It is a challenging task to accurately diagnose a railway bearing fault since bearing vibration signals are under strong interferences from wheel–rail excitations. The commonly used Kurtogram-based methods are often trapped in components induced by the wheel–rail excitations while adaptive mode decomposition methods are sensitive to input control parameters. To address these issues, based on a recently developed powerful signal decomposition method, that is, adaptive chirp mode decomposition (ACMD), a novel method called bandwidth-aware ACMD (BA-ACMD) is proposed in this article. First, the filter bank property of ACMD is thoroughly analyzed based on Monte-Carlo simulation and then a bandwidth expression with respect to the penalty parameter is first obtained by fitting a power law model. Then, a weighted spectrum trend (WST) method is proposed to partition frequency bands and then guide the parameter determination of ACMD through the integration of the obtained bandwidth expression. In addition, according to the order of magnitude of the WST in each band, the BA-ACMD adopts a recursive framework to extract signal modes one by one. In this way, dominating signal modes related to wheel–rail excitations can be extracted and then subtracted from the vibration signal in advance so that the bearing faults induced signal modes can be successfully identified. Both simulation and experimental validations are conducted showing that BA-ACMD can effectively detect single and compound faults of railway bearings under strong wheel–rail excitations.
LiuYChenZZhaiW, et al. Dynamic investigation of traction motor bearing in a locomotive under excitation from track random geometry irregularity. Int J Rail Transp2020; 10: 72–94.
2.
LiuYChenZLiW, et al. Dynamic analysis of traction motor in a locomotive considering surface waviness on races of a motor bearing. Rail Eng Science2021; 29: 379–393.
3.
EntezamiMRobertsCWestonP, et al. Perspectives on railway axle bearing condition monitoring. Proceed Institut Mechan Eng Part F: J Rail Rapid Transit2020; 234: 17–31.
4.
TarawnehCMontalvoJWilsonB. Defect detection in freight railcar tapered-roller bearings using vibration techniques. Rail Eng Science2021; 29: 42–58.
5.
YuXLiZHeQ, et al. Gearbox fault diagnosis based on bearing dynamic force identification. J Sound Vib2021; 511: 116360.
6.
HouDQiHWangC, et al. High-speed train wheel set bearing fault diagnosis and prognostics: evaluation of signal processing methods under multi-source interference. Struct Health Monit. Epub ahead of print 12September2022. DOI: 10.1177/14759217221122308.
7.
YuKLinTRTanJ. A bearing fault and severity diagnostic technique using adaptive deep belief networks and Dempster–Shafer theory. Struct Health Monit2020; 19: 240–261.
8.
LiYZuoMJChenZ, et al. Railway bearing and cardan shaft fault diagnosis via an improved morphological filter. Struct Health Monit2020; 19: 1471–1486.
9.
ChiZLinJChenR, et al. Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China’s HSR train. Measurement2020; 149: 107022.
10.
YangSGuXLiuY, et al. A general multi-objective optimized wavelet filter and its applications in fault diagnosis of wheelset bearings. Mech Syst Signal Process2020; 145: 106914.
11.
ChengYWangSChenB, et al. An improved envelope spectrum via candidate fault frequency optimization-gram for bearing fault diagnosis. J Sound Vib2022; 523: 116746.
12.
AntoniJ. Fast computation of the kurtogram for the detection of transient faults. Mech Syst Signal Process2007; 21: 108–124.
13.
MiaoYZhaoMLinJ. Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification. Meas Sci Technol2017; 28: 125001.
14.
AntoniJ. The infogram: entropic evidence of the signature of repetitive transients. Mech Syst Signal Process2016; 74: 73–94.
15.
WangDZhongJLiC, et al. Box-Cox sparse measures: a new family of sparse measures constructed from kurtosis and negative entropy. Mech Syst Signal Process2021; 160: 107930.
16.
LópezCWangDNaranjoÁ, et al. Box-Cox-sparse-measures-based blind filtering: understanding the difference between the maximum kurtosis deconvolution and the minimum entropy deconvolution. Mech Syst Signal Process2022; 165: 108376.
ChengYChenBZhangW. Adaptive multipoint optimal minimum entropy deconvolution adjusted and application to fault diagnosis of rolling element bearings. IEEE Sens J2019; 19: 12153–12164.
19.
LiYLiangXLinJ, et al. Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter. Mech Syst Signal Process2018; 101: 435–448.
20.
PangBNazariMSunZ, et al. An optimized variational mode extraction method for rolling bearing fault diagnosis. Struct Health Monit2022; 21: 558–570.
21.
JiangXWangJShenC, et al. An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis. Struct Health Monit. Epub ahead of print 25November2020. DOI: 10.1177/1475921720970856.
22.
DibajAEttefaghMMHassannejadR, et al. Fine-tuned variational mode decomposition for fault diagnosis of rotary machinery. Struct Health Monit2020; 19: 1453–1470.
23.
ChengYWangZChenB, et al. An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis. ISA Trans2019; 91: 218–234.
24.
AbdelkaderRKaddourADerouicheZ. Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method. Int J Adv Manuf Technol2018; 97: 3099–3117.
25.
ZhengJChengJYangY. Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis. Mech Syst Signal Process2013; 40: 136–153.
26.
LazhariMSadhuA. Decentralized modal identification of structures using an adaptive empirical mode decomposition method. J Sound Vib2019; 447: 20–41.
27.
GillesJ. Empirical wavelet transform. IEEE Trans Signal Process2013; 61: 3999–4010.
28.
CaoHFanFZhouK, et al. Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement2016; 82: 439–449.
29.
HuYTuXLiF, et al. An adaptive and tacholess order analysis method based on enhanced empirical wavelet transform for fault detection of bearings with varying speeds. J Sound Vib2017; 409: 241–255.
30.
WangDTsuiK-LQinY. Optimization of segmentation fragments in empirical wavelet transform and its applications to extracting industrial bearing fault features. Measurement2019; 133: 328–340.
31.
XuYTianWZhangK, et al. Application of an enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis. Meas Sci Technol2019; 30: 035001.
32.
DragomiretskiyKZossoD. Variational mode decomposition. IEEE Trans Signal Process2013; 62: 531–544.
33.
HuangYLinJLiuZ, et al. A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis. J Sound Vib2019; 444: 216–234.
34.
JiangXShenCShiJ, et al. Initial center frequency-guided VMD for fault diagnosis of rotating machines. J Sound Vib2018; 435: 36–55.
35.
JiangXWangJShiJ, et al. A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines. Mech Syst Signal Process2019; 116: 668–692.
36.
MiaoYZhaoMLinJ. Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition. ISA Trans2019; 84: 82–95.
37.
GaiJShenJHuY, et al. An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing. Measurement2020; 162: 107901.
38.
WeiDJiangHShaoH, et al. An optimal variational mode decomposition for rolling bearing fault feature extraction. Meas Sci Technol2019; 30: 055004.
39.
GuJPengYLuH, et al. An optimized variational mode decomposition method and its application in vibration signal analysis of bearings. Struct Health Monit2022; 21: 2386–2407.
YuKMaHHanH, et al. Second order multi-synchrosqueezing transform for rub-impact detection of rotor systems. Mech Mach Theory2019; 140: 321–349.
42.
YuKChuFWangX, et al. Frequency-domain energy-concentrated synchrosqueezing transform for frequency-varying signal with linear group delay. IEEE Trans Instrum Meas2022; 71: 1–15.
43.
YuKLinTRMaH, et al. A combined polynomial chirplet transform and synchroextracting technique for analyzing nonstationary signals of rotating machinery. IEEE Trans Instrum Meas2019; 69: 1505–1518.
44.
ZhouPDuMChenS, et al. Study on intra-wave frequency modulation phenomenon in detection of rub-impact fault. Mech Syst Signal Process2019; 122: 342–363.
45.
ZhouPYangYWangH, et al. The relationship between fault-induced impulses and harmonic-cluster with applications to rotating machinery fault diagnosis. Mech Syst Signal Process2020; 144: 106896.
46.
ChenSDongXPengZ, et al. Nonlinear chirp mode decomposition: A variational method. IEEE Trans Signal Process2017; 65: 6024–6037.
47.
ChenSYangYPengZ, et al. Adaptive chirp mode pursuit: algorithm and applications. Mech Syst Signal Process2019; 116: 566–584.
48.
ChenSWangKChangC, et al. A two-level adaptive chirp mode decomposition method for the railway wheel flat detection under variable-speed conditions. J Sound Vib2021; 498: 115963.
49.
ChenSYangYPengZ, et al. Detection of rub-impact fault for rotor-stator systems: a novel method based on adaptive chirp mode decomposition. J Sound Vib2019; 440: 83–99.
50.
WangXLuSZhangS. Rotating angle estimation for hybrid stepper motors with application to bearing fault diagnosis. IEEE Trans Instrum Meas2020; 69: 5556–5568.
51.
Ur RehmanNMandicDP. Filter bank property of multivariate empirical mode decomposition. IEEE Trans Signal Process2011; 59: 2421–2426.
52.
WangYMarkertR. Filter bank property of variational mode decomposition and its applications. Signal Process2016; 120: 509–521.
53.
ChenQLangXXieL, et al. Multivariate intrinsic chirp mode decomposition. Signal Process2021; 183: 108009.
54.
WangXHeYWangH, et al. A novel hybrid approach for damage identification of wind turbine bearing under variable speed condition. Mech Mach Theory2022; 169: 104629.
55.
ZhangKXuYLiaoZ, et al. A novel Fast Entrogram and its applications in rolling bearing fault diagnosis. Mech Syst Signal Process2021; 154: 107582.
56.
AntoniJXinGHamzaouiN. Fast computation of the spectral correlation. Mech Syst Signal Process2017; 92: 248–277.
57.
ChenSPengZYangY, et al. Intrinsic chirp component decomposition by using Fourier series representation. Signal Process2017; 137: 319–327.
58.
RandallRBAntoniJ. Rolling element bearing diagnostics—A tutorial. Mech Syst Signal Process2011; 25: 485–520.
