Application of wavelet analysis and its interpretation in rotating machines monitoring and fault diagnosis. A review

 
 
 
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  • Abstract


    Vibration analysis is a key element of predictive maintenance of rotating machines. Several signal analysis methods are used to obtain useful information from vibration signatures. This signal highlights the changes in time domain (root mean square), in the frequency spectrum (Fourier Transform) and in the time-frequency (Short Time Fourier Transform and Wavelet Transform). Currently, the most of these methods use spectral analysis based on Fourier Transform (FT). However, these methods exhibit some limitations: it is the case of non-stationary signals.


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      [1] K. Vernekar, “Fault Detection of Gear Using Spectrum and Cepstrum Analysis,” Proc. Indian Natl. Sci. Acad., vol. 81, no. 4, pp. 1177–1182, 2015.https://doi.org/10.16943/ptinsa/2015/v81i4/48270.

      [2] F. Khathyri, B. Elkihel, A. al motalibBerrehili, F. Delaunois, and I. Manssouri, “Detection and localization of defects in composite material using the non-destructive testing methods: Ultrasonic and Infrared Thermography,” Int. J. Emerg. Trends Eng. Dev. Issue, vol. 7, no. 2, pp. 36–45, 2017.

      [3] R. K. Mobley and R. K. Mobley, “2 – Financial Implications and Cost Justification,” in an Introduction to Predictive Maintenance, 2002, pp. 23–42.

      [4] D. Dyer, D. Burch, R. Cheeser, C. Bethea, L. Bowman, and R. Moshage, “Vibration Monitoring for Predictive Maintenance in Central Energy Plants,” no. September, pp. 1–70, 1993.

      [5] R. K. Mobley and R. K. Mobley, “7 – Vibration Monitoring and Analysis,” in an Introduction to Predictive Maintenance, 2002, pp. 114–171.

      [6] R. K. Mobley and R. K. Mobley, “3 – Role of Maintenance Organization,” in an Introduction to Predictive Maintenance, 2002, pp. 43–59.

      [7] N. BAYDAR and A. BALL, “Detection of Gear Failures Via Vibration and Acoustic Signals Using Wavelet Transform,” Mech. Syst. Signal Process., vol. 17, no. 4, pp. 787–804, 2003.https://doi.org/10.1006/mssp.2001.1435.

      [8] S. Natarajan, “WAVELET ANALYSIS in FAULT DIAGNOSIS of SPUR,” vol. 2, no. 7, pp. 8–19, 2015.

      [9] S. Mallat and W. L. Hwang, “Singularity detection and processing with wavelets,” IEEE Trans. Inf. Theory, vol. 38, no. 2, pp. 617–643, 1992.https://doi.org/10.1109/18.119727.

      [10] H. Bendjama, S. Bouhouche, and M. S. Boucherit, “Application of Wavelet Transform for Fault Diagnosis in Rotating Machinery,” Int. J. Mach. Learn. Comput, vol. 2, no. 1, pp. 82–87, 2012.https://doi.org/10.7763/IJMLC.2012.V2.93.

      [11] L. Li, L. Qu, and X. Liao, “Haar wavelet for machine fault diagnosis,” Mech. Syst. Signal Process, vol. 21, no. 4, pp. 1773–1786, 2007.https://doi.org/10.1016/j.ymssp.2006.07.006.

      [12] V. DhanushAbhijit, V. Sugumaran, and K. I. Ramachandran, “Fault Diagnosis of Bearings using Vibration Signals and Wavelets,” Indian J. Sci. Technol., vol. 9, no. 33, 2016.https://doi.org/10.17485/ijst/2016/v9i33/101325.

      [13] I. Daubechies, “mallt,” Commun. Pure Appl. Math., vol. 41, no. 7, pp. 909–996, Oct. 1988.https://doi.org/10.1002/cpa.3160410705.

      [14] Z. K. Peng and F. L. Chu, “Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography,” Mech. Syst. Signal Process., vol. 18, no. 2, pp. 199–221, Mar. 2004.https://doi.org/10.1016/S0888-3270(03)00075-X.

      [15] C.-L. Liu, “A Tutorial of the Wavelet Transform Chapter 1 Overview.” 2010.

      [16] Y. Qin, Y. Mao, and B. Tang, “Vibration signal component separation by iteratively using basis pursuit and its application in mechanical fault detection,” J. Sound Vib, vol. 332, no. 20, pp. 5217–5235, 2013.https://doi.org/10.1016/j.jsv.2013.04.021.

      [17] H. ZHENG, Z. LI, and X. CHEN, “GEAR FAULT DIAGNOSIS BASED ON CONTINUOUS WAVELET TRANSFORM,” Mech. Syst. Signal Process., vol. 16, no. 2–3, pp. 447–457, Mar. 2002.

      [18] R. Moshage et al., “Vibration Monitoring for Predictive Maintenance in Central Energy Plants.” 1993.

      [19] A. Doncescu, “Ondelettes: Théorie ET Applications.”

      [20] X. Jiang and S. Mahadevan, “Wavelet spectrum analysis approach to model validation of dynamic systems,” Mech. Syst. Signal Process, vol. 25, no. 2, pp. 575–590, Feb. 2011.https://doi.org/10.1016/j.ymssp.2010.05.012.

      [21] M. Gómez, C. Castejón, and J. García-Prada, “Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors,” Algorithms, vol. 9, no. 1, p. 19, Mar. 2016.https://doi.org/10.3390/a9010019.

      [22] S. Delvecchio Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology. 2012.

      [23] Henrique S.MALVAR, “Lapped Transforms for Efficient Transform/Subband Coding,” IEEE Trans. Acoust. Signal Process. vol. 38, pp. 969–978, 1990.https://doi.org/10.1109/29.56057.

      [24] S. Mallat, a Wavelet Tour of Signal Processing. 2008.

      [25] J. LIN and M. J. ZUO, “GEARBOX FAULT DIAGNOSIS USING ADAPTIVE WAVELET FILTER,” Mech. Syst. Signal Process., vol. 17, no. 6, pp. 1259–1269, Nov. 2003.https://doi.org/10.1006/mssp.2002.1507.

      [26] L. M. Hee, M. S. Leong, and K. H. Hui, “Analysis of Residual Wavelet Scalogram for Machinery Fault Diagnosis,” Adv. Mater. Res., vol. 845, pp. 113–117, Dec. 2013.

      [27] J. Olkkonen, DISCRETE WAVELET TRANSFORMS - THEORY AND APPLICATIONS. 2011.

      [28] S. G. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Trans. PATTERN Anal. Mach. Intell. VOL. II, vol. 11, no. 7, pp. 674–693, 1989.

      [29] P. G. Kulkarni and A. D. Sahasrabudhe, “Application Of Wavelet Transform For Fault Diagnosisof Rolling Element Bearings,” Int. J. Sci. Technol. reseach, vol. 2, no. 4, pp. 138–148, 2013.

      [30] A. Raad, S. Kass, and J. Antoni, “CFA / VISHNO 2016 Etude de la décompositionen mode empiriquecombinéedans le cas des signaux de vibrations de roulements et d’ engrenagesdansdiverses conditions,” pp. 1839–1844, 2016.

      [31] W. Wang and P. McFadden, “Application of the wavelet transform to gearbox vibration analysis,” in Structural dynamics and vibrations - 16th Annual energy-sources technology conference, Structural dynamics and vibrations -1993-, 1993, pp. 15–20.

      [32] W. J. Wang, “Wavelet transform in vibration analysis for mechanical fault diagnosis,” 1996.

      [33] C. K. Sung, H. M. Tai, and C. W. Chen, “Locating defects of a gear system by the technique of wavelet transform,” Mech. Mach. Theory, vol. 35, no. 8, pp. 1169–1182, 2000.https://doi.org/10.1016/S0094-114X(99)00045-2.

      [34] H. Zheng, Z. Li, and X. Chen, “GEAR FAULT DIAGNOSIS BASED ON CONTINUOUS WAVELET TRANSFORM,” 2002.

      [35] S. S. C. S. M.Amarnath, “Application of Wavelet transform to gearbox fault diagnosis.pdf.” 2005.

      [36] G. Marie, R. Razafindrazato, O. Riou, and J. F. Durastanti, “Détection de défaut sur motoréducteur à engrenageenutilisant la transforméeenondelettes,” Rev. Sci. Maint., 2012.

      [37] Z. Mentouri, S. Ziani, and S. Taleb, “Wavelet Transform and Envelope Detection for Gear Fault Diagnosis. A Comparative Study,” Electr. Eng., no. 2, 2013.

      [38] K. Vernekar, H. Kumar, and K. V. Gangadharan, “Gear Fault Detection Using Vibration Analysis and Continuous Wavelet Transform,” Procedia Mater. Sci., vol. 5, pp. 1846–1852, 2014.https://doi.org/10.1016/j.mspro.2014.07.492.

      [39] B.Abouelanouar, “A comparative experimental study of different methods in detection and monitoring bearing defects,” Int. J. Adv. Sci. Tech. Res., vol. 1, no. 7, pp. 409–423, 2017.

      [40] C. T. Yiakopoulos and I. A. Antoniadis, “Wavelet based demodulation of vibration signals generated by defects in rolling element bearings,” Shock Vib., vol. 9, no. 6, pp. 293–306, 2001.https://doi.org/10.1155/2002/592436.

      [41] N. G. Nikolaou and I. A. Antoniadis, “Rolling element bearing fault diagnosis using wavelet packets,” NDT E Int., vol. 35, no. 3, pp. 197–205, 2001.https://doi.org/10.1016/S0963-8695(01)00044-5.

      [42] Y. Sun Q.; Tan, “Singularityanalysis Using Continuous Wavelet Transform for Bearing Fault Diagnosis,” Mech. Syst. Signal Process., vol. 16, pp. 1025–1041, 2002.https://doi.org/10.1006/mssp.2002.1474.

      [43] X. Lou and K. A. Loparo, “Bearing fault diagnosis based on wavelet transform and fuzzy inference,” Mech. Syst. Signal Process. vol. 18, no. 5, pp. 1077–1095, 2004.https://doi.org/10.1016/S0888-3270(03)00077-3.

      [44] R. X. Gao and R. Yan, “Non-stationary signal processing for bearing health monitoring,” Int. J. Manuf. Res., vol. 1, no. 1, p. 18, 2006.https://doi.org/10.1504/IJMR.2006.010701.

      [45] A. Djebala, N. Ouelaa, and N. H. S. Guenfoud, “Application de la TransforméeenOndelettesDiscrètedans la Détection des Défauts de Roulements,” 2009.

      [46] I. Tsiafis, K.-D. Bouzakis, A. Kaplanis, A. Karamanidis, and T. Xenos, “Fault Diagnosis of Roller Bearings Using the Wavelet Transform,” Rom. Rev. Precis. Mech. Opt. Mechatronics, no. 39, pp. 21–24, 2011.

      [47] Q. Liu, F. Chen, Z. Zhou, and Q. Wei, “Fault diagnosis of rolling bearing based on wavelet package transform and ensemble empirical mode decomposition,” Adv. Mech. Eng., vol. 2013, 2012.

      [48] P. Shakya,a K. Darpe, and M. S. Kulkarni, “Vibration-based fault diagnosis in rolling element bearings : ranking of various time , frequency and time-frequency domain data-based damage identification parameters,” Int. J. Cond. Monit., vol. 3, no. 2, pp. 1–10, 2013.

      [49] R. Himanshu, “Importance of Vibration Parameters in Fault Diagnosis and Condition Monitoring of Bearing and Analysis of Wavelets,” Int. J. Adv. Eng. Reseach Sci., vol. 3, no. 6, pp. 65–72, 2016.

      [50] M. PRICOP, “Vibration Analysis for Detection and Localization the Faults of Rotating Machinery Using Wavelet Techiniques,” Sci. Bull. Nav. Acad., vol. 19, no. 1, pp. 264–272, 2016.

      [51] B.Bouzouane, A.Miloudi, N. Hamzaoui, and A. Benchaala, “Détection de défauts de machines tournantes per la méthode des ondelettes,” in CongrèsFrançais de Mécanique, At Nice, France, 2003.

      [52] L. H. Cherif, S. M. Debbal, and F. Bereksi-Reguig, “Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform,” Expert Syst. Appl., vol. 37, no. 2, pp. 913–918, Mar. 2010.https://doi.org/10.1016/j.eswa.2009.09.036.

      [53] P. W. Tse and W. X. Yang, “The Practical Use of Wavelet Transforms and Their Limitations in Machine Fault Diagnosis ,” Proc. Int. Symp. Mach. Cond. Monit. Diagnosis JSME Annu. Meet vol. 2002, pp. 9–16, and 2002.

      [54] R. Yan and R. X. GAO, “Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis,” Tribol. Int., vol. 42, no. 2, pp. 293–302, Feb. 2009.https://doi.org/10.1016/j.triboint.2008.06.013.

      [55] S. Wang, Z. Zhu, Y. He, and W. Huang, “Adaptive Parameter Identification Based on Morlet Wavelet and Application in Gearbox Fault Feature Detection,” EURASIP J. Adv. Signal Process., vol. 2010, no. 1, p. 842879, 2010.https://doi.org/10.1155/2010/842879.

      [56] G. Y. Luo, D. Osypiw, and M. Irle, “On-Line Vibration Analysis with Fast Continuous Wavelet Algorithm for Condition Monitoring of Bearing,” Modal Anal., vol. 9, no. 8, pp. 931–947, Aug. 2003.https://doi.org/10.1177/10775463030098002.

      [57] R. Yan, R. X. GAO, and X. Chen, “Wavelets for fault diagnosis of rotary machines: A review with applications,” Signal Processing, vol. 96, pp. 1–15, Mar. 2014.https://doi.org/10.1016/j.sigpro.2013.04.015.

      [58] D. Wenliao, Y. Jin, L. Yanming, and L. Chengliang, “Adaptive wavelet filtering for bearing monitoring based on block bootstrapping and white noise test,” Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., vol. 226, no. 9, pp. 2345–2360, Sep. 2012.https://doi.org/10.1177/0954406211430780.

      [59] Z. Li, X. Yan, C. Yuan, Z. Peng, and L. Li, “Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method,” Mech. Syst. Signal Process., vol. 25, no. 7, pp. 2589–2607, Oct. 2011.https://doi.org/10.1016/j.ymssp.2011.02.017.

      [60] P. Li, F. Kong, Q. He, and Y. Liu, “Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis,” Measurement, vol. 46, no. 1, pp. 497–505, Jan. 2013.https://doi.org/10.1016/j.measurement.2012.08.007.

      [61] D. Wang, Q. Miao, and R. Kang, “Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition,” J. Sound Vib, vol. 324, no. 3–5, pp. 1141–1157, Jul. 2009.https://doi.org/10.1016/j.jsv.2009.02.013.

      [62] J.-D. Wu and C.-C. Hsu, “Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy–logic inference,” Expert Syst. Appl., vol. 36, no. 2, pp. 3785–3794, Mar. 2009.https://doi.org/10.1016/j.eswa.2008.02.026.

      [63] R. X. Gao and R. Yan, “Non-stationary signal processing for bearing health monitoring,” Int. J. Manuf. Res., vol. 1, no. 1, p. 18, 2006.https://doi.org/10.1504/IJMR.2006.010701.

      [64] P. Boškoski and Đ. Juričić, “Fault detection of mechanical drives under variable operating conditions based on wavelet packet Rényi entropy signatures,” Mech. Syst. Signal Process., vol. 31, pp. 369–381, Aug. 2012.https://doi.org/10.1016/j.ymssp.2012.04.016.

      [65] Y. Feng and F. S. Schlindwein, “Normalized wavelet packets quantifiers for condition monitoring,” Mech. Syst. Signal Process., vol. 23, no. 3, pp. 712–723, Apr. 2009.https://doi.org/10.1016/j.ymssp.2008.07.002.

      [66] B. Liu, “Selection of wavelet packet basis for rotating machinery fault diagnosis,” J. Sound Vib., vol. 284, no. 3–5, pp. 567–582, Jun. 2005.https://doi.org/10.1016/j.jsv.2004.06.047.

      [67] Y. Yang, Y. He, J. Cheng, and D. Yu, “A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach,” Measurement, vol. 42, no. 4, pp. 542–551, May 2009.https://doi.org/10.1016/j.measurement.2008.09.011.

      [68] G.-M. Xian and B.-Q. Zeng, “An intelligent fault diagnosis method based on wavelet packer analysis and hybrid support vector machines,” Expert Syst. Appl., vol. 36, no. 10, pp. 12131–12136, Dec. 2009.https://doi.org/10.1016/j.eswa.2009.03.063.

      [69] Z.-L. Gaing, “Wavelet-Based Neural Network for Power Disturbance Recognition and Classification,” IEEE Trans. Power Deliv, vol. 19, no. 4, pp. 1560–1568, Oct. 2004.https://doi.org/10.1109/TPWRD.2004.835281.

      [70] H. Bendjama, D. Idiou, K. Gherfi, and Y. Laib, “Selection of Wavelet Decomposition Levels for Vibration Monitoring of Rotating Machinery,” ADVCOMP 2015 Ninth Int. Conf. Adv. Eng. Comput. Appl. Sci., no. c, pp. 96–100, 2015.

      [71] T. Ishak, “Extraction d’indicateursrobustes pour le diagnostic des défautsmécaniques : Comparaison de L’EMD ET des ondelettes (WT),” UNIVERSITE FERHAT ABBAS - SETIF UFAS (ALGERIE), 2012.

      [72] A. M. Gaouda, M. M. A. Salama, M. R. Sultan, and A. Y. Chikhani, “Power quality detection and classification using wavelet-multiresolution signal decomposition,” IEEE Trans. Power Deliv., vol. 14, no. 4, pp. 1469–1476, 1999.https://doi.org/10.1109/61.796242.


 

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Article ID: 21720
 
DOI: 10.14419/ijet.v7i4.21720




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