Abstract
Purpose
This work under consideration makes use of support vector machines (SVM) for regression and genetic algorithms (GA) which may be referred to as SVMGA, to classify faults in low-speed bearings over a specified speed range, with sinusoidal loads applied to the bearing along the radial and axial directions.
Methods
GA is used as a heuristic tool in finding profound solution to the difficult problem of solving the highly non-linear situation through the application of the principles of evolution by optimizing the statistical features selected for the SVM for regression training solution. It is used to determine the training parameters of SVM for regression which can optimize the model and hence without the forehand knowledge of the probabilistic distribution can form new features from the original dataset. Using SVM for regression, the non-linear regression and fault recognition are achieved. Classification is performed for three classes. In this work, the GA is used to first optimize the statistical features for the best performance before they are used to train the SVM for regression. Experimental studies using acoustic emission caused by bearing faults showed that SVMGA with a Gaussian kernel function better achieves classification on the bearings operated at low speed, regardless of the load type and, under different fault conditions, compared to the exponential kernel function and the other many kernel functions which also can be used for the same conditions.
Results
This study accomplished the effective classification of different bearing fault patterns especially at low speeds and at varying load conditions using support vector machines (SVM) for regression and genetic algorithms (GA) referred to as SVMGA.
Similar content being viewed by others
References
Hamadache M, Lee D (2014) Improving signal-to-noise ratio (SNR) for inchoate fault detection based on principal component analysis (PCA). In: 14th international conference on control, automation and systems (ICCAS 2014), Oct. 22–25, in KINTEX, Gyeonggi-do, Korea
Jamaludin N, Mba D, Bannister RH (2001) Condition monitoring of slow-speed rolling element bearing using stress waves. Proc Inst Mech Eng Part E J Process Mech Eng 215(4):245–271
Achmad W, Eric YK, Jong-Duk S, Bo-Suk Y, Andy CCT, Dong-Sik G, Byeong-Keun C, Joseph M (2009) Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine, Elsevier. Expert Syst Appl 36:7252–7261
Changqing S, Dong W, Yongbin L, Fanrang K, Peter WT (2014) Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines. Smart Struct Syst 13(3):453–471
Yu Y, Dejie Y, Junsheng C (2006) A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Elsevier (ScienceDirect) Meas 40:943–950
Yang B, Han T, Hwang W (2005) Fault diagnosis of rotating machinery based on multi-class support vector machine. J Mech Sci Technol (KSME Int J) 19(3):846–859
Kan X (2011) ECE 480 team 4, support vector machine, concept and MATLAB build
Li X, Chen W (2014) Rolling bearing fault diagnosis based on physical model and one-class support vector machine, Hindawi Publishing Corporation. ISRN Mechanical Engineering 2014:160281
Sloukia FE, Bouarfa R, Medromi H, Wahbi M (2013) Bearings prognostic using mixture of Gaussian hidden Markov model and support vector machine. Int J Netw Secur Appl (IJNSA) 5(3):85–97
Shen C, Wang D, Liu Y, Kong F, Tse PW (2014) Recognition of rolling bearing fault pattern and sizes based on two-layer support vector regression machines. Smart Struct Syst 13(3):453–471
Muhammet U, Mustafa D, Mustafa O, Haluk K (2013) Fault diagnosis of rolling bearing based on feature extraction and neural network algorithm, recent advances in telecommunications, signals and systems. ISBN: 978-1-61804-169-2. www.wseas.us/e-library/confrences/2013/lemesos. Accessed 2017
Hariharan V, Srinivasan PSS (2009) New approach of classification of rolling element bearing fault using artificial neural network. J Mech Eng ME 40(2):119–130
Hagan MT, Demuth HB, Beale M (1997) Neural network design. Thomson, Boston
Shuang L, Fujin Y, Jing L (2007) Bearing fault diagnosis based on k-l transform and support vector machine. In: Third international conference on natural computation (ICNC 2007) 0-7695-2875-9/07
Yu Y, Song M, Song J (2012) A novel hyper-parameters selection approach for support vector machines to predict time series. J Comput 7(12):2921–2930
Rao SS (2011) Mechanical vibrations. In: Fifth Edition in SI Units, University of Miami. Prentice Hall. Published in 2011 by Pearson Education South Asia Pte Ltd. 23/25 First Lok Yang Road, Jurong Singapore 629733
Gun SR (1998) Support vector machines for classification and regression, technical report, Faculty of Engineering, Science and Mathematics, School of Electronics and Computer Science, University of Southampton
SmolHamadache AJ, Lee MD (2014) Improving signal-to-noise ratio (SNR) for inchoate fault detection based on principal component analysis (PCA). In: 14th international conference on control, automation and systems (ICCAS 2014). KINTEX, Gyeonggi-do, Korea, pp 561–566
Li P, Jiang Y, Xiang J (2014) Experimental investigation for fault diagnosis based on a hybrid approach using wavelet packet and support vector classification. Sci World J. https://doi.org/10.1155/2014/145807
Xiang J, Zhong Y (2016) A novel personalized diagnosis methodology using numerical simulation and an intelligent method to detect faults in a shaft. Appl Sci 6:414
Kim Y, Tan ACC, Mathew J, Yang B (2006) Condition monitoring of low speed bearings: a comparative study of the ultrasound technique versus vibration measurements. WCEAM Paper 029:1–10
Guo H, Jack LB, Nandi A (2005) Feature generation using genetic programming with application to fault classification. IEEE Trans on Syst Man Cybern Part B Cybern 35(1):89–99
Lu D, Qiao W (2014) A GA-SVM hybrid classifier for multiclass fault identification of drivetrain greaboxes. In: Energy conversion congress and exposition (ECCE), IEEE pp 3894–3900
Tong Q, Han B, Lin Y, Zhang W, Cao J, Zhang X (2017) A fault feature detection approach for fault diagnosis of rolling element bearings based on redundant second generation wavelet packet transform and local characteristic-scale decomposition. J Vib Eng Technol 5(1):101–110
Fatima S, Mohanty AR, Kazmi HF (2016) Fault classification and detection in a rotor bearing rig. J Vib Eng Technol 4(6):491–498
Omoregbee HO, Heyns PS (2018) Fault detection in roller bearing operating at low speed and varying loads using Bayesian Robust New Hidden Markov Model. J Mech Sci Technol 32(9):4025–4036. https://doi.org/10.1007/s12206-018-0802-8
Acknowledgements
We profusely thank the staff of the C-AIM laboratory of the University of Pretoria, for their co-operation and help in seeing that a worthwhile experiment was conducted with successful recording of the acoustic signal which was used for analysis purpose in this work. We also thank our supervisor and colleagues for their support in the course of carrying out the experiment and also other well wishers who rendered their help to successfully carry out the experiment.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Omoregbee, H.O., Heyns, P.S. Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission. J. Vib. Eng. Technol. 7, 455–464 (2019). https://doi.org/10.1007/s42417-019-00143-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42417-019-00143-y