Advertisement

Role of Signal Processing, Modeling and Decision Making in the Diagnosis of Rolling Element Bearing Defect: A Review

  • Anil Kumar
  • Rajesh KumarEmail author
Article
  • 370 Downloads

Abstract

A significant development in condition monitoring techniques has been observed over the years. The scope of condition monitoring has been shifted from defect identification to its measurement, which was later on extended to automatic prediction of defect. This development is possible because of advancement in the area of signal processing. A number of signal processing and decision making techniques are available each having their own merits and demerits. A specific technique can be most appropriate for a given task, however, it may not be suitable or efficient for a different task. This paper reviewed recent and traditional research, and development in area of defect diagnosis, defect modelling, defect measurement and prognostics. Also it highlights the merit and demerit of various signal processing techniques. This paper is written with the objective to serve as guide map for those who work in the field of condition monitoring.

Keywords

Vibration Signal processing Modeling Artificial intelligence Prognosis 

Notes

Acknowledgements

Authors are thankful to Editor for facilitating reviewer’s feedback to the manuscript. The valuable suggestions of anonymous reviewers in improving the manuscript are thankfully acknowledged.

References

  1. 1.
    Salam, I., Tauqir, A., Haq, A.Ul, Khan, A.Q.: An air crash due to fatigue failure of a ball bearing. Eng. Fail. Anal. 5, 261–269 (1998)Google Scholar
  2. 2.
    Dybała, J., Zimroz, R.: Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal. Appl. Acoust. 77, 195–203 (2014)Google Scholar
  3. 3.
    Li, J.C., Wu, S.M.: On-line detection of localized defects in bearings by pattern recognition analysis. J. Eng. Ind. 111, 331–336 (1989)Google Scholar
  4. 4.
    Nikolaou, N.G., Antoniadis, I.A.: Rolling element bearing fault diagnosis using wavelet packets. NDT&E Int. 35, 197–205 (2002)Google Scholar
  5. 5.
    El-Thalji, I., Jantunen, E.: Fault analysis of the wear fault development in rolling bearings. Eng. Fail. Anal. 57, 470–482 (2015)Google Scholar
  6. 6.
    Randall, R., Antoni, J.: Rolling element bearing diagnostics—a tutorial. Mech. Syst. Signal Process. 25, 485–520 (2011)Google Scholar
  7. 7.
    SKF Group: Pole Position Bearing Self Study Guide. SKF Group, Gothenburg (2008)Google Scholar
  8. 8.
    Koyo, : Ball Roller Bearings: Failures, Causes and Countermeasures. JTEKT Corporation, Osaka (2001)Google Scholar
  9. 9.
    NTN Corporation. Care and Maintenance of Bearings (Cat. No. 3017/E). https://www.ntnglobal.com/en/products/catalog/pdf/3017E.pdf. Accessed 02 October 2018
  10. 10.
    Venner, C.H., Lubrecht, A.A.: Transient analysis of surface features in an EHL line contact in the case of sliding. J. Tribol. 116, 186–193 (1994)Google Scholar
  11. 11.
    Venner, C.H., Lubrecht, A.A.: Numerical simulation of a transverse ridge in a circular EHL contact under rolling/sliding. J. Tribol. 116, 751–761 (1994)Google Scholar
  12. 12.
    Nélias, D., Ville, F.: Detrimental effects of debris dents on rolling contact fatigue. J. Tribol. 122, 55–64 (2000)Google Scholar
  13. 13.
    Ashtekar, A., Sadeghi, F., Stacke, L.: Surface defects effects on bearing dynamics. J. Eng. Tribol. 224, 25–35 (2010)Google Scholar
  14. 14.
    Bormetti, E., Donzella, G., Mazzù, A.: Surface and subsurface cracks in rolling contact fatigue of hardened components. Tribol. Trans. 45, 274–283 (2002)Google Scholar
  15. 15.
    Sakae, C., Ohkoniori, Y., Murakami, Y.: Mode I1 Stress Intensity Factors for Spalling Cracks in Backup Roll. Internal Report, Kyushu University (1999)Google Scholar
  16. 16.
    Nélias, D., Dumont, M.L., Champiot, F., Vincent, A., Girodin, D., Fougéres, R., Flamand, L.: Role of inclusions, surface roughness and operating conditions on rolling contact fatigue. J. Tribol. 121, 240–251 (1999)Google Scholar
  17. 17.
    Melander, A.: A finite element study of short cracks with different inclusion types under rolling contact fatigue load. Int. J. Fatigue 19, 13–24 (1997)Google Scholar
  18. 18.
    Salehizadeh, H., Saka, N.: Crack propagation in rolling line contacts. J. Tribol. 114, 690–697 (1992)Google Scholar
  19. 19.
    Voskamp, P.: Fatigue and material response in rolling contact. ASTM Int. 1327, 152–166 (1998)Google Scholar
  20. 20.
    Rycerz, P., Olver, A., Kadiric, A.: Propagation of surface initiated rolling contact fatigue cracks in bearing steel. Int. J. Fatigue 97, 29–38 (2017)Google Scholar
  21. 21.
    Price, E.D., Lees, A.W., Friswell, M.I., Roylance, B.J.: Online detection of subsurface distress by acoustic emissions. Key Eng. Mater. 245–246, 451–460 (2003)Google Scholar
  22. 22.
    Schwach, D.W., Guo, Y.B.: A fundamental study on the impact of surface integrity by hard turning on rolling contact fatigue. Int. J. Fatigue 28, 1838–1844 (2006)Google Scholar
  23. 23.
    Elforjani, M., Mba, D.: Assessment of natural crack initiation and its propagation in slow speed bearings. Nondestruct. Test. Eval. 24, 261–275 (2009)Google Scholar
  24. 24.
    Eftekharnejad, B., Carrasco, M.R., Charnley, B., Mba, D.: The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing. Mech. Syst. Signal Process. 25, 266–284 (2011)Google Scholar
  25. 25.
    Zhang, Z.Q., Li, G.L., Wang, H.D., Xu, B.S., Piao, Z.Y., Zhu, L.N.: Investigation of rolling contact fatigue damage process of the coating by acoustics emission and vibration signals. Tribol. Int. 47, 25–31 (2012)Google Scholar
  26. 26.
    Liu, J., Shi, Z., Shao, Y.: An investigation of a detection method for a subsurface crack in the outer race of a cylindrical roller bearing. Eksploatacja I Niezawodnosc 19, 211–219 (2017)Google Scholar
  27. 27.
    Dolenc, B., Boškoski, P., Juričić, Ð.: Distributed bearing fault diagnosis based on vibration analysis. Mech. Syst. Signal Process. 66–67, 521–532 (2016)Google Scholar
  28. 28.
    Sawalhi, N., Randall, R.B.: Vibration response of spalled rolling element bearings: observations, simulations and signal processing techniques to track the spall size. Mech. Syst. Signal Process. 25, 846–870 (2011)Google Scholar
  29. 29.
    Kumar, R., Singh, M.: Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal. Measurement 46, 537–545 (2013)Google Scholar
  30. 30.
    Kumar, A., Kumar, R.: Enhancing weak defect features using undecimated and adaptive wavelet transform for estimation of roller defect size in a bearing. Tribol. Trans. 60, 794–806 (2017)Google Scholar
  31. 31.
    Harris, T.A., Kotzalas, M.N.: Rolling Bearing Analysis: Essential Concepts of Bearing Technology, 5th edn. Taylor & Francis, Florida (2006)Google Scholar
  32. 32.
    Wang, D., Miao, Q., Fan, X., Huang, H.-Z.: Rolling element bearing fault detection using an improved combination of Hilbert and Wavelet transforms. J. Mech. Sci. Technol. 23, 3292–3301 (2009)Google Scholar
  33. 33.
    Tsao, W.-C., Li, Y.-F., Le, D.D., Pan, M.-C.: An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosis. Measurement 45, 1489–1498 (2012)Google Scholar
  34. 34.
    Orhan, S., Akturk, N., Celik, V.: Vibration monitoring for defect diagnosis of rolling element bearings as a predictive maintenance tool: comprehensive case studies. NDT&E Int. 39, 293–298 (2006)Google Scholar
  35. 35.
    Rezaei, A., Dadouche, A., Wickramasinghe, V., Dmochowski, W.: A comparison study between acoustic sensors for bearing fault detection under different speed and load using a variety of signal processing techniques. Tribol. Trans. 54, 179–186 (2011)Google Scholar
  36. 36.
    Mahvash, A., Lakis, A.A.: Application of cyclic spectral analysis in diagnosis of bearing faults in complex machinery. Tribol. Trans. 58, 1151–1158 (2015)Google Scholar
  37. 37.
    Jardine, A.K.S., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostic implementing condition-based maintenance. Mech. Syst. Signal Process. 20, 1483–1510 (2006)Google Scholar
  38. 38.
    Dyer, D., Stewart, R.M.: Detection of rolling element bearing damage by statistical vibration analysis. ASME J. Mech. Des. 100, 229–235 (1978)Google Scholar
  39. 39.
    Li, C.Q., Pickering, C.J.: Robustness and sensitivity of non-dimensional amplitude parameters for diagnosis of fatigue spalling. Cond. Monit. Diagn. Technol. 2, 81–84 (1982)Google Scholar
  40. 40.
    Martin, H.R., Ismail, F., Sakuta, A.: Algorithms for statistical moment evaluation for machine health monitoring. Mech. Syst. Signal Process. 6, 317–327 (1992)Google Scholar
  41. 41.
    Randall, R.B.: Computer aided vibration spectrum trend analysis for condition monitoring. Maint. Manag. Int. 5, 161–167 (1985)Google Scholar
  42. 42.
    Braun, S. (ed.): Mechanical Signature Analysis. Academic Press, London (1986)Google Scholar
  43. 43.
    Tandon, N., Nakra, B.C.: Comparison of vibration and acoustic measurement techniques for the condition monitoring of rolling element bearings. Tribol. Int. 25, 205–212 (1992)Google Scholar
  44. 44.
    William, P.E., Hoffman, M.W.: Identification of bearing faults using time domain zero-crossings. Mech. Syst. Signal Process. 25, 3078–3088 (2011)Google Scholar
  45. 45.
    Doguer, T., Strackeljan, J.: Vibration analysis using time domain methods for the detection of small roller bearing defects. In: Proceedings of the SIRM 2009 8th International Conference on Vibrations in Rotating Machines, Vienna, Austria, 23–25 February 2009Google Scholar
  46. 46.
    Williams, T., Ribadeneira, X., Billington, S., Kurfess, T.: Rolling element bearing diagnostics in run-to-failure lifetime testing. Mech. Syst. Signal Process. 15, 979–993 (2001)Google Scholar
  47. 47.
    Vyas, N.S., Satishkumar, D.: Artificial neural network design for fault identification in a rotor-bearing system. Mech. Mach. Theory 36, 157–175 (2001)zbMATHGoogle Scholar
  48. 48.
    Samanta, B., AL-Balushi, K.R.: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Signal Process. 17, 317–328 (2003)Google Scholar
  49. 49.
    Saxena, A., Saad, A.: Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Appl. Soft Comput. 7, 441–454 (2007)Google Scholar
  50. 50.
    Alguindigue, I.E., Loskiewicz-Buczak, A., Uhrig, R.E.: Monitoring and diagnosis of rolling element bearings using artificial neural networks. IEEE Trans. Industr. Electron. 40, 209–216 (1993)Google Scholar
  51. 51.
    Zhang, Y., Zuo, H., Bai, F.: Classification of fault location and performance degradation of a roller bearing. Measurement 46, 1178–1189 (2013)Google Scholar
  52. 52.
    Barkov, A., Barkova, N., Mitchell, J.: Condition assessment and life prediction of rolling element bearings. Sound Vib. 28, 10–17 (1995)Google Scholar
  53. 53.
    Al-Ghamd, A.M., Mba, D.: 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. 20, 1537–1571 (2006)Google Scholar
  54. 54.
    Yang, W., Court, R.: Experimental study on the optimum time for conducting bearing maintenance. Measurement 46, 2781–2791 (2013)Google Scholar
  55. 55.
    Darlow, M.S., Badgley, R.H., Hogg, G.W.: Application of high frequency resonance techniques for bearing diagnostics in helicopter gearboxes. US Army Air Mobility Research and Development Laboratory Technical Report-74–77, October (1974)Google Scholar
  56. 56.
    Tse, P.W., Peng, Y.H., Yam, R.: Wavelet analysis and envelope detection for rolling element bearing fault diagnosis-their effectiveness and flexibilities. J. Vib. Acoust. 123, 303–313 (2001)Google Scholar
  57. 57.
    Adam, I.: Complex Wavelet Transform: application to denoising. Thesis, Ph.D. Politehnica University of Timisoara and Université deRennes 1, Timisoara and Rennes, Romania and France (2010)Google Scholar
  58. 58.
    Nataraj, C., Kappaganthu, K.: Vibration—based diagnostics of rolling element bearings: state of the art and challenges. In: Proceedings of the 13th World Congress in Mechanism and Machine Science, Guanajua´to Mexico, 19–25 June 2011Google Scholar
  59. 59.
    Liang, B., Iwnicki, S.D., Zhao, Y.: Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis. Mech. Syst. Signal Process. 39, 342–360 (2013)Google Scholar
  60. 60.
    Zheng, G.T., Wang, W.J.: A new cepstral analysis procedure of recovering excitations for transient components of vibration signals and applications to rotating machinery condition monitoring. J. Vib. Acoust. 123, 222–229 (2001)Google Scholar
  61. 61.
    Harris, C.M., Piersol, A.G.: Harris’ Shock and Vibration Handbook. McGraw-Hill, New York (2002)Google Scholar
  62. 62.
    Bogert, B.P., Healy, M.J.R., Tukey, J.W.: 1963. The quefrency analysis of time series for echoes: cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking. In: Rosenblatt, M. (ed.) Proceedings of the Symposium on Time Series Analysis, Wiley, New York vol. 15, pp. 209–243Google Scholar
  63. 63.
    van der Merwe, N.T., Hoffman, A.J.: A modified cepstrum analysis applied to vibrational signals. In: Proceedings of the 14th International Conference on Digital Signal Processing (DSP2002), Santorini, Greece, 1–3 July. pp. 873–876 (2002)Google Scholar
  64. 64.
    McFadden, P.D., Smith, J.D.: The vibration produced by multiple point defects in a rolling element bearing. J. Sound Vib. 98, 263–273 (1985)Google Scholar
  65. 65.
    Patel, V.N., Tandon, N., Pandey, R.K.: Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator. Measurement 45, 960–970 (2012)Google Scholar
  66. 66.
    Sheen, Y.-T., Hung, C.-K.: Constructing a wavelet-based envelope function for vibration signal analysis. Mech. Syst. Signal Process. 18, 19–126 (2004)Google Scholar
  67. 67.
    Sheen, Y.-T.: 3D spectrum analysis for vibration signals by wavelet based demodulation. Mech. Syst. Signal Process. 20, 843–853 (2006)Google Scholar
  68. 68.
    Randall, R.B.: Noise and vibration data analysis. In: Crocker, M. (ed.) Handbook of Noise and Vibration Control. Wiley, New Jersey (2007)Google Scholar
  69. 69.
    Carcaterra, A., Sestieri, A.: Complex envelope displacement analysis: a quasi-static approach to vibrations. J. Sound Vib. 201, 205–233 (1997)Google Scholar
  70. 70.
    Abboud, D., Antoni, J., Sieg-Zieba, S., Eltabach, M.: Envelope analysis of rotating machine vibrations in variable speed conditions: a comprehensive treatment. Mech. Syst. Signal Process. 84, 200–226 (2017)Google Scholar
  71. 71.
    Sheen, Y.T.: An envelope analysis based on the resonance modes of the mechanical system for the bearing defect diagnosis. Measurement 43, 912–934 (2010)Google Scholar
  72. 72.
    Guo, L., Chen, J., Li, X.: Rolling bearing fault classification based on envelope spectrum and support vector machine. J. Vib. Control 15, 1349–1363 (2009)zbMATHGoogle Scholar
  73. 73.
    Antoni, J.: Fast computation of the kurtogram for the detection of transient faults. Mech. Syst. Signal Process. 21, 108–124 (2007)Google Scholar
  74. 74.
    Zhang, Y., Randall, R.B.: Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram. Mech. Syst. Signal Process. 23, 1509–1517 (2009)Google Scholar
  75. 75.
    Barszcz, T., JabŁoński, A.: A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mech. Syst. Signal Process. 25, 431–451 (2011)Google Scholar
  76. 76.
    Borghesani, P., Pennacchi, P., Chatterton, S.: The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings. Mech. Syst. Signal Process. 43, 25–43 (2014)Google Scholar
  77. 77.
    Girondin, V., Pekpe, K.M., Morel, H., Cassar, J.P.: Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis. Mech. Syst. Signal Process. 38, 499–514 (2013)Google Scholar
  78. 78.
    Bonnardot, F., Randall, R., Guillet, F.: Extraction of second-order Cyclostationary sources—Application to vibration analysis. Mech. Syst. Signal Process. 19, 1230–1244 (2005)Google Scholar
  79. 79.
    Boustany, R., Antoni, J.: A subspace method for the blind extraction of cyclostationary source: application to rolling element bearing diagnostics. Mech. Syst. Signal Process. 19, 1245–1259 (2005)Google Scholar
  80. 80.
    Kilundu, B., Chiementin, X., Duez, J., Mba, D.: Cyclostationarity of Acoustic Emissions (AE) for monitoring bearing defects. Mech. Syst. Signal Process. 25(6), 2061–2072 (2011)Google Scholar
  81. 81.
    Andrade, F.A., Esat, I., Badi, M.N.M.: Gearbox fault detection using statistical methods, time–frequency methods (STFT and Wigner–Ville distribution) and harmonic wavelet—A comparative study. In: Proceedings of COMADEM 99, Chipping Norton. pp. 77–85 (1999)Google Scholar
  82. 82.
    Zhang, Y., Bingham, C., Yang, Z., Ling, B.W.-K., Gallimore, M.: Machine fault detection by signal denoising—with application to industrial gas turbines. Measurement 46, 353–359 (2013)Google Scholar
  83. 83.
    Qin, S.R., Zhong, Y.M.: Research on the unified mathematical model for FT, STFT and WT and its applications. Mech. Syst. Signal Process. 18, 1335–1347 (2004)Google Scholar
  84. 84.
    Wigner, E.P.: On the quantum correction for thermodynamic equilibrium. Phys. Rev. 40, 749–759 (1932)zbMATHGoogle Scholar
  85. 85.
    Ville, J.: Theorie et application de la notion de signal analytique. Cables et Trans. 2, 61–74 (1948)Google Scholar
  86. 86.
    Meng, Q., Qu, L.: Rotating machinery fault diagnosis using Wigner distribution. Mech. Syst. Signal Process. 5, 155–166 (1991)Google Scholar
  87. 87.
    Pan, M.-C., Brussel, H.V., Sas, P., Verbeure, B.: Fault diagnosis of joint backlash. J. Vib. Acoust. 120, 13–24 (1998)Google Scholar
  88. 88.
    Koo, I.S., Kim, W.W.: The development of reactor coolant pump vibration monitoring and a diagnostic system in the nuclear power plant. ISA Trans. 39, 309–316 (2000)Google Scholar
  89. 89.
    Baydar, N., Ball, A.: A comparative study of acoustic and vibration signals in detection of gear failures using Wigner–Ville distribution. Mech. Syst. Signal Process. 15, 1091–1107 (2001)Google Scholar
  90. 90.
    Grossman, A., Morlet, J.: Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15, 723–736 (1984)MathSciNetzbMATHGoogle Scholar
  91. 91.
    Polikar, R.: Multiresolution analysis and the continuous wavelet transform, The wavelet tutorial, Part III, Ames, Lowa, USA (1996)Google Scholar
  92. 92.
    Yang, W.-X.: A natural way for improving the accuracy of the continuous wavelet transforms. J. Sound Vib. 306, 928–939 (2007)MathSciNetGoogle Scholar
  93. 93.
    Rubini, R., Meneghetti, U.: Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mech. Syst. Signal Process. 15, 287–302 (2001)Google Scholar
  94. 94.
    Zheng, H., Li, Z., Chen, X.: Gear fault diagnosis based on continuous wavelet transform. Mech. Syst. Signal Process. 16, 447–457 (2002)Google Scholar
  95. 95.
    Tse, P.W., Yang, W., Tam, H.Y.: Machine fault diagnosis through an effective exact wavelet analysis. J. Sound Vib. 277, 1005–1024 (2004)Google Scholar
  96. 96.
    Yan, R., Gao, R.X.: Hilbert-Huang transform-based vibration signal analysis for machine health monitoring. IEEE Trans. Instrum. Meas. 55, 2320–2329 (2006)Google Scholar
  97. 97.
    Qiu, H., Lee, J., Lin, J., Yu, G.: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. J. Sound Vib. 289, 1066–1090 (2006)Google Scholar
  98. 98.
    Junsheng, C., Dejie, Y., Yu, Y.: Application of an impulse response wavelet to fault diagnosis of rolling bearings. Mech. Syst. Signal Process. 21, 920–929 (2007)Google Scholar
  99. 99.
    Hong, H., Liang, M.: Fault severity assessment for rolling element bearings using the Lempel-Ziv complexity and continuous wavelet transform. J. Sound Vib. 320, 452–458 (2009)Google Scholar
  100. 100.
    Su, W., Wang, F., Zhu, H., Zhang, Z., Guo, Z.: Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement. Mech. Syst. Signal Process. 24, 1458–1472 (2010)Google Scholar
  101. 101.
    Wang, X., Zi, Y., He, Z.: Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis. Mech. Syst. Signal Process. 25, 285–304 (2011)Google Scholar
  102. 102.
    Kumar, A. and Kumar, R.: 2013. Adaptive Wavelet Based Signal Processing Scheme for Detecting Localized Defects in Rolling Element of Taper Roller Bearing. In: Proceedings of Surveillance 7, Institute of Technology of Chartres, France, October 2013Google Scholar
  103. 103.
    Kumar, R., Kumar, A.: Fusion of microphone and accelerometer sensing for the identification and measurement of inner race defect. In: Proceedings of the 10th International Conference on Measurement, Smolenice, Slovakia, pp. 183–186Google Scholar
  104. 104.
    Wang, J., Gao, R.X., Yan, R.: Multi-scale enveloping order spectrogram for rotating machine health diagnosis. Mech. Syst. Signal Process. 46, 28–44 (2014)Google Scholar
  105. 105.
    He, W., Miao, Q., Azarian, M., Pecht, M.: Health monitoring of cooling fan bearings based on wavelet filter. Mech. Syst. Signal Process. 64–65, 149–161 (2015)Google Scholar
  106. 106.
    Wang, J., He, Q., Kong, F.: Multiscale envelope manifold for enhanced fault diagnosis of rotating machines. Mech. Syst. Signal Process. 52–53, 376–392 (2015)Google Scholar
  107. 107.
    Kumar, A., Kumar, R.: Time–Frequency analysis and support vector machine in automatic detection of defect from vibration signal of centrifugal pump. Measurement 108, 119–133 (2017)Google Scholar
  108. 108.
    Saravanan, N., Ramachandran, K.I.: Fault diagnosis of spur bevel gear box using discrete wavelet features and decision tree classification. Expert Syst. Appl. 36, 9564–9573 (2009)Google Scholar
  109. 109.
    Gokgoz, E., Subasi, A.: Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed. Signal Process. Control 18, 138–144 (2015)Google Scholar
  110. 110.
    Liu, H., Tian, H.-Q., Pan, D.-F., Li, Y.-F.: Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks. Appl. Energy 107, 191–205 (2013)Google Scholar
  111. 111.
    Khorrami, H., Moavenian, M.: A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Syst. Appl. 37, 5751–5757 (2010)Google Scholar
  112. 112.
    Prabhakar, S., Mohanty, A.R., Sekhar, A.S.: Application of discrete wavelet transform for detection of ball bearing race faults. Tribol. Int. 35, 793–800 (2002)Google Scholar
  113. 113.
    Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, San Diego (1999)zbMATHGoogle Scholar
  114. 114.
    Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process. 96, 1–15 (2014)Google Scholar
  115. 115.
    Mori, K.N., Kasashmi, T., Ueno, Y.: Prediction of spalling on ball bearings by applying discrete wavelet transform to vibration signals. Wear 8, 162–195 (1996)Google Scholar
  116. 116.
    Lou, X., Loparo, K.A.: Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech. Syst. Signal Process. 18, 1077–1095 (2004)Google Scholar
  117. 117.
    Purushotham, V., Narayanana, S., Prasad, S.A.N.: Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT&E Int. 38, 654–664 (2005)Google Scholar
  118. 118.
    Abbasion, S., Rafsanjani, A., Farshidianfar, A., Irani, N.: Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mech. Syst. Signal Process. 21, 2933–2945 (2007)Google Scholar
  119. 119.
    Hao, R., Chu, F.: Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings. J. Sound Vib. 320, 1164–1177 (2009)Google Scholar
  120. 120.
    Li, P., Kong, F., He, Q., Liu, Y.: Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis. Measurement 46, 497–505 (2013)Google Scholar
  121. 121.
    Singh, M., Yadav, R.K., Kumar, R.: Discrete wavelet transform based measurement of inner race defect width in taper roller bearing. Mapan—J. Metrol Soc. India 28, 17–23 (2013)Google Scholar
  122. 122.
    Liu, S., Du, R., Yang, S.: Fault diagnosis for diesel engines by wavelet packet analysis of vibration signal measured on cylinder head. J. Vib. Eng. 13, 577–584 (2000)Google Scholar
  123. 123.
    Ortiz, E., Syrmos, V.: Support vector machines and wavelet packet analysis for fault detection and identification. In: Proceedings of IJCNN 06, International Joint Conference on Neural Networks, Vancouver, BC, Canada, 16–21 July pp. 3449–3456 (2006)Google Scholar
  124. 124.
    Wu, J.D., Liu, C.H.: An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Syst. Appl. 36, 4278–4286 (2009)Google Scholar
  125. 125.
    Fan, X., Zuo, M.J.: Gearbox fault detection using Hilbert and wavelet packet transform. Mech. Syst. Signal Process. 20, 966–982 (2006)Google Scholar
  126. 126.
    Yadav, M., Wadhwani, S.: Automatic fault classification of rolling element bearing using wavelet packet decomposition and artificial neural network. Int. J. Eng. Technol. 3, 270–276 (2011)Google Scholar
  127. 127.
    Bin, G.F., Gao, J.J., Li, X.J., Dhillon, B.S.: Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mech. Syst. Signal Process. 27, 696–711 (2012)Google Scholar
  128. 128.
    Rajeswari, C., Sathiyabhama, B., Devendiran, S., Manivannan, K.: Bearing fault diagnosis using wavelet packet transform, hybrid PSO and support vector machine. Proc. Eng. 97, 1772–1783 (2014)Google Scholar
  129. 129.
    Wang, Y., Xu, G., Liang, L., Jiang, K.: Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mech. Syst. Signal Process. 54–55, 259–276 (2015)Google Scholar
  130. 130.
    Chacon, J.L.F., Kappatos, V., Balachandran, W., Gan, T.-H.: A novel approach for incipient defect detection in rolling bearings using acoustic emission technique. Appl. Acoust. 89, 88–100 (2015)Google Scholar
  131. 131.
    Cai, G., Chen, X., He, Z.: Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox. Mech. Syst. Signal Process. 41, 34–53 (2013)Google Scholar
  132. 132.
    Wang, H., Chen, J., Dong, G.: Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform. Mech. Syst. Signal Process. 48, 103–119 (2014)Google Scholar
  133. 133.
    He, W., Zi, Y., Chen, B., Wu, F., He, Z.: Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform. Mech. Syst. Signal Process. 54–55, 457–480 (2015)Google Scholar
  134. 134.
    Shi, J., Liang, M.: Intelligent bearing fault signature extraction via iterative oscillatory behaviour based signal decomposition (IOBSD). Expert Syst. Appl. 45, 40–55 (2016)Google Scholar
  135. 135.
    Huang, N.E., Shen, Z., Long, S.R.: The Empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. 454, 903–995 (1998)MathSciNetzbMATHGoogle Scholar
  136. 136.
    Kedadouche, M., Thomas, M., Tahan, A.: A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis. Mech. Syst. Signal Process. 81, 88–107 (2016)Google Scholar
  137. 137.
    Keshtan, M.N., Khajavi, M.N.: Bearings fault diagnosis using vibrational signal analysis by EMD method. Res. Nondestr. Eval. 27, 155–174 (2016)Google Scholar
  138. 138.
    Wu, Z.H., Huang, N.E.: Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv. Adaptive Data Anal. 1, 1–41 (2009)Google Scholar
  139. 139.
    Jiang, H., Li, C., Li, H.: An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis. Mech. Syst. Signal Process. 36, 225–239 (2013)Google Scholar
  140. 140.
    Zhang, X., Zhou, J.: Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mech. Syst. Signal Process. 41, 127–140 (2013)Google Scholar
  141. 141.
    Jiang, F., Zhu, Z., Li, W., Zhou, G., Chen, G.: Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis. J. Sound Vib. 333, 3321–3331 (2014)Google Scholar
  142. 142.
    Rai, V.K., Mohanty, A.R.: Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform. Mech. Syst. Signal Process. 21, 2607–2615 (2007)Google Scholar
  143. 143.
    Ricci, R., Pennacchi, P.: Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions. Mech. Syst. Signal Process. 25, 821–838 (2011)Google Scholar
  144. 144.
    Zhao, X., Patel, T.H., Zuo, M.J.: Multivariate EMD and full spectrum based condition monitoring for rotating machinery. Mech. Syst. Signal Process. 27, 712–728 (2012)Google Scholar
  145. 145.
    Pan, M.-C., Tsao, W.-C.: Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings. Int. J. Mech. Sci. 69, 114–124 (2013)Google Scholar
  146. 146.
    Zhao, S., Liang, L., Xu, G., Wang, J., Zhang, W.: Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method. Mech. Syst. Signal Process. 40, 154–177 (2013)Google Scholar
  147. 147.
    Hong, S., Zhou, Z., Zio, E., Hong, K.: Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digit. Signal Process. 27, 159–166 (2014)Google Scholar
  148. 148.
    Saidi, L., Ali, J.B., Fnaiech, F.: Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis. ISA Trans. 53, 1650–1660 (2014)Google Scholar
  149. 149.
    Zhang, Y., Tang, B., Xiao, X.: Time–frequency interpretation of multi-frequency signal from rotating machinery using an improved Hilbert-Huang transform. Measurement 82, 221–239 (2016)Google Scholar
  150. 150.
    Gilles, J.: Empirical wavelet transform. IEEE Trans. Signal Process 61, 3999–4010 (2013)MathSciNetzbMATHGoogle Scholar
  151. 151.
    Kedadouche, M., Liu, Z., Vu, V.-H.: A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis. Measurement 90, 292–308 (2016)Google Scholar
  152. 152.
    Pan, J., Chen, J., Zi, Y., Yuan, J., Chen, B., He, Z.: Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain fault diagnosis. Mech. Syst. Signal Process. 80, 533–552 (2016)Google Scholar
  153. 153.
    Cao, H., Fan, F., Zhou, K., He, Z.: Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement 82, 439–449 (2016)Google Scholar
  154. 154.
    Liu, B., Ling, S.F., Gribonval, R.: Bearing failure using matching pursuit. NDT&E Int. 35, 255–262 (2002)Google Scholar
  155. 155.
    Yang, H., Mathew, J., Ma, L.: Fault diagnosis of rolling element bearings using basis pursuit. Mech. Syst. Signal Process. 19, 341–356 (2005)Google Scholar
  156. 156.
    Cui, L., Wu, N., Ma, C., Wang, H.: Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary. Mech. Syst. Signal Process. 68–69, 34–43 (2016)Google Scholar
  157. 157.
    Li, Y., Liang, X., Lin, J., Chen, Y., Liu, J.: Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter. Mech. Syst. Signal Process. 101, 435–448 (2018)Google Scholar
  158. 158.
    Tian, X., Gu, J.X., Rehab, I., Abdalla, G.M., Gu, F., Ball, A.D.: A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the Kurtogram. Mech. Syst. Signal Process. 100, 167–187 (2018)Google Scholar
  159. 159.
    Lv, J., Yu, J.: Average combination difference morphological filters for fault feature extraction of bearing. Mech. Syst. Signal Process. 100, 827–845 (2018)Google Scholar
  160. 160.
    Yang, T., Guo, Y., Wu, X., Na, J., Fung, R.F.: Fault feature extraction based on combination of envelope order tracking and cICA for rolling element bearings. Mech. Syst. Signal Process. 113, 131–144 (2018)Google Scholar
  161. 161.
    Cheng, J., Zhang, K., Yang, Y.: An order tracking technique for the gear fault diagnosis using local mean decomposition method. Mech. Mach. Theory 55, 67–76 (2012)Google Scholar
  162. 162.
    Hu, Y., Tu, X., Li, F., Li, H., Meng, G.: An adaptive and tacholess order analysis method based on enhanced empirical wavelet transform for fault detection of bearings with varying speeds. J. Sound Vib. 409, 241–255 (2017)Google Scholar
  163. 163.
    Wang, Y., Xu, G., Luo, A., Liang, L., Jiang, K.: An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection. J. Sound Vib. 367, 233–249 (2015)Google Scholar
  164. 164.
    Zhong, J., Zhong, S., Zhang, Q., Peng, Z.: Measurement of instantaneous rotational speed using double-sine-varying-density fringe pattern. Mech. Syst. Signal Process. 103, 117–130 (2018)Google Scholar
  165. 165.
    Huang, H., Baddour, N., Liang, M.: Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time–frequency curve extraction. J. Sound Vib. 414, 43–60 (2018)Google Scholar
  166. 166.
    Liu, J., Shao, Y., Lim, T.C.: Vibration analysis of ball bearings with a localized defect applying piecewise response function. Mech. Mach. Theory 56, 156–169 (2012)Google Scholar
  167. 167.
    Khanam, S., Tandon, N., Dutt, J.K.: Fault size estimation in the outer race of ball bearing using discrete wavelet transform of the vibration signal. Proc. Technol. 14, 12–19 (2014)Google Scholar
  168. 168.
    Jena, D.P., Panigrahi, S.N.: Precise measurement of defect width in tapered roller bearing using vibration signal. Measurement 55, 39–50 (2014)Google Scholar
  169. 169.
    Wang, W., Sawalhi, N., Becker, A.: Size estimation for naturally occurring bearing faults using synchronous averaging of vibration signals. J. Vib. Acoust. 138, 051015 (2016)Google Scholar
  170. 170.
    Hemmati, F., Orfali, W., Gadala, M.S.: Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Appl. Acoust. 104, 101–118 (2016)Google Scholar
  171. 171.
    Liu, J., Shao, Y.: A new dynamic model for vibration analysis of a ball bearing due to a localized surface defect considering edge topographies. Nonlinear Dyn. 79, 1329–1351 (2015)Google Scholar
  172. 172.
    Moazen-Ahmadi, A., Howard, C.Q.: A defect size estimation method based on operational speed and path of rolling elements in defective bearings. J. Sound Vib. 385, 138–148 (2016)Google Scholar
  173. 173.
    Liu, J., Shao, Y., Zhu, W.D.: A new model for the relationship between vibration characteristics caused by the time-varying contact stiffness of a deep groove ball bearing and defect sizes. J. Tribol. 137(3), 031101 (2015).  https://doi.org/10.1115/1.4029461 CrossRefGoogle Scholar
  174. 174.
    Khanam, S., Dutt, J.K., Tandon, N.: Impact force based model for bearing local fault identification. J. Vib. Acoust. 137, 051002 (2015).  https://doi.org/10.1115/1.4029988 CrossRefGoogle Scholar
  175. 175.
    Moazen Ahmadi, A., Petersen, D., Howard, C.: A nonlinear dynamic vibration model of defective bearings—the importance of modelling the finite size of rolling elements. Mech. Syst. Signal Process. 52–53, 309–326 (2015)Google Scholar
  176. 176.
    Mishra, C., Samantaray, A.K., Chakraborty, G.: Ball bearing defect models: a study of simulated and experimental fault signatures. J. Sound Vib. 400, 86–112 (2017)Google Scholar
  177. 177.
    Liu, J., Shao, Y.: Dynamic modeling for rigid rotor bearing systems with a localized defect considering additional deformations at the sharp edges. J. Sound Vib. 398, 84–102 (2017)Google Scholar
  178. 178.
    Liu, J., Shao, Y.: An improved analytical model for a lubricated roller bearing including a localized defect with different edge shapes. J. Vib. Control 24, 3894–3907 (2018)MathSciNetGoogle Scholar
  179. 179.
    Singh, S., Howard, C.Q., Hansen, C.H., Köpke, U.G.: Analytical validation of an explicit finite element model of a rolling element bearing with a localised line spall. J. Sound Vib. 416, 94–110 (2018)Google Scholar
  180. 180.
    Liu, J., Shi, Z., Shao, Y.: An analytical model to predict vibrations of a cylindrical roller bearing with a localized surface defect. Nonlinear Dyn. 89, 2085–2102 (2017)Google Scholar
  181. 181.
    Liu, Y., Zhu, Y., Yan, K., Wang, F., Hong, J.: A novel method to model effects of natural defect on roller bearing. Tribol. Int. 122, 169–178 (2018)Google Scholar
  182. 182.
    Hammad, M., Kahn, N.Z.A., Saeed, A.: Experimental analysis and modelling of c-crack propagation in silicon nitride ball bearing element under rolling contact fatigue. Tribol. Int. 126, 386–401 (2018)Google Scholar
  183. 183.
    Cerrada, M., Sánchez, R.V., Li, C., Pacheco, F., Cabrera, D., Valente de Oliveira, J., Vásquez, R.E.: A review on data-driven fault severity assessment in rolling bearings. Mech. Syst. Signal Process. 99, 169–196 (2018)Google Scholar
  184. 184.
    Li, Y., Billington, S., Zhang, C., Kurfess, T., Danyluk, S., Liang, S.Y.: Adaptive prognostics for rolling element bearing condition. Mech. Syst. Signal Process. 13, 103–113 (1999)Google Scholar
  185. 185.
    Yang, Y., Yu, D., Cheng, J.: A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement 40, 943–950 (2007)Google Scholar
  186. 186.
    Sreejith, B., Verma, A.K. Srividya, A.: Fault diagnosis of rolling element bearing using time-domain features and neural networks. In: Proceedings of IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, India, pp. 1–6, 8–10 December 2008Google Scholar
  187. 187.
    Widodo, A., Kim, E.Y., Son, J.-D., Yang, B.-S., Tan, A.C.C., Gu, D.-S., Choi, B.-K., Mathew, J.: Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Syst. Appl. 36, 7252–7261 (2009)Google Scholar
  188. 188.
    Al-Raheem, K.F., Abdul-Karem, W.: Rolling bearing fault diagnostics using artificial neural networks based on Laplace wavelet analysis. Int. J. Eng. Sci. Technol. 2, 278–290 (2010)Google Scholar
  189. 189.
    Lei, Y., He, Z., Zi, Y.: EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Syst. Appl. 38, 7334–7341 (2011)Google Scholar
  190. 190.
    Fernández-Francos, D., Martínez-Rego, D., Fontenla-Romero, O., Alonso-Betanzos, A.: Automatic bearing fault diagnosis based on one-class m-SVM. Comput. Ind. Eng. 64, 357–365 (2013)Google Scholar
  191. 191.
    Kankar, P.K., Sharma, S.C., Harsha, S.P.: Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform. Neurocomputing 110, 9–17 (2013)Google Scholar
  192. 192.
    Pandya, D.H., Upadhyay, S.H., Harsha, S.P.: Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN. Expert Syst. Appl. 40, 4137–4145 (2013)Google Scholar
  193. 193.
    Shen, C., Wang, D., Kong, F., Tse, P.W.: Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement 46, 1551–1564 (2013)Google Scholar
  194. 194.
    Du, W., Tao, J., Li, Y., Liu, C.: Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mech. Syst. Signal Process. 43, 57–75 (2014)Google Scholar
  195. 195.
    Chen, F., Tang, B., Song, T., Li, L.: Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization. Measurement 47, 576–590 (2014)Google Scholar
  196. 196.
    Shao, R., Hu, W., Wang, Y., Qi, X.: The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform. Measurement 54, 118–132 (2014)Google Scholar
  197. 197.
    Unal, M., Onat, M., Demetgul, M., Kucuk, H.: Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement 58, 187–196 (2014)Google Scholar
  198. 198.
    Zhu, K., Xigeng, S., Dongxin, X.: A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm. Measurement 47, 669–675 (2014)Google Scholar
  199. 199.
    Safizadeh, M.S., Latifi, S.K.: Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Inf. Fusion. 18, 1–8 (2014)Google Scholar
  200. 200.
    Tang, B., Song, T., Li, F., Deng, L.: Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine. Renew. Energy 62, 1–9 (2014)Google Scholar
  201. 201.
    Liu, Z., Zuo, M.J., Qin, Y.: Remaining useful life prediction of rolling element bearings based on health state assessment. Proc. Inst. Mech. Eng. C 230, 314–330 (2016)Google Scholar
  202. 202.
    Kumar, A., Kumar, R.: Least square optimization for adaptive wavelet generation and automatic prediction of defect size in the bearing using Levenberg-Marquardt backpropagation. J. Nondestr. Eval. 36, 1–16 (2017)Google Scholar
  203. 203.
    Jia, F., Lei, Y., Lin, J., Zhou, X., Lu, N.: Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72–73, 303–315 (2016)Google Scholar
  204. 204.
    Guo, X., Chen, L., Shen, C.: Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 93, 490–502 (2016)Google Scholar
  205. 205.
    Shao, H., Jiang, H., Zhang, H., Duan, W., Liang, T., Wu, S.: Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech. Syst. Signal Process. 100, 743–765 (2018)Google Scholar
  206. 206.
    Li, Y., Kurfess, T.R., Liang, S.Y.: Stochastic prognostics for rolling element bearings. Mech. Syst. Signal Process. 14, 747–762 (2000)Google Scholar
  207. 207.
    Mahamad, A.K., Saon, S., Hiyama, T.: Predicting remaining useful life of rotating machinery based artificial neural network. Comput. Math. Appl. 60, 1078–1087 (2010)zbMATHGoogle Scholar
  208. 208.
    Li, R., Sopon, P., He, D.: Fault features extraction for bearing prognostics. J. Intell. Manuf. 23, 313–321 (2012)Google Scholar
  209. 209.
    Maio, F.Di, Tsui, K.L., Zio, E.: Combining Relevance Vector Machines and exponential regression for bearing residual life estimation. Mech. Syst. Signal Process. 31, 405–427 (2012)Google Scholar
  210. 210.
    Benkedjouh, T., Medjaher, K., Zerhouni, N., Rechak, S.: Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Eng. Appl. Artif. Intell. 26, 1751–1760 (2013)Google Scholar
  211. 211.
    Dong, S., Luo, T.: Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement 46, 3143–3152 (2013)Google Scholar
  212. 212.
    Shakya, P., Kulkarni, M.S., Darpe, A.K.: A novel methodology for online detection of bearing health status for naturally progressing defect. J. Sound Vib. 333, 5614–5629 (2014)Google Scholar
  213. 213.
    Ali, J.B., Fnaiech, N., Saidi, L., Chebel-Morello, B., Fnaiech, F.: Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl. Acoust. 89, 16–27 (2015)Google Scholar
  214. 214.
    Singh, J., Darpe, A.K., Singh, S.P.: Bearing damage assessment using Jensen-Rényi Divergence based on EEMD. Mech. Syst. Signal Process. 87, 307–339 (2017)Google Scholar
  215. 215.
    Rai, A., Upadhyay, S.H.: Bearing performance degradation assessment based on a combination of empirical mode decomposition and K-medoids clustering. Mech. Syst. Signal Process. 93, 16–29 (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Amity UniversityNoidaIndia
  2. 2.Precision Metrology Laboratory, Department of Mechanical EngineeringSant Longowal Institute of Engineering and TechnologyLongowalIndia

Personalised recommendations