Weak characteristic information extraction from early fault of wind turbine generator gearbox

doi:10.1007/s11465-017-0423-4

Front. Mech. Eng.

2017, Vol. 12

Issue (3) : 357-366 https://doi.org/10.1007/s11465-017-0423-4

RESEARCH ARTICLE

Weak characteristic information extraction from early fault of wind turbine generator gearbox

Xiaoli XU, Xiuli LIU(

)

Key Laboratory of Modern Measurement & Control Technology (Ministry of Education), Beijing Information Science and Technology University, Beijing 100192, China

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Abstract

Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition (SVD)-based denoising may result in loss of useful information. A weak characteristic information extraction based on µ-SVD and local mean decomposition (LMD) is developed to address this problem. The basic principle of the method is as follows: Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and µ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.

Keywords wind turbine generator gearbox µ-singular value decomposition local mean decomposition weak characteristic information extraction early fault warning

Corresponding Author(s): Xiuli LIU

Just Accepted Date: 16 March 2017 Online First Date: 06 April 2017 Issue Date: 04 August 2017

Cite this article:

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.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-017-0423-4
https://academic.hep.com.cn/fme/EN/Y2017/V12/I3/357

Fig.1 LMD algorithm flow chart

Fig.2 Pretreatment method flow chart

Fig.3 Time-domain waveform and frequency spectrum of source signal

Fig.4 Time-domain waveform and frequency spectrum of signal with noise

Fig.5 Relation between denoising order and cumulative contribution rate (CCR)

Fig.6 Waveform of the second part of signal

Fig.7 LMD decomposition result of the second part of signals

Fig.8 Correlation of three PF components with the second part of signals

Fig.9 Determination of delay time

Fig.10 Detemination of embedding dimension

Fig.11 Denoising order of phase space reconstruction

Fig.12 Signal after extraction of weak information with PF1

Tab.1 SNR and RMSE after treatment using different methods

Fig.13 Signal after extraction of weak information

Fig.14 Installation locations of sensors

Tab.2 Deep groove ball bearing specification information

Fig.15 Time-domain waveform of three kinds of faults. (a) Ball failure; (b) inner ring fault; (c) outer ring fault

Fig.16 Three kinds of fault signal spectrum. (a) Rolling body fault; (b) inner ring fault; (c) outer ring fault

Fig.17 Second part of the original signal and the PF component. (a) The second part of the original signal; (b) PF1; (c) PF2; (d) PF3; (e) PF4

Fig.18 Weak information of signal

Fig.19 Weak information spectrum

1	Wang G, He Z, Chen X, et al. Basic research on machinery fault diagnosis—What is the prescription. Journal of Mechanical Engineering, 2013, 49(1): 63–72 (in Chinese)
2	Xu X, Wang H. Large Rotating Machinery Running Trend Forecasting. Beijing: Science Press, 2011 (in Chinese)
3	Xu X, Jiang Z, Ren B, et al. Extract method of flue gas generator set state feature weak information based on Birgé-Massart threshold. Journal of Mechanical Engineering, 2012, 48(12): 7–12 (in Chinese)
4	Man Z, Wang W, Khoo S, et al. Optimal sinusoidal modeling of gear mesh vibration signals for gear diagnosis and prognosis. Mechanical Systems and Signal Processing, 2012, 33: 256–274 https://doi.org/10.1016/j.ymssp.2012.07.004
5	Lv Z, Zhang W, Xu J. A denoising method based singular spectrum and its application in machine fault diagnosis. Chinese Journal of Mechanical Engineering, 1999, 35(3): 85–88
6	Chen J, Zhang L, Duan L, et al. Diagnosis of liquid valve based on undecimated lifting scheme packet and singular value decomposition. Journal of Mechanical Engineering, 2011, 47(9): 72–77 (in Chinese) https://doi.org/10.3901/JME.2011.09.072
7	Liu Y, Zhang J, Lin J, et al. Application of improved LMD, SVD technique and RVM to fault diagnosis of diesel valve trains. Transactions of Tianjin University, 2015, 21(4): 304–311 https://doi.org/10.1007/s12209-015-2430-z
8	Yu Z, Sun Y, Jin W. A novel generalized demodulation approach for multi-component signals. Signal Processing, 2016, 118: 188–202 https://doi.org/10.1016/j.sigpro.2015.07.001
9	Zhao X, Ye B, Chen T. Difference spectrum theory of singular value and its application to the fault diagnosis of headstock of lathe. Journal of Mechanical Engineering, 2010, 46(1): 100–108 (in Chinese) https://doi.org/10.3901/JME.2010.01.100
10	Zhong Z, Zhang B, Durrani T S, et al. Nonlinear signal processing for vocal folds damage detection based on heterogeneous sensor network. Signal Processing, 2016, 126(S1): 125–133 https://doi.org/10.1016/j.sigpro.2015.08.019
11	Zeng M, Yang Y, Zheng J, et al.μ-SVD based denoising method and its application to gear fault diagnosis. Journal of Mechanical Engineering, 2015, 51(3): 95–103 (in Chinese) https://doi.org/10.3901/JME.2015.03.095
12	Zhu S, Qiao Z, Yang Z. An improved method for the extraction of weak signal based on SVD and EMD. Measurement & Control Technology, 2014, 33(1): 60–62
13	Jiang W, Zheng Z, Zhu Y, et al. Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis. Mechanical Systems and Signal Processing, 2015, 58–59: 179–205 https://doi.org/10.1016/j.ymssp.2014.10.017
14	Sun W, Xiong B, Huang J, et al. Fault diagnosis of a rolling bearing using wavelet packet de-noising and LMD. Journal of Vibration and Shock, 2012, 31(18): 153–156 (in Chinese)
15	Morzfeld M, Ajavakom N, Ma F. Diagonal dominance of damping and the decoupling approximation in linear vibratory systems. Journal of Sound and Vibration, 2009, 320(1–2): 406–420 https://doi.org/10.1016/j.jsv.2008.07.025
16	Wu Z, Cheng J, Yu Y,et al. Adaptive characteristic-scale decomposition method and its application. China Mechanical Engineering, 2015, 42(23): 7–15 (in Chinese) https://doi.org/10.3901/JME.2015.18.007
17	Wang B, Ren Z, Wen B. Fault diagnoses method of rotating machines based on nonlinear. Chinese Journal of Mechanical Engineering, 2012, 48(5): 63–69 https://doi.org/10.3901/JME.2012.05.063
18	Cao L. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 1997, 110(1–2): 43–50 https://doi.org/10.1016/S0167-2789(97)00118-8
19	Miao L, Ren W, Hu Y,et al. Separating temperature effect from dynamic strain measurements of a bridge based on analytical mode decomposition method. China Mechanical Engineering, 2012, 31(21): 6–10 (in Chinese)
20	Wang H, Li X, Wang G, et al. Research on failure of wind turbine gearbox and recent development of its design and manufacturing technologies. China Mechanical Engineering, 2013, 24(11): 1542–1549 (in Chinese)
21	Vanhollebeke F, Peeters P, Helsen J, et al. Large scale validation of a flexible multibody wind turbine gearbox model. Journal of Computational and Nonlinear Dynamics, 2015, 10(4): 041006 https://doi.org/10.1115/1.4028600

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