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Tytuł artykułu

Experimental comparison of vibration and acoustic emission signal analysis using kurtosis-based methods for induction motor bearing condition monitoring

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Eksperymentalne porównanie drgań i analizy sygnałów emisji akustycznej do monitorowania stanu łożysk
Języki publikacji
EN
Abstrakty
EN
Vibration and acoustic emission (AE) signal measurement are established tools in condition monitoring of bearings. This paper presents a comparison between two statistical methods, i.e. kurtosis, and another kurtosis based method called I-kaz using simultaneous signal of vibration and AE. The existence of harmonics fault frequencies in envelope spectrums also have been discussed. The results reveal that both signals are suitable for induction motor bearing fault detection.
PL
W artykule przedstawiono porównanie dwóch metod statystycznych, kurtozy oraz I-kaz używająnych do diagnostyki łożyskowania maszyn elektrycznych. Jako sygnały diagnostyczne wykorzystuuje się pomiar wibracji i emisji akustycznej.
Słowa kluczowe
Rocznik
Strony
208--212
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
  • Dept. of Mechanical and Material Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
autor
  • Dept. of Mechanical and Material Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
autor
  • Dept of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
Bibliografia
  • [1] R. Bogue, “Sensors for condition monitoring: a review of technologies and applications,” Sens. Rev., vol. 33, no. 4, pp. 295–299, 2013.
  • [2] Y. He, X. Zhang, and M. I. Friswell, “Defect Diagnosis for Rolling Element Bearings Using Acoustic Emission,” Journal of Vibration and Acoustics, vol. 131, no. 6. p. 061012, 2009.
  • [3] S. Al-Dossary, R. Hamzah, and D. Mba, “Observations of changes in acoustic emission waveform for varying seede defect sizes in a rolling element bearing,” Appl. Acoust., vol. 70, no. 1, pp. 58–81, 2009.
  • [4] A. M. Al-Ghamd and D. Mba, “A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size,” Mech. Syst. Signal Process., vol. 20, no. 7, pp. 1537–1571, 2006.
  • [5] B. Eftekharnejad, M. R. Carrasco, B. Charnley, and D. Mba, “The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing,” Mech. Syst. Signal Process., vol. 25, no. 1, pp. 266–284, 2011.
  • [6] X. Liu, X. Wu, and C. Liu, “A comparison of acoustic emission and vibration on bearing fault detection,” in 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), 2011, pp. 922–926.
  • [7] N. Tandon and A. Choudhury, “A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings,” Tribol. Int., vol. 32, no. 1999, pp. 469–480, 2000.
  • [8] D. Dyer and R. Stewart, “Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis,” J. Mech. Des., vol. 100, no. 2, pp. 229–235, 1978.
  • [9] W. Guo, P. W. Tse, and A. Djordjevich, “Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition,” Meas. J. Int. Meas. Confed., vol. 45, no. 5, pp. 1308–1322, 2012.
  • [10] V. C. M. N. Leite, J. Guedes, G. Francimeire, C. Veloso, L. Eduardo, S. Member, G. Lambert-torres, E. L. Bonaldi, L. Ely, and D. L. De Oliveira, “Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1855–1865, 2015.
  • [11] X. Zhang, J. Kang, L. Xiao, J. Zhao, and H. Teng, “A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis,” Shock Vib., vol. 2015, p. 22 pages, 2015.
  • [12] Y. Lei, J. Lin, Z. He, and Y. Zi, “Application of an improved kurtogram method for fault diagnosis of rolling element bearings,” Mech. Syst. Signal Process., vol. 25, no. 5, pp. 1738–1749, Jul. 2011.
  • [13] M. Z. Nuawi, M. J. M. Nor, N. Jamaludin, S. Abdullah, F. Lamin, and C. K. E. Nizwan, “Development of integrated Kurtosis-based Algorithm for Z-filter technique,” J. Appl. Sci., vol. 8, no. 8, pp. 1541–1547, 2008.
  • [14] N. I. I. Mansor, M. J. Ghazali, M. Z. Nuawi, and S. E. M. Kamal, “Monitoring bearing condition using airborne sound,” Int. J. Mech. Mater. Eng., vol. 4, no. 2, pp. 152–155, 2009.
  • [15] M. Z. Nuawi, S. Abdullah, A. R. Ismail, R. Zulkifli, M. K. Zakaria, and M. F. H. Hussin, “A Study on Ultrasonic Signals Processing Generated From Automobile Engine Block Using Statistical Analysis,” WSEAS Trans. Signal Process., vol. 4, no. 5, pp. 279–288, 2008.
  • [16] S. Abdullah, M. Z. Nuawi, M. Z. Nopiah, and A. Ariffin, “Study on correlation between strain and vibration signal using hybrid I-Kaz method,” Contin. Mech. Fluids, Heat, vol. 6, no. 3, pp. 79–88, 2010.
  • [17] M. Z. Nuawi, F. Lamin, A. R. Ismail, S. Abdullah, and Z. Wahid, “A Novel Machining Signal Filtering Technique: Znotch Filter,” World Acad. Sci. Eng. Technol. 54, vol. 3, no. 6, pp. 24–29, 2009.
  • [18] M. A. F. Ahmad, M. Z. Nuawi, S. Abdullah, Z. Wahid, Z. Karim, and M. Dirhamsyah, “Development of Tool Wear Machining Monitoring Using Novel Statistical Analysis Method, I-kazTM,” Procedia Eng., vol. 101, pp. 355–362, 2015.
  • [19] J. A. Ghani, M. Rizal, A. Sayuti, M. Z. Nuawi, M. N. Ab. Rahman, and C. H. Che Haron, “New Regression Model and I-Kaz Method for Online Cutting Tool Wear Monitoring,” World Acad. Sci. Eng. Technol. 60, no. 2008, pp. 420–425, 2009.
  • [20] M. B. Ali, S. Abdullah, M. Z. Nuawi, M. M. Padzi, and K. A. Zakaria, “Experimental Analysis of an Instrumented Charpy Impact Using Statistical Study Based Data Analysis,” Int. J. Mech. Mater. Eng., vol. 6, no. 2, pp. 260–268, 2011.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9bdd6108-06d8-4cf7-9ff6-ae907e67cf3d
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