Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission

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