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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
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)
Xu X, Wang H. Large Rotating Machinery Running Trend Forecasting. Beijing: Science Press, 2011 (in Chinese)
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)
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
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
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)
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
Yu Z, Sun Y, Jin W. A novel generalized demodulation approach for multi-component signals. Signal Processing, 2016, 118: 188–202
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)
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
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)
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
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
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)
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
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)
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
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
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)
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)
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
Acknowledgements
This research was sponsored by the National Natural Science Foundation of China (Grant Nos. 51275052 and 51105041), and the Key Project Supported by Beijing Natural Science Foundation (Grant No. 3131002).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Xu, X., Liu, X. Weak characteristic information extraction from early fault of wind turbine generator gearbox. Front. Mech. Eng. 12, 357–366 (2017). https://doi.org/10.1007/s11465-017-0423-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11465-017-0423-4