposition answers. IEEE Trans Signal Process2007; 56(1): 85–95.
37.
BoashashB. Estimating and interpreting the instantaneous frequency of a signal. II. Algorithms and applications. IEEE Proc.1992; 80(4): 540–568.
38.
ZhouYLeungY. Multifractal temporally weighted detrended fluctuation analysis and its application in the analysis of scaling behavior in temperature series. J Stat Mech-Theory E2010; 2010(06): P06021.
39.
QueZJinXXuZ. Remaining useful life prediction for bearings based on a gated recurrent unit. IEEE Trans Instrum Meas2021; 70: 3511411.
40.
MeneveauCSreenivasanKR. Simple multifractal cascade model for fully developed turbulence. Phys Rev Lett1987; 59(13): 1424.
41.
RandallRBSawalhiNCoatsM. A comparison of methods for separation of deterministic and random signals. Int J Cond Monit2011; 1(1): 11–19.
42.
PeetersCGuillaumePHelsenJ. A comparison of cepstral editing methods as signal pre-processing techniques for vibration-based bearing fault detection. Mech Syst Sig Process2017; 91: 354–381.
43.
KantelhardtJWKoscielny-BundeERegoH, et al.Detecting long-range correlations with detrended fluctuation analysis. Phys A Stat Mech Appl2001; 295(3): 441–454.
El-ThaljiIJantunenE. A descriptive model of wear evolution in rolling bearings. Eng Fail Anal2014; 45: 204–224.
46.
RenLLvW, Remaining useful life estimation of rolling bearings based on sparse representation. In: Proceeding of 7th International Conference Mechanical Aerospace Engineering, London, UK, 18–20 July, 2016, 2016, pp. 209–213.
47.
ArakereNKSubhashG. Work hardening response of M50-NiL case hardened bearing steel during shakedown in rolling contact fatigue. Mater Sci Technol2012; 28(1): 34–38.
