Tacholess order-tracking approach for wind turbine gearbox fault detection

doi:10.1007/s11465-017-0452-z

Front. Mech. Eng.

2017, Vol. 12

Issue (3) : 427-439 https://doi.org/10.1007/s11465-017-0452-z

RESEARCH ARTICLE

Tacholess order-tracking approach for wind turbine gearbox fault detection

Yi WANG^1,², Yong XIE², Guanghua XU^2,³(

), Sicong ZHANG², Chenggang HOU²

¹. School of Mechanical Engineering, Chongqing University, Chongqing 400044, China
². School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
³. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Download: PDF(539 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

Keywords wind turbine variable-speed operating conditions Vold-Kalman filtering tacholess order tracking

Corresponding Author(s): Guanghua XU

Just Accepted Date: 07 June 2017 Online First Date: 19 July 2017 Issue Date: 04 August 2017

Cite this article:

Yi WANG,Yong XIE,Guanghua XU, et al. Tacholess order-tracking approach for wind turbine gearbox fault detection[J]. Front. Mech. Eng., 2017, 12(3): 427-439.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-017-0452-z
https://academic.hep.com.cn/fme/EN/Y2017/V12/I3/427

Fig.1 Time-frequency representation obtained by STFT

Fig.2 Ridges detected by CFRD and DLMRD

Fig.3 Flowchart of the proposed tacholess order-tracking technique

Fig.4 Simulated signals with signal-to-noise ratio equal to −3 dB

Fig.5 Envelope spectrum of the original signal

Fig.6 (a) TFR of the simulated signals in the low-frequency range; (b) second harmonic IF estimation result

Fig.7 (a) Waveform of the extracted signal related to the second harmonic; (b) instantaneous phase calculated from the extracted mono-component signal

Fig.8 (a) Resampled envelope signal; (b) envelope order spectrum

Fig.9 Sketch map of the transmission chain of the investigated WT

Tab.1 Structural parameters of the transmission chain

Fig.10 Sensor locations on the WT (A, B, and C represent the main bearing, planetary gearbox, and generator, respectively)

Tab.2 Order corresponding to rotational and mesh frequencies

Fig.11 Schematic of the proposed technique

Fig.12 Original signal from Sensor 3, which was installed on WT 049#

Fig.13 TFRs of the vibration signal obtained from WT 049# by (a) Sensor 3, which was mounted on the planetary gearbox house, and (b) Sensor 8, which was installed close to the generator input shaft

Fig.14 First harmonic separated by VKF

Fig.15 Envelope order spectrum of the resampled signal of Sensor 3 in WT 049#

Fig.16 Original signal of Sensor 3 on 035# WT

Fig.17 Envelope order spectrum of the vibration signal from Sensor 3 on WT 35#

42	JablounM, MartinN, LeonardF, et al.Estimation of the instantaneous amplitude and frequency of non-stationary short-time signals. Signal Processing, 2008, 88(7): 1636–1655 https://doi.org/10.1016/j.sigpro.2007.12.014
1	McFaddenP D, SmithJ D. Vibration monitoring of rolling element bearings by the high-frequency resonance technique—A review. Tribology International, 1984, 17(1): 3–10 https://doi.org/10.1016/0301-679X(84)90076-8
43	Chandra SekharS, SreenivasT V. Effect of interpolation on PWVD computation and instantaneous frequency estimation. Signal Processing, 2004, 84(1): 107–116 https://doi.org/10.1016/j.sigpro.2003.07.015
2	RubiniR, MeneghettiU. Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing, 2001, 15(2): 287–302 https://doi.org/10.1006/mssp.2000.1330
3	MakowskiR A, ZimrozR. Adaptive Bearings Vibration Modelling for Diagnosis. Berlin: Springer, 2011
44	ShuiP, BaoZ, SuH. Nonparametric detection of FM signals using time-frequency ridge energy. IEEE Transactions on Signal Processing, 2008, 56(5): 1749–1760 https://doi.org/10.1109/TSP.2007.909322
45	CombetF, ZimrozR. A new method for the estimation of the instantaneous speed relative fluctuation in a vibration signal based on the short time scale transform. Mechanical Systems and Signal Processing, 2009, 23(4): 1382–1397 https://doi.org/10.1016/j.ymssp.2008.07.001
4	WangY, XuG, LiangL, et al.Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing, 2015, 54–55: 259–276 https://doi.org/10.1016/j.ymssp.2014.09.002
5	WangD, TseP W, TsuiK L. An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 2013, 35(1–2): 176–199 https://doi.org/10.1016/j.ymssp.2012.10.003
46	PengZ, MengG, ChuF, et al.Polynomial chirplet transform with application to instantaneous frequency estimation. IEEE Transactions on Instrumentation and Measurement, 2011, 60(9): 3222–3229 https://doi.org/10.1109/TIM.2011.2124770
47	GrylliasK C, AntoniadisI A. Estimation of the instantaneous rotation speed using complex shifted Morlet wavelets. Mechanical Systems and Signal Processing, 2013, 38(1): 78–95 https://doi.org/10.1016/j.ymssp.2012.06.026
48	UrbanekJ, BarszczT, AntoniJ. A two-step procedure for estimation of instantaneous rotational speed with large fluctuations. Mechanical Systems and Signal Processing, 2013, 38(1): 96–102 https://doi.org/10.1016/j.ymssp.2012.05.009
49	RankineL, MesbahM, BoashashB. IF estimation for multicomponent signals using image processing techniques in the time-frequency domain. Signal Processing, 2007, 87(6): 1234–1250 https://doi.org/10.1016/j.sigpro.2006.10.013
50	YangY, PengZ, MengG, et al.Characterize highly oscillating frequency modulation using generalized Warblet transform. Mechanical Systems and Signal Processing, 2012, 26: 128–140 https://doi.org/10.1016/j.ymssp.2011.06.020
51	YangY, DongX, PengZ, et al.Vibration signal analysis using parameterized time-frequency method for features extraction of varying-speed rotary machinery. Journal of Sound and Vibration, 2015, 335: 350–366 https://doi.org/10.1016/j.jsv.2014.09.025
52	LiuH, CartwrightA N, BasaranC. Moiré interferogram phase extraction: A ridge detection algorithm for continuous wavelet transforms. Applied Optics, 2004, 43(4): 850–857 https://doi.org/10.1364/AO.43.000850
53	FengZ, ChuF, ZuoM. Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation. Journal of Sound and Vibration, 2011, 330(6): 1225–1243 https://doi.org/10.1016/j.jsv.2010.09.030
54	HyersR W, McGowanJ G, SullivanK L, et al.Condition monitoring and prognosis of utility scale wind turbines. Energy Materials, 2006, 1(3): 187–203 https://doi.org/10.1179/174892406X163397
55	PengZ, ChuF. Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mechanical Systems and Signal Processing,2004, 18(2): 199–221 https://doi.org/10.1016/S0888-3270(03)00075-X
56	VoldH, LeuridanJ. High resolution order tracking at extreme slew rates, using Kalman tracking filters. Shock and Vibration, 1995, 2(6): 507–515 https://doi.org/10.3233/SAV-1995-2609
57	QinS. Feng Z, Liang M. Application of Vold-Kalman and higher order energy separation to fault diagnosis of planetary gearbox under time-varying conditions. Journal of Vibration Engineering, 2015, 28(5): 841–845 (in Chinese)
58	PanM C, LinY F. Further exploration of Vold-Kalman filtering order tracking with shaft-speed information—I: Theoretical part, numerical implementation and parameter investigations. Mechani-cal Systems and Signal Processing, 2006, 20(5): 1134–1154 https://doi.org/10.1016/j.ymssp.2005.01.005
59	SharmaV, PareyA. A review of gear fault diagnosis using various condition indicators.Procedia Engineering, 2016, 144: 253–263 https://doi.org/10.1016/j.proeng.2016.05.131
60	ChengJ, YangY, YuD. The envelope order spectrum based on generalized demodulation time-frequency analysis and its application to gear fault diagnosis.Mechanical Systems and Signal Processing, 2010, 24(2): 508–521 https://doi.org/10.1016/j.ymssp.2009.07.003
61	MaJ, LiC. Gear defect detection through model-based wideband demodulation of vibrations. Mechanical Systems and Signal Processing, 1996, 10(5): 653–665 https://doi.org/10.1006/mssp.1996.0044
62	RandallR B, AntoniJ, ChobsaardS. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mechani-cal Systems and Signal Processing, 2001, 15(5): 945–962 https://doi.org/10.1006/mssp.2001.1415
63	FengZ, ChenX, LiangM. Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox. Mechanical Systems and Signal Processing, 76– 77: 242–264
6	SawalhiN, RandallR B, EndoH. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing, 2007, 21(6): 2616–2633 https://doi.org/10.1016/j.ymssp.2006.12.002
7	BarszczT, JabŁońskiA. A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mechanical Systems and Signal Processing, 2011, 25(1): 431–451 https://doi.org/10.1016/j.ymssp.2010.05.018
8	UrbanekJ, AntoniJ, BarszczT. Detection of signal component modulations using modulation intensity distribution. Mechanical Systems and Signal Processing, 2012, 28: 399–413 https://doi.org/10.1016/j.ymssp.2011.12.018
9	SamuelP D, PinesD J. A review of vibration-based techniques for helicopter transmission diagnostics. Journal of Sound and Vibration, 2005, 282(1–2): 475–508 https://doi.org/10.1016/j.jsv.2004.02.058
10	CombetF, GelmanL. Optimal filtering of gear signals for early damage detection based on the spectral kurtosis. Mechanical Systems and Signal Processing, 2009, 23(3): 652–668 https://doi.org/10.1016/j.ymssp.2008.08.002
11	BrieD, TomczakM, OehlmannH, et al.Gear crack detection by adaptive amplitude and phase demodulation. Mechanical Systems and Signal Processing, 1997, 11(1): 149–167 https://doi.org/10.1006/mssp.1996.0068
12	ZimrozR, BartkowiakA. Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions. Mechanical Systems and Signal Processing, 2013, 38(1): 237–247 https://doi.org/10.1016/j.ymssp.2012.03.022
13	ZimrozR, BartelmusW. Gearbox condition estimation using cyclo-stationary properties of vibration signal. Key Engineering Materials, 2009,413–414: 471–478
14	ZimrozR, BartkowiakA. Investigation on spectral structure of gearbox vibration signals by principal component analysis for condition monitoring purposes.Journal of Physics: Conference Series,2011, 305(1): 012075
15	LiuW, TangB, HanJ, et al.The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renewable and Sustainable Energy Reviews, 2015, 44: 466–472 https://doi.org/10.1016/j.rser.2014.12.005
16	TangB, LiuW, SongT. Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution. Renewable Energy, 2010, 35(12): 2862–2866 https://doi.org/10.1016/j.renene.2010.05.012
17	JiangY, TangB, QinY, et al.Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD. Renewable Energy, 2011, 36(8): 2146–2153 https://doi.org/10.1016/j.renene.2011.01.009
18	TangB, SongT, LiF, et al.Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine. Renewable Energy, 2014, 62: 1–9 https://doi.org/10.1016/j.renene.2013.06.025
19	YangW, TavnerP J, TianW. Wind turbine condition monitoring based on an improved spline-kernelled Chirplet transform. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6565–6574 https://doi.org/10.1109/TIE.2015.2458787
20	FengZ, LiangM, ZhangY, et al.Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation. Renewable Energy, 2012, 47: 112–126 https://doi.org/10.1016/j.renene.2012.04.019
21	XuY, ChenJ. Characterizing nonstationary wind speed using empirical mode decomposition. Journal of Structural Engineering, 2004, 130(6): 912–920 https://doi.org/10.1061/(ASCE)0733-9445(2004)130:6(912)
22	AnX, JiangD, LiS, et al.Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine. Energy, 2011, 36(9): 5508–5520 https://doi.org/10.1016/j.energy.2011.07.025
23	YangW, CourtR, TavnerP J, et al.Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring. Journal of Sound and Vibration, 2011, 330(15): 3766–3782 https://doi.org/10.1016/j.jsv.2011.02.027
24	LiuH, ChenC, TianH, et al.A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renewable Energy, 2012, 48: 545–556 https://doi.org/10.1016/j.renene.2012.06.012
25	FengZ, LiangM. Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time-frequency analysis. Renewable Energy, 2014, 66: 468–477 https://doi.org/10.1016/j.renene.2013.12.047
26	RandallR B, AntoniJ. Rolling element bearing diagnostics—A tutorial. Mechanical Systems and Signal Processing, 2011, 25(2): 485–520 https://doi.org/10.1016/j.ymssp.2010.07.017
27	BorghesaniP, RicciR, ChattertonS, et al.A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions. Mechanical Systems and Signal Processing, 2013, 38(1): 23–35 https://doi.org/10.1016/j.ymssp.2012.09.014
28	McFaddenP D, ToozhyM M. Application of synchronous averaging to vibration monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 2000, 14(6): 891–906 https://doi.org/10.1006/mssp.2000.1290
29	FyfeK R, MunckE D S. Analysis of computed order tracking. Mechanical Systems and Signal Processing, 1997, 11(2): 187–205 https://doi.org/10.1006/mssp.1996.0056
30	WangT, LiangM, LiJ, et al.Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis. Mechanical Systems and Signal Processing, 2014, 45(1): 139–153 https://doi.org/10.1016/j.ymssp.2013.11.011
31	ZhaoM, LinJ, XuX, et al.Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds. Sensors (Basel), 2013, 13(8): 10856–10875 https://doi.org/10.3390/s130810856
32	WangY, XuG, ZhangQ, et al.Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions. Journal of Sound and Vibration, 2015, 348: 381–396 https://doi.org/10.1016/j.jsv.2015.03.018
33	WangY, XuG, LuoA, et al.An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection. Journal of Sound and Vibration, 2016, 367: 233–249 https://doi.org/10.1016/j.jsv.2015.12.041
34	BonnardotF, El BadaouiM, RandallR B, et al.Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation). Mechanical Systems and Signal Processing, 2005, 19(4): 766–785 https://doi.org/10.1016/j.ymssp.2004.05.001
35	ZhaoM, LinJ, WangX, et al.A tacho-less order tracking technique for large speed variations. Mechanical Systems and Signal Processing, 2013, 40(1): 76–90 https://doi.org/10.1016/j.ymssp.2013.03.024
36	BoashashB. Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals. Proceedings of the IEEE, 1992, 80(4): 520–538 https://doi.org/10.1109/5.135376
37	BoashashB. Estimating and interpreting the instantaneous frequency of a signal. II. Algorithms and applications. Proceedings of the IEEE, 1992, 80(4): 540–568 https://doi.org/10.1109/5.135378
38	RodopoulosK, YiakopoulosC, AntoniadisI. A parametric approach for the estimation of the instantaneous speed of rotating machinery. Mechanical Systems and Signal Processing, 2014, 44(1–2): 31–46 https://doi.org/10.1016/j.ymssp.2013.02.011
39	FengZ, ChenX, LiangM. Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions. Mechanical Systems and Signal Processing, 2015, 52–53: 360–375 https://doi.org/10.1016/j.ymssp.2014.07.009
40	CombetF, GelmanL. An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor. Mechanical Systems and Signal Processing, 2007, 21(6): 2590–2606 https://doi.org/10.1016/j.ymssp.2006.12.006
41	UrbanekJ, BarszczT, SawalhiN, et al.Comparison of amplitude-based and phase-based methods for speed tracking in application to wind turbines. Metrology and Measurement Systems, 2011, 18(2): 295–304 https://doi.org/10.2478/v10178-011-0011-z

[1]	Binrong WEN, Sha WEI, Kexiang WEI, Wenxian YANG, Zhike PENG, Fulei CHU. Power fluctuation and power loss of wind turbines due to wind shear and tower shadow[J]. Front. Mech. Eng., 2017, 12(3): 321-332.
[2]	Xiaoli XU, Xiuli LIU. Weak characteristic information extraction from early fault of wind turbine generator gearbox[J]. Front. Mech. Eng., 2017, 12(3): 357-366.
[3]	Zhaohui DU, Xuefeng CHEN, Han ZHANG, Yanyang ZI, Ruqiang YAN. Multiple fault separation and detection by joint subspace learning for the health assessment of wind turbine gearboxes[J]. Front. Mech. Eng., 2017, 12(3): 333-347.
[4]	Xueping PAN, Ping JU, Feng WU, Yuqing JIN. Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator[J]. Front. Mech. Eng., 2017, 12(3): 367-376.
[5]	Maolin CAI, Yixuan WANG, Zongxia JIAO, Yan SHI. Review of fluid and control technology of hydraulic wind turbines[J]. Front. Mech. Eng., 2017, 12(3): 312-320.
[6]	Hamed HABIBI, Hamed RAHIMI NOHOOJI, Ian HOWARD. Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control[J]. Front. Mech. Eng., 2017, 12(3): 377-388.
[7]	Shuaishuai WANG, Caichao ZHU, Chaosheng SONG, Huali HAN. Effects of elastic support on the dynamic behaviors of the wind turbine drive train[J]. Front. Mech. Eng., 2017, 12(3): 348-356.
[8]	Shoudao HUANG, Xuan WU, Xiao LIU, Jian GAO, Yunze HE. Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults[J]. Front. Mech. Eng., 2017, 12(3): 281-302.
[9]	Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU. Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum[J]. Front. Mech. Eng., 2017, 12(3): 406-419.
[10]	Lingli JIANG, Zhenyong DENG, Fengshou GU, Andrew D. BALL, Xuejun LI. Effect of friction coefficients on the dynamic response of gear systems[J]. Front. Mech. Eng., 2017, 12(3): 397-405.
[11]	Pengxing YI,Peng HUANG,Tielin SHI. Numerical analysis and experimental investigation of modal properties for the gearbox in wind turbine[J]. Front. Mech. Eng., 2016, 11(4): 388-402.
[12]	Sampath S. S.,Sawan SHETTY,Chithirai Pon Selvan M.. Estimation of power in low velocity vertical axis wind turbine[J]. Front. Mech. Eng., 2015, 10(2): 211-218.
[13]	Pengxing YI,Lijian DONG,Tielin SHI. Multi-objective genetic algorithms based structural optimization and experimental investigation of the planet carrier in wind turbine gearbox[J]. Front. Mech. Eng., 2014, 9(4): 354-367.
[14]	LIU Xiong, CHEN Yan, YE Zhiquan. Optimization model for rotor blades of horizontal axis wind turbines[J]. Front. Mech. Eng., 2007, 2(4): 483-488.

Viewed

Full text

Abstract

Cited

Shared

Discussed