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Matching Pursuit Algorithm in Assessing the State of Rolling Bearings

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper the results of Matching Pursuit (MP) Octave algorithm applied to noise, vibration and harness (NVH) diagnosis of rolling bearings are presented. For this purpose two bearings in different condition state were examined. The object of the analysis was to calculate and present which energy error values of MP algorithm give the most accuracy results for different changes in bearing structures and also how energy values spread in time-frequency domain for chosen energy error value.
Słowa kluczowe
Rocznik
Strony
61--71
Opis fizyczny
Bibliogr. 18 poz. fig., tab.
Twórcy
autor
  • Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin
autor
  • Medical University of Lublin, Głuska 2, 20-439 Lublin
Bibliografia
  • [1] Cempel, C. (1989). Wibroakustyka Stosowana. Warszawa PWN.
  • [2] Chandra, H.N., & Sekhar A.S. (2016). Fault detection in rotor bearing systems using time frequency techniques. Mechanical Systems and Signal Processing, 72–73, 105–133. doi: dx.doi.org/10.1016/j.ymssp.2015.11.013
  • [3] Chandran, S., Mishra, A., & Shirhatti, V., Ray, S. (2016). Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time-Frequency Power Spectrum of Brain Signals. The Journal of Neuroscience, 36, 3399–3408. doi: 10.1523/JNEUROSCI.3633-15.2016
  • [4] Cui, L., Wu, N., Ma, C., & Wang, H. (2016). Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary. Mechanical Systems and Signal Processing, 68-69, 34–43. doi: dx.doi.org/10.1016/j.ymssp.2015.05.032
  • [5] Cui, L., Gong, X., & Zhang, J., Wang, H. (2016). Double-dictionary matching pursuit for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity. Journal of Sound and Vibration, 385, 372–388. doi: dx.doi.org/10.1016/j.jsv.2016.09.008
  • [6] Cui, L., Wang, J., & Lee, S., (2014). Matching pursuit of and adaptive impulse dictionary for bearing fault diagnosis. Journal of Sound and Vibration, 333, 2840–2862. doi: dx.doi.org/10.1016/j.jsv.2013.12.029
  • [7] Durka, P.J., Ircha, M., & Blinowska, K.J, (2001). Stochastic Time-Frequency Dictionaries for Matching Pursuit. IEEE Transactions of Signal Processing, 49, 507–510. doi: 10.1109/78.905866
  • [8] Gao, R.X., & Yan, R., (2011). Wavelets: Theory and Applications for Manufacturing. Springer. doi: DOI 10.1007/978-1-4419-1545-0_2
  • [9] He, G., Ding, K., & Lin, H., (2016). Fault feature of rolling element bearings using sparse representation. Journal of Sound and Vibration. 366, 514–527. doi: dx.doi.org/10.1016/j.jsv.2015.12.020
  • [10] Kuś, R., Różański, P.T., & Durka, P.J., (2013). Multivariate matching pursuit in optimal Gabor dictionaries: theroy and software with interface for EEG/MEG via Svarog. Biomedical Engineering Online, 12. doi: doi:10.1186/1475-925X-12-94
  • [11] Liu, B., Ling, S.F., & Gribonoval, R., (2002). Bearing failure detection using matching pursuit. NDT&E International, 35, 255–262.
  • [12] Mallat, S., & Zhang, Z., (1993). Matching pursuit with time-frequency dictionaries. IEEE Trans On Signal Processing, 41, 3397–3415. doi: 10.1109/78.258082
  • [13] Nguyen-Schafer, H., (2016). Computational Design of Rolling Bearings. Springer. doi: 10.1007/978-3-319-27131-6
  • [14] Tang, H.F., Chen, J., & Dong, G.M., (2012). Signal complexity analysis for fault diagnosis of rolling element bearing based on matching pursuit. Journal of Vibration and Control, 18, 671–683. doi: doi.org/10.1177/1077546311405369
  • [15] Yan, R., Gao, R.X., & Chen, X., (2014). Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 96, 1–15. doi: dx.doi.org/10.1016/j.sigpro.2013.04.015
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-967a6f6d-8f3f-4dc3-9748-db7ba94583b8
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