Journal of Applied and Computational Mechanics

Bearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm

Document Type : Research Paper

Authors

1 MSc., Mechanical Engineering, Shahid Chamran University of Ahvaz

2 Assistant Professor, Mechanical Engineering Department, Shahid Chamran University of Ahvaz

Abstract

Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estimation values), which are derived from the vibration signals of test data. The results show that the performance of the proposed optimized system is better than most previous studies, even though it uses only two features. Effectiveness of the above method is illustrated using obtained bearing vibration data.

Keywords

Main Subjects

[1] Seera, M., Lim, CH. P., Nahavandi, S., Loo, CH. K., “Condition Monitoring of Induction Motors: A Review and an Application of an Ensemble of Hybrid Intelligent Models”, Expert Systems with Applications, Vol. 41, No. 10, pp. 4891-4903, 2014.
[2] Thomas, M., and Fiabilite, 2003, “Maintenance Predictive”, et Vibration de Machines, Publications ETS, Montreal, Qc, Can, p. 616.
[3] Archambault, J., Archambault, R. and Thomas, M., 2002, “Time domain descriptors for rolling-element bearing fault detection”, Proceedings of the 20th seminar on machinery vibration, CMVA, Québec, p. 10.
[4] Thomas, M., Archambault, R., and Archambault, J., 2003, “Modified Julien index as a shock detector: its application to detect rolling element bearing defect”, Proceedings of the 21th seminar on machinery vibration, CMVA, Halifax (N.S.), pp. 21.1-21.12.
[5] Gluzman, D., 2000, “The use of log scales to analyse bearing failures”, J. Vibrations, 16 (3), pp. 3-5.
[6] Randall, R. B., Antoni, J., “Rolling Element Bearing Diagnostics _ A Tutorial”, Mechanical Systems and Signal Processing, Vol. 25, No. 2, pp. 485-520, 2011.
[7] Yan, R., Gao, R. X., “Multi-Scale Enveloping Spectrogram for Vibration Analysis in Bearing Defect Diagnosis”, Tribology International, Vol. 42, No. 2, pp. 293-302, 2009.
[8] Loutas, T. H., Roulias, D., Pauly, E., Kostopoulos, V., “The Combined Use of Vibration, Acoustic Emission and Oil Debris On-line Monitoring Towards a More Effective Condition Monitoring of Rotating Machinery”, Mechanical Systems and Signal Processing, Vol. 25, No. 4, pp. 1339-1352, 2011.
[9] Hamel, M., Addali, A., Mba, D., “Investigation of the Influence of Oil Film Thickness on Helical Gear Defect Detection Using Acoustic Emission”, Applied Acoustic, Vol. 79, pp. 42-46, 2014.
[10] Safizadeh, M.S., Lakis, A.A., and Thomas, M., 2002, “Time-Frequency distributions and their Application to Machinery Fault Detection”, J. International Journal of Condition Monitoring and Diagnosis Engineering Management, 5 (2), pp. 41-56.
[11] Khemili, I., Chouchane, M., “Detection of Rolling Element Bearing Defects by Adaptive Filtering”, European Journal of Mechanics – A/Solids, Vol. 24, No. 2, pp. 293-303, 2005.
[12] Wang, H., Chen, P., “Intelligent Diagnosis Method for Rolling Element Bearing Faults Using Possibility Theory and Neural Network”, Computers & Industrial Engineering, Vol. 60, No. 4, pp. 511-518, 2011.
[13] Castejon, C., Lara, O., Garcia-Prada J. C., “Automated Diagnosis of Rolling Bearings Using MRA and Neural Networks”, Mechanical Systems and Signal Processing, Vol. 24, No. 1, pp. 289-299, 2010.
[14] Dong, G., Chen, J., “Noise Resistant Time Frequency Analysis and Application in Fault Diagnosis of Rolling Element Bearings”, Mechanical Systems and Signal Processing, Vol. 33, pp. 212-236, 2012.
[15] Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S. and Zaidi, M., 2006, “The Bees Algorithm. A novel tool for complex optimization problems”, In Proceedings of  the 2nd International Virtual Conference on Intelligent production machines and systems (IPROMS 2006), pp. 454–459 (Elsevier, Oxford), see also URL http://www.iproms.org/
[16] Samanta, B., and Al-Balushi, K.R., 2003, “Artificial neural network based fault diagnostics of rolling element bearings using time-domain features”, J. Mechanical Systems and Signal Processing, 17 (2), pp. 317-328.
[17] Abbasion, S., Rafsanjani, A., Farshidianfar, A., and Irani, N., 2007, “Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine”, J. Mechanical Systems and Signal Processing, 21, pp. 2933-2945.
[18] Sassi, S., Badri, B., and Thmoas, M., 2006, “"TALAF" and "THIKAT" as innovative time domain indicators for tracking BALL bearings”, Proceedings of the 24nd Seminar on machinery vibration, Canadian Machinery Vibration Association, éditeur M. Thomas, Montréal, Canada, pp.404-419.
[19] Loparo, K.A., Bearings vibration data set, 2003, Case Western Reserve University, available from <http://www.eecs.cwru.edu/laboratory/bearing/download.htm>.
[20] Nelwamondo, F.V., Marwala, T., and Mahola, U., 2006, “Early classifications of bearing faults using hidden markov models, Gaussian mixture models, mel-frequency cepstral coefficients and fractals”, J. International Journal of Innovative Computing, Information and Control, 2 (6), pp. 1281-1299.
[21] Marwala, T., and Vilakazi, C.B., 2007, “Computational intelligence for condition monitoring”, CoRR.
[22] Yang, J., Zhang, Y., and Zhu, Y., 2007, “Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension”, J. Mechanical Systems and Signal Processing, 21, pp. 2012-2024.
[23] Sreejith, B., Verma, A.K., and Srividya, A., 2008, “Fault diagnosis of rolling element bearing using time-domain features and neural network”, IEEE region 10 Colloquium and the ICIIS, Kharagpur, India, No. 409.
[24] Xu, Z., Xuan, J., Shi, T., Wu, B., and Hu, Y., 2009, “A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique”, J. Expert Systems with Applications, 36, pp. 11801-11807.
[25] Wang, Q., Zhang, Y., and Zhu, Y., 2010, “Neural network Compact Ensemble and Its Application”, Chinese Journal of Mechanical Engineering, 23(2).
Journal of Applied and Computational Mechanics
Volume 1, Issue 1 - Serial Number 1
March 2015
Pages 35-43