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Fault Analysis of Worm Gear Box Using Symlets Wavelet

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%.
Rocznik
Strony
521--540
Opis fizyczny
Bibliogr. 17 poz., fot., rys., tab., wykr.
Twórcy
  • School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • Advanced Ceramics and Nanotechnology Laboratory, Department of Materials Engineering, University of Concepcion, Chile
  • School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Bibliografia
  • 1. Antoniadou I., Manson G., Staszewski W. J., Barszcz T., Worden K. (2015), A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions, Mechanical Systems and Signal Processing, 64-65: 188-216, doi: 10.1016/j.ymssp.2015.03.003.
  • 2. Elasha F., Ruiz-Cárcel C., Mba D., Kiat G., Nze I., Yebra G. (2014), Pitting detection in worm gearboxes with vibration analysis, Engineering Failure Analysis, 42: 366-376, doi: 10.1016/j.engfailanal.2014.04.028.
  • 3. Elforjani M., Mba D., Muhammad A., Sire A. (2012), Condition monitoring of worm gears, Applied Acoustics, 73 (8): 859-863, doi: 10.1016/j.apacoust.2012.03.008.
  • 4. Fan X., Zuo M. J. (2006), Gearbox fault detection using Hilbert and wavelet packet transform, Mechanical Systems and Signal Processing, 20 (4): 966-982.
  • 5. Ghodake S. B., Mishra A. K., Deokar A. V. (2016a), A review paper on fault detection of worm gearbox International Advanced Research Journal in Science, Engineering and Technology, 3 (1): 161-164.
  • 6. Ghodake S. B., Mishra A. K., Deokar A. V. (2016b), Experimental analysis of faults in worm gearbox using vibration analysis, International Engineering Research Journal, 1: 1518-1523, http://www.ierjournal.org/pupload/mitpgcon/1518-1523.pdf.
  • 7. Ismon M. B., Zaman I. B., Ghazali M. I. (2015), Condition monitoring of variable speed worm gearbox lubricated with different viscosity oil, Applied Mechanics and Materials, 773-774: 178-182, doi: 10.4028/www.scientific.net/AMM.773-774.178.
  • 8. Jing L., Zhao M., Li P., Xu X. (2017), A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox, Measurement, 111: 1-10, doi: 10.1016/j.measurement.2017.07.017.
  • 9. Kalkat M. (2015), Investigations on the effect of oil quality on gearboxes using neural network predictors, Industrial Lubrication and Tribology, 67 (2): 99-109, doi: 10.1108/ilt-02-2013-0020.
  • 10. Kumar R., Singh M. (2013), Outer race defekt width measurement in taper roller bearing using discrete wavelet transform of vibration signal, Measurement, 46 (1): 537-545, doi: 10.1016/j.measurement.2012.08.012.
  • 11. Li C., Liang M. (2012), Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform, Mechanical Systems and Signal Processing, 26: 205-217, doi: 10.1016/j.ymssp.2011.07.001.
  • 12. Peng Z., Kessissoglou N. (2003), An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis, Wear, 255 (7-12): 1221-1232, doi: 10.1016/S0043-1648(03)00098-X.
  • 13. Rajput P., Kumar S. (2015), Development of novel denoising technique using total variation and symlet wavelet filter, International Journal of Engineering Trends and Technology, 22 (3): 109-114.
  • 14. Singh A., Parey A. (2019), Gearbox fault diagnosis under non-stationary conditions with independent angular re-sampling technique applied to vibration and sound emission signals, Applied Acoustics, 144: 11-22, doi: 10.1016/j.apacoust.2017.04.015.
  • 15. Tilak T. N., Krishnakumar S. (2015), Effectiveness of symlets in de-noising fingerprint images, International Journal of Computer Sciences and Engineering, 3 (12): 29-34, http://www.ijcseonline.org/pub_paper/4-IJCSE-01410.pdf.
  • 16. Wang W. J., McFadden P. D. (1996), Application of wavelets to gearbox vibration signals for fault detection, Journal of Sound and Vibration, 192 (5): 927-939, doi: 10.1006/jsvi.1996.0226.
  • 17. Zhang Z-Y., Wang K-S. (2014), Wind turbine fault detection based on SCADA data analysis using ANN, Advances in Manufacturing, 2 (1): 70-78, doi: 10.1007/s40436-014-0061-6.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d3ab8c2a-645d-481a-9213-b1192b8582f7
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