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Gear crack detection using residual signal and empirical mode decomposition

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Diagnosis of gearbox defects at an early stage is very important to avoid catastrophic failures. This article presents experimental results of tests made to evaluate the cracks of the cylindrical gears of a transfer case under advanced test conditions. For the diagnosis of a gearbox, various signal processing techniques are mainly used for the vibration study of the gears, such as: Fast Fourier Transform, synchronous time average, and time-based wavelet transformation, etc. Various methods can be found in the literature which can be used to calculate the residual signal (RS), however, in this paper, we suggest a new method combined empirical mode decomposition (EMD) technique with RS for detection of the crack gear. In order to extract the associated defect characteristics of the transfer box vibration signals, the EMD has been performed. The results show the effectiveness of the EMD method in the evaluation of tooth cracking in spur gears. This effectiveness can be proved by the obtained results of the experimental tests, which were presented and carried out on a test rig equipped with a transfer box.
Rocznik
Strony
1133--1144
Opis fizyczny
Bibliogr. 34 poz., fot. kolor., wykr.
Twórcy
autor
  • Laboratory of Mechanics, Mechanical Engineering Department, Faculty of Technology Sciences, University of Brothers Mentouri Constantine, Algeria
  • Laboratory of Mechanics, Mechanical Engineering Department, Faculty of Technology Sciences, University of Brothers Mentouri Constantine, Algeria
autor
  • Laboratory of Mechanics, Mechanical Engineering Department, Faculty of Technology Sciences, University of Brothers Mentouri Constantine, Algeria
  • Laboratory of Applied Energetics and Materials, Mechanical Engineering, Department, University of Jijel, Algeria
Bibliografia
  • [1] Wójcik, R. and Wlazlo, J.: Damage of the Surface Layer Gears in Grinding Process, Mechanics and Mechanical Engineering, 17(4), 317-323, 2013.
  • [2] Mohammed, O.D., Rantatalo, M. and Aidanpaa, J.: Improving mesh stiffness calculation of cracked gears for the purpose of vibration-based fault analysis, Engineering Failure Analysis, 34, 235-251, 2013.
  • [3] Eritenel, T. and Parker, R.G.: An investigation of tooth mesh nonlinearity and partial contact loss in gear pairs using a lumped-parameter model, Mechanism and Machine Theory, 76, 20-38, 2014.
  • [4] Wang, W.Q., Ismail, F. and Golnaraghi, M.F.: Assessment of gear damage monitoring techniques using vibration measurements, Mechanical Systems and Signal Processing, 15(5), 905-922, 2001.
  • [5] Lei, Y., Zuo, M.J., He, Z. and Zi, Y.: A multidimensional hybrid intelligent method for gear fault diagnosis, Expert Systems with Applications, 37, 1419-1430, 2010.
  • [6] Parey, A. and Tandon, N.: Fault Detection of Spur Gears Using Vibration Monitoring, Lambert, Saarbrucken, Germany, 2010.
  • [7] Li, C.J. and Lee, H.: Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics, Mechanical Systems and Signal Processing, 19, 836-846, 2005.
  • [8] Forrester, B.D.: Analysis of gear vibration in the time-frequency domain, in: Proceedings of the 44th Meeting of Mechanical Failures Prevention Group of the Vibration Institute, Virginia Beach, VA, 3-5 April, 1989.
  • [9] Allgood, G.: Detection of gear wear on the 757/767 internal drive generator using higher order statistics and wavelets, in: 15th International Modal Analysis Conference, Florida, 1997.
  • [10] Belsak, A. and Flasker, J.: Detecting cracks in the tooth root of gears, Engineering Failure Analysis, 14, 1466-1475, 2007.
  • [11] Heirani, H. and Farhangdoost, Kh.: Predicting Depth and Path of Subsurface Crack Propagation at Gear Tooth Flank under Cyclic Contact Loading, Journal of Solid Mechanics, 9(3), 587-598, 2017.
  • [12] Andrade, F.A., Esat, I. and Badi, M.N.M.: Introduces a new technique for early identification of spur gear tooth fatigue cracks, namely the Kolmogorov-Smirnov test, Journal of Sound and Vibration, 240(5), 909-919, 2001.
  • [13] Baydar, N. and Ball, A.: A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution, Mechanical Systems and Signal Processing, 15, 1091-1107, 2001.
  • [14] Yuan, X. and Cia, L.: Variable amplitude Fourier series with its application in gearbox diagnosis|Part II: Experiment and application, Mechanical Systems and Signal Processing, 19, 1067-1081, 2005.
  • [15] Belsak, A. and Flasker, J.: Method for detecting fatigue crack in gears, Theoretical and Applied Fracture Mechanics, 46, 105-113, 2006.
  • [16] Yu, D., Yang, Y. and Cheng, J.: Application of time-frequency entropy method based on Hilbert-Huang transform to gear fault diagnosis, Measurement, 40, 823-830, 2007.
  • [17] Loutridis, S.J.: Instantaneous energy density as a feature for gear fault detection, Mechanical Systems and Signal Processing, 20, 1239-1253, 2006.
  • [18] Barszcz, T. and Randal, R.B.: Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine, Mechanical Systems and Signal Processing, 23, 1352-1365, 2009.
  • [19] Belsak, A. and Flasker, J.: Wavelet analysis for gear crack identification, Engineering Failure Analysis 16, 1983-1990, 2009.
  • [20] Li, H., Zhang, Y. and Zheng, H.: Application of Hermitian wavelet to crack fault detection in gearbox, Mechanical Systems and Signal Processing, 25, 1353-1363, 2011.
  • [21] Wang, W.: K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads, Mechanical Systems and Signal Processing, 70-71, 201-208, 2016.
  • [22] Huang, N.E., Shen, Z., Long S.R., Wu, M.L.C., Shih, H.H., Zheng, Q.N., Yen N.C., Tung, C.C. and Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London Series A - Mathematical Physical and Engineering Sciences, 454, 903-995, 1998.
  • [23] Cheng, J., Yu, D., Tang, J. and Yang, Y.: Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis, Mechanism and Machine Theory, 43, 712-723, 2008.
  • [24] Ricci, R. and Pennacchi, P.: Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions, Mechanical Systems and Signal Processing, 25, 821-838, 2011.
  • [25] Amarnath, M. and Praveen Krishna, I.R.: Local fault detection in helical gears via vibration and acousticsignals using EMD based statistical parameter analysis, Measurement, 58, 154-164, 2014.
  • [26] Parey, A., El Badaoui, M., Guillet, F. and Tandon, N.: Dynamic modeling of spur gear pair and application empirical mode decomposition-based statistical analysis for early detection of localized tooth defect, Journal of Sound and Vibration, 294, 547-561, 2006.
  • [27] Loutridis, S.J.: Damage detection in gear systems using empirical mode decomposition, Engineering Structures, 26, 1833-1841, 2004.
  • [28] Mahgoun, H., Elhad iBekka, R. and Felkaoui, A.: Gearbox fault diagnosis using ensemble empirical mode decomposition (EEMD) and residual signal, Mechanics & Industry, 13, 33-44, 2012.
  • [29] Stewart, R.M.: Some useful data analysis techniques for gear box diagnosis. Applications of time series analysis, Ph.D. Thesis, ISVR, University of Southampton, 1977.
  • [30] Wu, S., Zuo, M. and Parey, A.: Simulation of spur gear dynamics and estimation of fault growth, Journal of Sound and Vibration, 317, 608-624, 2008.
  • [31] Smith, J.D.: Alias errors in precision rotary encoder calibration, Proceedings of the Institution of Mechanical Engineers, 206, 71-73, 1992.
  • [32] McFadden, P.D.: Windows functions for the calculation of the time average of the vibration of the individual planet, ASME Transactions Journal of Vibration and Acoustics, 116, 179-187, 1994.
  • [33] Capdessus, C., Sidahmed, M. and Lacoume, J. L.: Application in gear faults early diagnosis, Mechanical Systems and Signal Processing, 14(3), 371-385, 2000.
  • [34] Rilling, G., Flandrin, P. and Gon calves, P.: On empirical mode decomposition and its algorithms, IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing 3, 8-11, 2003.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9673131c-8306-415f-be30-870dc9272726
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