Deconvolution methods have been widely used in machinery fault diagnosis. However, their application would be confined due to the heavy noise and complex interference since the fault feature in the measured signal becomes rather weak. Time synchronous averaging (TSA) can enhance the periodic components and suppress the others by the comb filter function. And in the iteration process of the deconvolution methods, the filtered signal after each iteration can be further processed using TSA, and the time delay with maximum Gini index value is refined as the iterative period for the next iteration. Benefitting from these advantages, a period-refined maximum second-order cyclostationarity blind deconvolution (PRCYCBD) using TSA is proposed for the weak fault detection of rolling element bearings (REBs) in this paper. Firstly, without any prior knowledge, the proposed method which can estimate the period more accurately is more suitable for the weak fault detection of REBs, especially incipient fault. Secondly, TSA is firstly applied to estimate the iterative period rather than just depending on the Signal Noise Ratio (SNR) of the filtered signal in the iterative process . Furthermore, the new improvement frame can be expanded to other deconvolution methods using iterative algorithms, especially under heavy noise. Finally, a simulation with a slight bearing fault as well as two real experimental data including the vibration signal with the wind turbine bearing fault and the acoustical signal with the locomotive wheel bearing fault is used to verify the superiority of the proposed PRCYCBD compared with the traditional minimum entropy deconvolution and the traditional autocorrelation-improved cyclostationarity blind deconvolution.
HuangWLiNSelesnickI, et al. Nonconvex group sparsity signal decomposition via convex optimization for bearing fault diagnosis. IEEE Trans Instr Measur2020; 69: 4863–4872.
2.
YangDLvYYuanR, et al. Robust fault diagnosis of rolling bearings via entropy-weighted nuisance attribute projection and neural network under various operating conditions. Struct Health Monit2022; 21: 14759217221077414.
3.
PangBNazariMSunZ, et al. An optimized variational mode extraction method for rolling bearing fault diagnosis. Struct Health Monit2021; 21: 558–570.
4.
JiangXWangJShenC, et al. An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis. Struct Health Monit2020; 20: 147592172097085.
5.
LiuJXuZ.A simulation investigation of lubricating characteristics for a cylindrical roller bearing of a high-power gearbox. Tribol Int2022; 167: 107373.
6.
LiuJWangLShiZ. Dynamic modelling of the defect extension and appearance in a cylindrical roller bearing. Mech Syst Signal Process2022; 173: 109040.
7.
HeCWuTGuR, et al. Rolling bearing fault diagnosis based on composite multiscale permutation entropy and reverse cognitive fruit fly optimization algorithm—Extreme learning machine. Measurement2021; 173: 108636.
8.
HuangWChengJYangY. Rolling bearing fault diagnosis and performance degradation assessment under variable operation conditions based on nuisance attribute projection. Mech Syst Signal Process2019; 114: 165–188.
9.
ChengYZhouNZhangW, et al. Application of an improved minimum entropy deconvolution method for railway rolling element bearing fault diagnosis. J Sound Vibr2018; 425: 53–69.
10.
ChaofanHYanxueW. Research on multi-channel signal denoising method for multiple faults diagnosis of rolling element bearings based on tensor factorization. J Mech Eng2019; 55: 50–57.
11.
LiuJXuZZhouL, et al. A statistical feature investigation of the spalling propagation assessment for a ball bearing. Mech Mach Theor2019; 131: 336–350.
12.
ChengYChenBMeiG, et al. A novel blind deconvolution method and its application to fault identification. J Sound Vibr2019; 460: 114900.
13.
LiuCGrylliasK. A semi-supervised Support Vector Data Description-based fault detection method for rolling element bearings based on cyclic spectral analysis. Mech Syst Signal Process2020; 140: 106682.
14.
LeiYQiaoZXuX, et al. An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings. Mech Syst Signal Process2017; 94: 148–164.
15.
HeWDingYZiY, et al. Sparsity-based algorithm for detecting faults in rotating machines. Mech Syst Signal Process2016; 72–73: 46–64.
16.
McFaddenPSmithJ. Model for the vibration produced by a single point defect in a rolling element bearing. J Sound Vibr1984; 96: 69–82.
17.
WangYXuGLuoA, et al. An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection. J Sound Vibr2016; 367: 233–249.
18.
MiaoYZhaoMLinJ, et al. Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings. Mech Syst Signal Process2017; 92: 173–195.
19.
MiaoYZhangBLiC, et al. Feature mode decomposition: new decomposition theory for rotating machinery fault diagnosis. IEEE Trans Ind Electron2022; 70(2): 1949–1960.
20.
DyerDStewartR. Detection of rolling element bearing damage by statistical vibration analysis. J Mech Des1978; 100: 229–235.
21.
SawalhiNRandallRBEndoH. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech Syst Signal Process2007; 21: 2616–2633.
22.
EndoHRandallRB. Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter. Mech Syst Signal Process2007; 21: 906–919.
23.
RandallRBAntoniJ. Rolling element bearing diagnostics—A tutorial. Mech Syst Signal Process2011; 25: 485–520.
24.
XuXZhaoMLinJ, et al. Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis. Measurement2016; 91: 385–397.
25.
MiaoYZhaoMHuaJ. Research on sparsity indexes for fault diagnosis of rotating machinery. Measurement2020; 158: 107733.
26.
McDonaldGLZhaoQZuoMJ. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection. Mech Syst Signal Process2012; 33: 237–255.
27.
McDonaldGLZhaoQ. Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection. Mech Syst Signal Process2017; 82: 461–477.
28.
MiaoYZhaoMLiangK, et al. Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal. Renew Energy2020; 151: 192–203.
29.
MiaoYLiCShiH, et al. Deep network-based maximum correlated kurtosis deconvolution: a novel deep deconvolution for bearing fault diagnosis. Mech Syst Signal Process2023; 189: 110110.
30.
BuzzoniMAntoniJD’EliaG. Blind deconvolution based on cyclostationarity maximization and its application to fault identification. J Sound Vibr2018; 432: 569–601.
31.
ZhangBMiaoYLinJ, et al. Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis. Mech Syst Signal Process2021; 158: 107736.
32.
WangZZhouJDuW, et al. Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution. Mech Syst Signal Process2022; 162: 108018.
33.
ChenBZhangWSongD, et al. Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement. Measurement2020; 159: 107804.
34.
ZhaoMJiaX. A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery. Mech Syst Signal Process2017; 94: 129–147.
35.
MiaoYZhangBLinJ, et al. A review on the application of blind deconvolution in machinery fault diagnosis. Mech Syst Signal Process2022; 163: 108202.
MiaoYZhangBZhaoM, et al. Period-oriented multi-hierarchy deconvolution and its application for bearing fault diagnosis. ISA Trans2021; 114: 455–469.
38.
LeeJYNandiAK. Extraction of impacting signals using blind deconvolution. J Sound Vibr2000; 232: 945–962.
39.
BraunS. The synchronous (time domain) average revisited. Mech Syst Signal Process2011; 25: 1087–1102.
40.
CombetFGelmanL. An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor. Mech Syst Signal Process2007; 21: 2590–2606.
41.
MarkWD. Time-synchronous-averaging of gear-meshing-vibration transducer responses for elimination of harmonic contributions from the mating gear and the gear pair. Mech Syst Signal Process2015; 62–63: 21–29.
42.
HaJMYounBDOhH, et al. Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines. Mech Syst Signal Process2016; 70–71: 161–175.
43.
MiaoYWangJZhangB, et al. Practical framework of Gini index in the application of machinery fault feature extraction. Mech Syst Signal Process2022; 165: 108333.
44.
WangDPengZXiL. The sum of weighted normalized square envelope: a unified framework for kurtosis, negative entropy, Gini index and smoothness index for machine health monitoring. Mech Syst Signal Process2020; 140: 106725.
45.
ZhaoMLinJWangX, et al. A tacho-less order tracking technique for large speed variations. Mech Syst Signal Process2013; 40: 76–90.
46.
ZhengKLuoJZhangY, et al. Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD. ISA Trans2019; 89: 256–271.
47.
YaoRJiangHLiX, et al. Bearing incipient fault feature extraction using adaptive period matching enhanced sparse representation. Mech Syst Signal Process2022; 166: 108467.
48.
LuSWangXHeQ, et al. Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals. J Sound Vibr2016; 385: 16–32.
