Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Journal of Nondestructive Evaluation
Published in: Journal of Nondestructive Evaluation 1/2019

01-03-2019

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

Authors: Anil Kumar, Rajesh Kumar

Published in: Journal of Nondestructive Evaluation | Issue 1/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
next article Case Study of Empirical Beam Hardening Correction Methods for Dimensional X-ray Computed Tomography Using a Dedicated Multi-material Reference Standard

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Literature
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)
2.
Dybała, J., Zimroz, R.: Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal. Appl. Acoust. 77, 195–203 (2014)
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)
4.
Nikolaou, N.G., Antoniadis, I.A.: Rolling element bearing fault diagnosis using wavelet packets. NDT&E Int. 35, 197–205 (2002)
5.
El-Thalji, I., Jantunen, E.: Fault analysis of the wear fault development in rolling bearings. Eng. Fail. Anal. 57, 470–482 (2015)
6.
Randall, R., Antoni, J.: Rolling element bearing diagnostics—a tutorial. Mech. Syst. Signal Process. 25, 485–520 (2011)
7.
SKF Group: Pole Position Bearing Self Study Guide. SKF Group, Gothenburg (2008)
8.
Koyo, : Ball Roller Bearings: Failures, Causes and Countermeasures. JTEKT Corporation, Osaka (2001)
9.
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)
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)
12.
Nélias, D., Ville, F.: Detrimental effects of debris dents on rolling contact fatigue. J. Tribol. 122, 55–64 (2000)
13.
Ashtekar, A., Sadeghi, F., Stacke, L.: Surface defects effects on bearing dynamics. J. Eng. Tribol. 224, 25–35 (2010)
14.
Bormetti, E., Donzella, G., Mazzù, A.: Surface and subsurface cracks in rolling contact fatigue of hardened components. Tribol. Trans. 45, 274–283 (2002)
15.
Sakae, C., Ohkoniori, Y., Murakami, Y.: Mode I1 Stress Intensity Factors for Spalling Cracks in Backup Roll. Internal Report, Kyushu University (1999)
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)
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)
18.
Salehizadeh, H., Saka, N.: Crack propagation in rolling line contacts. J. Tribol. 114, 690–697 (1992)
19.
Voskamp, P.: Fatigue and material response in rolling contact. ASTM Int. 1327, 152–166 (1998)
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)
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)
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)
23.
Elforjani, M., Mba, D.: Assessment of natural crack initiation and its propagation in slow speed bearings. Nondestruct. Test. Eval. 24, 261–275 (2009)
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)
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)
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)
27.
Dolenc, B., Boškoski, P., Juričić, Ð.: Distributed bearing fault diagnosis based on vibration analysis. Mech. Syst. Signal Process. 66–67, 521–532 (2016)
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)
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)
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)
31.
Harris, T.A., Kotzalas, M.N.: Rolling Bearing Analysis: Essential Concepts of Bearing Technology, 5th edn. Taylor & Francis, Florida (2006)
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)
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)
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)
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)
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)
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)
38.
Dyer, D., Stewart, R.M.: Detection of rolling element bearing damage by statistical vibration analysis. ASME J. Mech. Des. 100, 229–235 (1978)
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)
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)
41.
Randall, R.B.: Computer aided vibration spectrum trend analysis for condition monitoring. Maint. Manag. Int. 5, 161–167 (1985)
42.
Braun, S. (ed.): Mechanical Signature Analysis. Academic Press, London (1986)
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)
44.
William, P.E., Hoffman, M.W.: Identification of bearing faults using time domain zero-crossings. Mech. Syst. Signal Process. 25, 3078–3088 (2011)
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 2009
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)
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)MATH
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)
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)
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)
51.
Zhang, Y., Zuo, H., Bai, F.: Classification of fault location and performance degradation of a roller bearing. Measurement 46, 1178–1189 (2013)
52.
Barkov, A., Barkova, N., Mitchell, J.: Condition assessment and life prediction of rolling element bearings. Sound Vib. 28, 10–17 (1995)
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)
54.
Yang, W., Court, R.: Experimental study on the optimum time for conducting bearing maintenance. Measurement 46, 2781–2791 (2013)
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)
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)
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)
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 2011
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)
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)
61.
Harris, C.M., Piersol, A.G.: Harris’ Shock and Vibration Handbook. McGraw-Hill, New York (2002)
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. 209–243
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)
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)
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)
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)
67.
Sheen, Y.-T.: 3D spectrum analysis for vibration signals by wavelet based demodulation. Mech. Syst. Signal Process. 20, 843–853 (2006)
68.
Randall, R.B.: Noise and vibration data analysis. In: Crocker, M. (ed.) Handbook of Noise and Vibration Control. Wiley, New Jersey (2007)
69.
Carcaterra, A., Sestieri, A.: Complex envelope displacement analysis: a quasi-static approach to vibrations. J. Sound Vib. 201, 205–233 (1997)
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)
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)
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)MATH
73.
Antoni, J.: Fast computation of the kurtogram for the detection of transient faults. Mech. Syst. Signal Process. 21, 108–124 (2007)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
84.
Wigner, E.P.: On the quantum correction for thermodynamic equilibrium. Phys. Rev. 40, 749–759 (1932)MATH
85.
Ville, J.: Theorie et application de la notion de signal analytique. Cables et Trans. 2, 61–74 (1948)
86.
Meng, Q., Qu, L.: Rotating machinery fault diagnosis using Wigner distribution. Mech. Syst. Signal Process. 5, 155–166 (1991)
87.
Pan, M.-C., Brussel, H.V., Sas, P., Verbeure, B.: Fault diagnosis of joint backlash. J. Vib. Acoust. 120, 13–24 (1998)
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)
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)
90.
Grossman, A., Morlet, J.: Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15, 723–736 (1984)MathSciNetMATH
91.
Polikar, R.: Multiresolution analysis and the continuous wavelet transform, The wavelet tutorial, Part III, Ames, Lowa, USA (1996)
92.
Yang, W.-X.: A natural way for improving the accuracy of the continuous wavelet transforms. J. Sound Vib. 306, 928–939 (2007)MathSciNet
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)
94.
Zheng, H., Li, Z., Chen, X.: Gear fault diagnosis based on continuous wavelet transform. Mech. Syst. Signal Process. 16, 447–457 (2002)
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)
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)
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)
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)
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)
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)
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)
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 2013
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. 183–186
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)
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)
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)
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)
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)
109.
Gokgoz, E., Subasi, A.: Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed. Signal Process. Control 18, 138–144 (2015)
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)
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)
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)
113.
Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, San Diego (1999)MATH
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)
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)
116.
Lou, X., Loparo, K.A.: Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech. Syst. Signal Process. 18, 1077–1095 (2004)
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)
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)
119.
Hao, R., Chu, F.: Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings. J. Sound Vib. 320, 1164–1177 (2009)
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)
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)
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)
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)
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)
125.
Fan, X., Zuo, M.J.: Gearbox fault detection using Hilbert and wavelet packet transform. Mech. Syst. Signal Process. 20, 966–982 (2006)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)MathSciNetMATH
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)
137.
Keshtan, M.N., Khajavi, M.N.: Bearings fault diagnosis using vibrational signal analysis by EMD method. Res. Nondestr. Eval. 27, 155–174 (2016)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
150.
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)
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)
153.
Cao, H., Fan, F., Zhou, K., He, Z.: Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement 82, 439–449 (2016)
154.
Liu, B., Ling, S.F., Gribonval, R.: Bearing failure using matching pursuit. NDT&E Int. 35, 255–262 (2002)
155.
Yang, H., Mathew, J., Ma, L.: Fault diagnosis of rolling element bearings using basis pursuit. Mech. Syst. Signal Process. 19, 341–356 (2005)
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)
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)
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)
159.
Lv, J., Yu, J.: Average combination difference morphological filters for fault feature extraction of bearing. Mech. Syst. Signal Process. 100, 827–845 (2018)
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)
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)
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)
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)
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)
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)
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)
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)
168.
Jena, D.P., Panigrahi, S.N.: Precise measurement of defect width in tapered roller bearing using vibration signal. Measurement 55, 39–50 (2014)
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)
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)
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)
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)
173.
174.
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)
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)
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)
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)MathSciNet
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)
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)
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)
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)
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)
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)
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)
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 2008
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
206.
Li, Y., Kurfess, T.R., Liang, S.Y.: Stochastic prognostics for rolling element bearings. Mech. Syst. Signal Process. 14, 747–762 (2000)
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)MATH
208.
Li, R., Sopon, P., He, D.: Fault features extraction for bearing prognostics. J. Intell. Manuf. 23, 313–321 (2012)
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)
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)
211.
Dong, S., Luo, T.: Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement 46, 3143–3152 (2013)
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)
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)
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)
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)
Metadata
Title
Role of Signal Processing, Modeling and Decision Making in the Diagnosis of Rolling Element Bearing Defect: A Review
Authors
Anil Kumar
Rajesh Kumar
Publication date
01-03-2019
Publisher
Springer US
Published in
Journal of Nondestructive Evaluation / Issue 1/2019
Print ISSN: 0195-9298
Electronic ISSN: 1573-4862
DOI
https://doi.org/10.1007/s10921-018-0543-8

Other articles of this Issue 1/2019

Journal of Nondestructive Evaluation 1/2019 Go to the issue

OriginalPaper

Water Migration in One-Side Heated Concrete: 4D In-Situ CT Monitoring of the Moisture-Clog-Effect

OriginalPaper

Experimental Investigations to Assess Surface Contact Fatigue Faults in the Rolling Contact Bearings by Enhancement of Sound and Vibration Signals

OriginalPaper

Estimating Fatigue Damage of Austenitic Steel by Combining the Ultrasonic with Eddy Current Monitoring

OriginalPaper

Practical Aspects of Nondestructive Induction Field Testing in Determining the Depth of Steel and Reinforced Concrete Foundations

OriginalPaper

Towards Automated Tracking of Initiation and Propagation of Cracks in Aluminium Alloy Coupons Using Thermoelastic Stress Analysis

OriginalPaper

Acoustic Emission Characterization of Hybrid Fiber Reinforced Cellular Concrete Under Direct Shear Loads

Premium Partners

    Image Credits