access icon free Fault diagnosis method for wind turbine rolling bearings based on Hankel tensor decomposition

In order to diagnose the wind turbine rolling bearing faults with vibration signals effectively, a fault diagnosis method based on Hankel tensor decomposition is proposed. Firstly, IMF-SVD (intrinsic mode function, IMF; singular value decomposition, SVD) is used to estimate the number of sources in sensor observation signals. Secondary, a third-order Hankel tensor is formed by the observation matrix, and a set of low-rank tensor subterms are obtained by tensor rank- decomposition. The fault features of each source are contained in the first and second modes of the corresponding subterm. Then, the source signals are reconstructed by the subterms. Finally, the envelope spectra of the reconstructed source signals are analysed, and the fault characteristic frequencies are extracted. The results of simulation and practical case analysis show that this method can realise the fault diagnosis of wind turbine rolling bearings correctly and effectively.

Inspec keywords: mechanical engineering computing; rolling bearings; singular value decomposition; fault diagnosis; wind turbines; vibrations; tensors; signal reconstruction

Other keywords: envelope spectra; Hankel tensor decomposition; low-rank tensor subterms; vibration signals; tensor rank; wind turbine rolling bearings; sensor observation signals; reconstructed source signals; fault characteristic frequencies; fault diagnosis method; singular value decomposition; intrinsic mode function

Subjects: Mechanical engineering applications of IT; Vibrations and shock waves (mechanical engineering); Algebra; Mechanical components; Numerical analysis; Algebra; Digital signal processing; Civil and mechanical engineering computing; Maintenance and reliability

References

    1. 1)
      • 19. Tan, V., Tran, D., Ma, W.: ‘Tensor decomposition and application in image classification with histogram of oriented gradients’, Neurocomputing, 2015, 165, pp. 3845.
    2. 2)
      • 2. Malik, H., Mishra, S.: ‘Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and Simulink’, IET Renew. Power Gener., 2017, 11, (6), pp. 889902.
    3. 3)
      • 17. Cichocki, A., Mandic, D., Lathauwer, L.D., et al: ‘Tensor decompositions for signal processing applications: from two-way to multiway component analysis’, IEEE Signal Process. Mag., 2015, 32, (2), pp. 145163.
    4. 4)
      • 5. Hao, R.J., Lu, W.X., Chu, F.L.: ‘Mathematical morphology extracting method on roller bearing fault signals’, Proc. CSEE, 2008, 28, (26), pp. 6570.
    5. 5)
      • 18. Cong, F., Lin, Q.H., Kuang, L.D., et al: ‘Tensor decomposition of EEG signals: a brief review’, J. Neurosci. Methods, 2015, 248, pp. 5969.
    6. 6)
      • 3. Amirat, Y., Benbouzid, M.E.H., Al-Ahmar, E., et al: ‘A brief status on condition monitoring and fault diagnosis in wind energy conversion systems’, Renew. Sustain. Energy Rev., 2009, 13, (9), pp. 26292636.
    7. 7)
      • 8. Zhu, W.L., Zhou, J.Z., Xiao, J., et al: ‘An ICA-EMD feature extraction method and its application to vibration signals of hydroelectric generating units’, Proc. CSEE, 2013, 33, (29), pp. 95101.
    8. 8)
      • 13. Zhao, H.S., Li, L., Wang, Y.: ‘Fault feature extraction method of wind turbine bearing based on blind source separation and manifold learning’, Acta Energ. Sol. Sin., 2016, 37, (2), pp. 269275.
    9. 9)
      • 9. Zhao, H.S., Li, L.: ‘Fault diagnosis of wind turbine bearing based on variational mode decomposition and Teager energy operator’, IET Renew. Power Gener., 2017, 11, (4), pp. 453460.
    10. 10)
      • 6. Luo, Z.H., Xue, X.N., Wang, X.Z., et al: ‘Study on the method of incipient motor bearing fault diagnosis based on wavelet transform and EMD’, Proc. CSEE, 2005, 25, (14), pp. 125129.
    11. 11)
      • 23. Vandevoorde, D.: ‘A fast exponential decomposition algorithm and its applications to structured matrices’ (Rensselaer Polytechnic Institute, Troy, 1996).
    12. 12)
      • 16. Kolda, T.G., Bader, B.W.: ‘Tensor decompositions and applications’, SIAM Rev., 2009, 51, (3), pp. 455500.
    13. 13)
      • 1. Hu, C.Z., Yang, Q., Huang, M.Y., et al: ‘Diagnosis of non-linear mixed multiple faults based on underdetermined blind source separation for wind turbine gearbox: simulation, testbed and realistic scenarios’, IET Renew. Power Gener., 2017, 11, (11), pp. 14181429.
    14. 14)
      • 12. Hu, C.Z., Yang, Q., Huang, M.Y., et al: ‘Sparse component analysis-based under-determined blind source separation for bearing fault feature extraction in wind turbine gearbox’, IET Renew. Power Gener., 2017, 11, (3), pp. 330337.
    15. 15)
      • 10. Servière, C., Fabry, P.: ‘Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic’, Mech. Syst. Signal Process., 2005, 19, (6), pp. 12931311.
    16. 16)
      • 14. Jolliffe, I.: ‘Principal component analysis’, vol. 87 (Springer-Verlag, Berlin, 2005), (100), pp. 4164.
    17. 17)
      • 20. Dong, C., Xu, N., Batselier, K., et al: ‘Tensor singular value decompositions based on TTr1SVD and their applications to face recognition problem. Appl. Res. Comput., 2018, 35, (1), pp. 18.
    18. 18)
      • 7. Van, M., Kang, H.J., Shin, K.S.: ‘Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition’, IET Sci. Meas. Technol., 2014, 8, (6), pp. 571578.
    19. 19)
      • 21. Lathauwer, L.D.: ‘Decompositions of a higher-order tensor in block terms–PART II, definitions and uniqueness’, SIAM J. Matrix Anal. Appl., 2011, 30, (3), pp. 10331066.
    20. 20)
      • 25. Randall, R.B., Antoni, J.: ‘Rolling element bearing diagnostics – a tutorial ⋆’, Mech. Syst. Signal Process., 2011, 25, (2), pp. 485520.
    21. 21)
      • 22. Boussé, M., Debals, O., Lathauwer, L.D.: ‘A tensor-based method for large-scale blind source separation using segmentation’, IEEE Trans. Signal Process., 2016, 65, (2), pp. 346358.
    22. 22)
      • 24. Randall, R.B., Antoni, J., Chobsaard, S.: ‘The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals’, Mech. Syst. Signal Process., 2001, 15, (5), pp. 945962.
    23. 23)
      • 4. Liu, W.Y., Tang, B.P., Han, J.G., et al: ‘The structure healthy condition monitoring and fault diagnosis methods in wind turbines: a review’, Renew. Sustain. Energy Rev., 2015, 44, pp. 466472.
    24. 24)
      • 15. Comon, P., Jutten, C.: ‘Handbook of blind source separation: independent component analysis and separation’ (Academic Press, UK, 2010), pp. 714.
    25. 25)
      • 11. Qian, S.X., Jiao, W.D., Yang, S.X.: ‘Method of independent component analysis and its application to fault diagnosis’, Proc. CSEE, 2006, 26, (5), pp. 137142.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2018.5284
Loading

Related content

content/journals/10.1049/iet-rpg.2018.5284
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading