research-article

Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model

Authors:

Mitchell Yuwono,

Branko G. Celler,

Steven W. SuAuthors Info & Claims

Engineering Applications of Artificial Intelligence, Volume 47, Issue C

Pages 88 - 100

https://doi.org/10.1016/j.engappai.2015.03.007

Published: 01 January 2016 Publication History

Abstract

Ball bearings are integral elements in most rotating manufacturing machineries. While detecting defective bearing is relatively straightforward, discovering the source of defect requires advanced signal processing techniques. This paper proposes an automatic bearing defect diagnosis method based on Swarm Rapid Centroid Estimation (SRCE) and Hidden Markov Model (HMM). Using the defect frequency signatures extracted with Wavelet Kurtogram and Cepstral Liftering, SRCE+HMM achieved on average the sensitivity, specificity, and error rate of 98.02%, 96.03%, and 2.65%, respectively, on the bearing fault vibration data provided by Case School of Engineering of the Case Western Reserve University (CSE) which warrants further investigation. Graphical abstractDisplay Omitted HighlightsThis paper proposes an automatic fault diagnosis algorithm for rolling bearing defects.The classification algorithm was Hidden Markov Model optimized with swarm clustering.The features were defect harmonics extracted using wavelet kurtogram and cepstral liftering.The bearing fault vibration data was obtained from Case Western Reserve University.Sensitivity and specificity of 98.02% and 96.03% were achieved on the test data.

References

[1]

Csegroups.case.edu, 2014. Case Western Reserve University Bearing Data Center Website. URL {http://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website}.

[2]

Fang, S., Zijie, W., 2007. Rolling bearing fault diagnosis based on wavelet packet and rbf neural network. In: Control Conference, 2007. CCC 2007. Chinese, 2007, pp. 451-455. http://dx.doi.org/10.1109/CHICC.2006.4346979.

[3]

M. Feldman, Hilbert transform in vibration analysis, Mech. Syst. Signal Process., 25 (2011) 735-802.

[4]

Fuglede, B., Topsoe, F., 2004. Jensen-Shannon divergence and Hilbert space embedding. In: Proceedings. International Symposium on Information Theory (ISIT), 2004, 2004, p. 30. http://dx.doi.org/10.1109/ISIT.2004.1365067.

[5]

P. Goupillaud, A. Grossmann, J. Morlet, Cycle-octave and related transforms in seismic signal analysis, Geoexploration, 23 (1984) 85-102.

[6]

Guo, Y., Dehestani, D., Li, J., Wall, J., West, S., Su, S., 2012. Intelligent outlier detection for hvac system fault detection. In: Proceedings of the 10th International Healthy Buildings Conference, Brisbane, Queensland, Australia.

[7]

Guo, Y., Wall, J., Li, J., West, S., 2013. Intelligent model based fault detection and diagnosis for hvac system using statistical machine learning methods. In: Proceedings of the ASHRAE 2013 Winter Conference, Dallas, USA.

[8]

Huang, Y.-T., Cheng, F.-T., Hung, M.-H., 2009. Developing a product quality fault detection scheme. In: IEEE International Conference on Robotics and Automation, 2009. ICRA '09, pp. 927-932. http://dx.doi.org/10.1109/ROBOT.2009.5152474.

[9]

P.G. Kulkarni, A.D. Sahasrabudhe, Application of wavelet transform for fault diagnosis of rolling element bearings, Int. J. Sci. Technol. Res., 2 (2013) 138-148.

[10]

S. Kullback, R.A. Leibler, On information and sufficiency, Ann. Math. Stat., 22 (1951) 79-86.

[11]

Y. Lei, J. Lin, Z. He, Y. Zi, Application of an improved kurtogram method for fault diagnosis of rolling element bearings, Mech. Syst. Signal Process., 25 (2011) 1738-1749.

[12]

S. Li, J. Wen, A model-based fault detection and diagnostic methodology based on pca method and wavelet transform, Energy Build., 68 (2014) 63-71.

[13]

B. Li, M.-Y. Chow, Y. Tipsuwan, J. Hung, Neural-network-based motor rolling bearing fault diagnosis, IEEE Trans. Ind. Electron., 47 (2000) 1060-1069.

[14]

Publications, B.U., 2007. Bearing Failure: Causes and Cures. URL {http://www.schaeffler.com/remotemedien/media/_shared_media/08_media_library/01_publications/barden/brochure_2/downloads_24/barden_bearing_failures_us_en.pdf}.

[15]

R.B. Randall, J. Antoni, Rolling element bearing diagnostics-a tutorial, Mech. Syst. Signal Process., 25 (2011) 485-520.

[16]

R.B. Randall, J. Hee, Cepstrum analysis, Tech. Rev. Adv. Tech. Acoust. Electr. Mech. Meas., 3-40 (1981).

[17]

Sawalhi, N., Randall, R.B., 2005. Spectral kurtosis optimization for rolling element bearings. In: Proceedings of the ISSPA Conference, Sydney, Australia, pp. 839 - 842.

[18]

C.E. Shannon, A mathematical theory of communication, SIGMOBILE Mob. Comput. Commun. Rev., 5 (2001) 3-55.

Digital Library

[19]

Slocum, A., 2008. Fundamentals of Design-Topic 10 Bearings. URL {http://web.mit.edu/2.75/fundamentals/FUNdaMENTALs%20Book%20pdf/FUNdaMENTALs%20Topic%2010.PDF}.

[20]

Valeriu Vrabie, C.S., Pierre Granjon, 2003. Spectral kurtosis: from definition to application. In: Sixth IEEE International Workshop on Nonlinear Signal and Image Processing (NSIP 2003).

[21]

van der Merwe, D.W., Engelbrecht, A.P., 2003. Data clustering using particle swarm optimization. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation, 2003, vol. 1, pp. 215-220.

[22]

V. Venkatasubramanian, R. Rengaswamy, K. Yin, S.N. Kavuri, A review of process fault detection and diagnosis, Comput. Chem. Eng., 27 (2003) 293-311.

[23]

Wall, J., Guo, Y., Li, J., West, S., 2011. A dynamic machine learning-based technique for automated fault detection in hvac systems. In: Proceedings of the ASHRAE Annual Conference, Montreal, Quebec, Canada, pp. 449-456.

[24]

Yuwono, M., Su, S.W., Moulton, B.D., Nguyen, H.T., 2012a. Fast unsupervised learning method for rapid estimation of cluster centroids. In: Proceedings of the 2012 IEEE Congress on Evolutionary Computation, pp. 889-896.

[25]

Yuwono, M., Su, S.W., Moulton, B.D., Nguyen, H.T., 2012b. Method for increasing the computation speed of an unsupervised learning approach for data clustering. In: Proceedings of the 2012 IEEE Congress on Evolutionary Computation, pp. 2957-2964.

[26]

Yuwono, M., Su, S.W., Moulton, B.D., Nguyen, H.T., 2012c. Optimization strategies for rapid centroid estimation. In: Proceedings of the 34rd Annual International Conference of the IEEE EMBS, San Diego, pp. 6212-6215.

[27]

Yuwono, M., Su, S.W., Guo, Y., Li, J., West, S., Wall, J., 2013a. Automatic feature selection using multiobjective cluster optimization for fault detection in a heating ventilation and air conditioning system. In: Proceedings of the 2013 First International Conference on Artificial Intelligence, Modelling and Simulation, AIMS '13, IEEE Computer Society, Washington, DC, USA, pp. 171-176. http://dx.doi.org/10.1109/AIMS.2013.34.

Digital Library

[28]

M. Yuwono, S. Su, B. Moulton, H. Nguyen, Data clustering using variants of rapid centroid estimation, IEEE Trans. Evol. Comput., 18 (2013) 366-377.

[29]

Yuwono, M., Su, S., Moulton, B., Nguyen, H., 2014. An algorithm for scalable clustering: ensemble rapid centroid estimation. In: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 1250-1257.

[30]

G. Zoubin, An introduction to hidden Markov model and Bayesian network, Int. J. Pattern Recognit. Artif. Intell., 15 (2001) 9-42.

Cited By

Wanner JWissuchek CWelsch GJaniesch C(2023)A Taxonomy and Archetypes of Business Analytics in Smart ManufacturingACM SIGMIS Database: the DATABASE for Advances in Information Systems10.1145/3583581.358358454:1(11-45)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3583581.3583584
Pattnaik NVemula UKumar KKumar AMajumdar AChandra MPal AGummeson JLee SGao JXing G(2022)CycleGAN Based Unsupervised Domain Adaptation for Machine Fault DiagnosisProceedings of the 20th ACM Conference on Embedded Networked Sensor Systems10.1145/3560905.3568303(973-979)Online publication date: 6-Nov-2022
https://dl.acm.org/doi/10.1145/3560905.3568303
Dekhane ADjellal ABoutebbakh FLakel RBen Ahmed MBoudhir A(2020)Cooling Fan Combined Fault Vibration Analysis Using Convolutional Neural Network ClassifierProceedings of the 3rd International Conference on Networking, Information Systems & Security10.1145/3386723.3387898(1-6)Online publication date: 31-Mar-2020
https://dl.acm.org/doi/10.1145/3386723.3387898
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cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 47, Issue C

January 2016

145 pages

ISSN:0952-1976

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Copyright © Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 January 2016

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Cited By

Wanner JWissuchek CWelsch GJaniesch C(2023)A Taxonomy and Archetypes of Business Analytics in Smart ManufacturingACM SIGMIS Database: the DATABASE for Advances in Information Systems10.1145/3583581.358358454:1(11-45)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3583581.3583584
Pattnaik NVemula UKumar KKumar AMajumdar AChandra MPal AGummeson JLee SGao JXing G(2022)CycleGAN Based Unsupervised Domain Adaptation for Machine Fault DiagnosisProceedings of the 20th ACM Conference on Embedded Networked Sensor Systems10.1145/3560905.3568303(973-979)Online publication date: 6-Nov-2022
https://dl.acm.org/doi/10.1145/3560905.3568303
Dekhane ADjellal ABoutebbakh FLakel RBen Ahmed MBoudhir A(2020)Cooling Fan Combined Fault Vibration Analysis Using Convolutional Neural Network ClassifierProceedings of the 3rd International Conference on Networking, Information Systems & Security10.1145/3386723.3387898(1-6)Online publication date: 31-Mar-2020
https://dl.acm.org/doi/10.1145/3386723.3387898
Goyal DChoudhary APabla BDhami S(2020)Support vector machines based non-contact fault diagnosis system for bearingsJournal of Intelligent Manufacturing10.1007/s10845-019-01511-x31:5(1275-1289)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s10845-019-01511-x
Liu ZZheng QJi ZZhao W(2018)Sparse Self-Represented Network MapEngineering Applications of Artificial Intelligence10.1016/j.engappai.2017.11.00268:C(121-130)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.engappai.2017.11.002
Mousavirad SEbrahimpour-Komleh H(2017)Human mental searchApplied Intelligence10.1007/s10489-017-0903-647:3(850-887)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s10489-017-0903-6

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