Archives of Acoustics, 45, 2, pp. 283–295, 2020
10.24425/aoa.2020.133149

Enhancement in Bearing Fault Classification Parameters Using Gaussian Mixture Models and Mel Frequency Cepstral Coefficients Features

Youcef ATMANI
Ecole Nationale Polytechnique – ENP
Algeria

Said RECHAK
Ecole Nationale Polytechnique – ENP
Algeria

Ammar MESLOUB
Ecole Militaire Polytechnique – EMP
Algeria

Larbi HEMMOUCHE
Ecole Militaire Polytechnique – EMP
Algeria

Last decades, rolling bearing faults assessment and their evolution with time have been receiving much interest due to their crucial role as part of the Conditional Based Maintenance (CBM) of rotating machinery. This paper investigates bearing faults diagnosis based on classification approach using Gaussian Mixture Model (GMM) and the Mel Frequency Cepstral Coefficients (MFCC) features. Throughout, only one criterion is defined for the evaluation of the performance during all the cycle of the classification process. This is the Average Classification Rate (ACR) obtained from the confusion matrix. In every test performed, the generated features vectors are considered along to discriminate between four fault conditions as normal bearings, bearings with inner and outer race faults and ball faults. Many configurations were tested in order to determinate the optimal values of input parameters, as the frame analysis length, the order of model, and others. The experimental application of the proposed method was based on vibration signals taken from the bearing datacenter website of Case Western Reserve University (CWRU). Results show that proposed method can reliably classify different fault conditions and have a highest classification performance under some conditions.
Keywords: bearing faults; Gaussian mixture models; Mel frequency cepstral coefficients; feature extraction; diagnosis
Full Text: PDF
Copyright © The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

References

Akaike H. (1974), A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19(6): 716–723, doi: 10.1109/tac.1974.1100705.

Aye S.A., Heyns P.S., Thiart C.J. (2015), Fault detection of slow speed bearings using an integrated approach, IFAC-Papers On Line, 48(3): 1779–1784, doi: 10.1016/j.ifacol.2015.06.344.

Barszcz T., Sawalhi N. (2012), Fault detection enhancement in rolling element bearings using the minimum entropy deconvolution, Archives of Acoustics, 37(2): 131–141, doi: 10.2478/v10168-012-0019-2.

Benkedjouh T., Chettibi T., Saadouni Y., Afroun M. (2018), Gearbox Fault Diagnosis Based on Mel-Frequency Cepstral Coefficients and Support Vector Machine, [in:] Amine A., Mouhoub M., Ait Mohamed O., Djebbar B. [Eds], Computational Intelligence and Its Applications. CIIA 2018. IFIP Advances in Information and Communication Technology, Vol. 522, pp. 220–231, Springer, doi: 10.1007/978-3-319-89743-1_20.

Bishop C.M. (2006), Pattern Recognition and Machine Learning, Springer Science + Business Media LLC.

Cerrada M. et al. (2018), A review on data-driven fault severity assessment in rolling bearings, Mechanical Systems and Signal Processing, 99, 169–196, doi: 10.1016/j.ymssp.2017.06.012.

Dempster A.P., Laird N.M., Rubin D.B. (1977), Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B (Methodological), 39(1): 1–38, www.jstor.org/stable/2984875.

Duda R.O., Hart P.E., Stork D.G. (1995), Pattern Classification and Scene Analysis. Part 1: Pattern Classification, 2nd ed., John Wiley and Sons, Inc.

Ericsson S., Grip N., Johansson E., Persson L.E., Sjöberg R., Strömberg J.O. (2004), Towards automatic detection of local bearing defects in rotating machines, Mechanical Systems and Signal Processing, 19(3): 509–535, doi: 10.1016/j.ymssp.2003.12.004.

Girondin V., Pekpe M.K., Cassar J.P., Morel H. (2013), Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis, Mechanical Systems and Signal Processing, 38(2): 499– 514, doi: 10.1016/j.ymssp.2013.03.015.

Huang X., Acero A., Hon H. (2001), Spoken language processing: a guide to theory, algorithm, and system development, Prentice Hall PTR.

Loparo K.A. (2012), Bearings Vibration Data Sets, Case Western Reserve University, http://csegroups. case.edu/bearingdatacenter/home.

Mannepalli K., Sastry P.N., Suman M. (2016), MFCC-GMM based accent recognition system for Telugu speech signals, International Journal Speech Technology, 19(1): 87–93, doi: 10.1007/s10772-015-9328-y.

McKenzie P., Alder M. (1994), Selecting the optimal number of components for a Gaussian mixture model, Proceedings of IEEE International Symposium on Information Theory, doi: 10.1109/ISIT. 1994.394626.

McLachlan G.J., Krishnan T. (2008), The EM algorithm and extensions, 2nd ed., New Jersey: John Wiley & Sons Inc.

McLachlan G.J., Peel D. (2000), Finite mixture models, New York: John Wiley & Sons.

Mesloub A., Abed-Meraim K., Belouchrani A. (2018), Ground moving target classification based on micro-Doppler signature using novel spectral information features, 2017 IEEE European Radar Conference (EURAD), pp. 255–258, doi: 10.23919/EURAD. 2017.8249195.

Narendiranath Babu T., Aravind A., Rakesh A., Jahzan M., Rama Prabha D. (2018), Application of EMD ANN and DNN for self-aligning bearing fault diagnosis, Archives of Acoustics, 43(2): 163– 175, doi: 10.24425/122364.

Narendiranath Babu T., Himamshu H.S., Prabin Kumar N., Rama Prabha D., Nishant C. (2017), Journal bearing fault detection based on Daubechies wavelet, Archives of Acoustics, 42(3): 401–414, doi: 10.1515/aoa-2017-0042.

Nelwamondo F.V., Marwala T., Mahola U. (2006), Early classifications of bearing faults using hidden Markov models, Gaussian mixture models, Mel-frequency Cepstral coefficients and fractals, International Journal of Innovative Computing, Information and Control, 2(6): 1281–1299, http://www.ijicic.org/05-077-1.pdf.

Ocak H., Loparo K.A. (2004), Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data, Mechanical Systems and Signal Processing, 18(3): 515–533, doi: 10.1016/s0888-3270(03)00052-9.

Purushotham V., Narayanan S., Suryanarayana A.N.P. (2005), Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition, NDT & E International, 38(8): 654–664, doi: 10.1016/j.ndteint.2005.04.003.

Rabiner L.R., Schafer R.W. (2011), Theory and applications of digital speech processing, Pearson Higher Education, Inc.

Randall R.B., Antoni J. (2011), Rolling elementbearing diagnostics – A tutorial, Mechanical Systems and Signal Processing, 25(2): 485–520, doi: 10.1016/j.ymssp.2010.07.017.

Shen C., Wang D., Liu Y., Kong F., Tse P.W. (2014), Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines, Smart Structures and Systems, 13(3): 000-000, http://dx.doi.org/10.12989/sss.2014.13.3.000.

Singh N., Khan R.A., Shree R. (2012), MFCC and prosodic feature extraction techniques: a comparative study, International Journal of Computer Applications, 54(1): 0975–8887, doi: 10.5120/8529-2061.

Wang J.C., Wang J.F, Weng Y.S. (2002), Chip design of MFCC extraction for speech recognition, Integration, the VLSI Journal, 32(1–2): 111–131, doi: 10.1016/S0167-9260(02)00045-7.

Yu G., Li C., Sun J. (2010), Machine fault diagnosis based on Gaussian mixture model and its application, International Journal of Advanced Manufacturing Technology, 48(1–4): 205–212, doi: 10.1007/s00170-009-2283-5.

Yu J. (2011), Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models, Mechanical Systems and Signal Processing, 25(7): 2573–2588, doi: 10.1016/j.ymssp.2011.02.006.




DOI: 10.24425/aoa.2020.133149