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

PurposeThis 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.MethodsGA 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.ResultsThis 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.

[1]  Wei Qiao,et al.  A GA-SVM hybrid classifier for multiclass fault identification of drivetrain gearboxes , 2014, 2014 IEEE Energy Conversion Congress and Exposition (ECCE).

[2]  Singiresu S. Rao.  MECHANICAL VIBRATIONS FIFTH EDITION IN SI UNITS , 2011 .

[3]  Tian Han,et al.  Fault diagnosis of rotating machinery based on multi-class support vector machines , 2005 .

[4]  Pengfei Li,et al.  Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification , 2014, TheScientificWorldJournal.

[5]  Jing Liu,et al.  Bearing Fault Diagnosis Based on K-L Transform and Support Vector Machine , 2007, Third International Conference on Natural Computation (ICNC 2007).

[6]  Mustafa Demetgul,et al.  Fault Diagnosis of Rolling Bearing Based on Feature Extraction and Neural Network Algorithm , 2013 .

[7]  P. S. Heyns,et al.  Fault detection in roller bearing operating at low speed and varying loads using Bayesian robust new hidden Markov model , 2018, Journal of Mechanical Science and Technology.

[8]  Jiawei Xiang,et al.  A Novel Personalized Diagnosis Methodology Using Numerical Simulation and an Intelligent Method to Detect Faults in a Shaft , 2016 .

[9]  Jong-Duk Son,et al.  Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine , 2009, Expert Syst. Appl..

[10]  Mohamed El Aroussi,et al.  Bearings prognostic using Mixture of Gaussians Hidden Markov Model and Support Vector Machine , 2013, 2013 ACS International Conference on Computer Systems and Applications (AICCSA).

[11]  D Mba,et al.  Condition monitoring of slow-speed rolling element bearings using stress waves , 2001 .

[12]  Yanhua Yu,et al.  A Novel Hyper-parameters Selection Approach for Support Vector Machines to Predict Time Series , 2012, J. Comput..

[13]  Changqing Shen,et al.  Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines , 2014 .

[14]  Yu Yang,et al.  A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .

[15]  P. S. S. Srinivasan,et al.  NEW APPROACH OF CLASSIFICATION OF ROLLING ELEMENT BEARING FAULT USING ARTIFICIAL NEURAL NETWORK , 2010 .

[16]  Martin T. Hagan,et al.  Neural network design , 1995 .

[17]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[18]  Andy C. C. Tan,et al.  Condition monitoring of low speed bearings: A comparative study of the ultrasound technique versus vibration measurements , 2008, WCE 2008.

[19]  Asoke K. Nandi,et al.  Feature generation using genetic programming with application to fault classification , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Dongik Lee,et al.  Improving signal-to-noise ratio (SNR) for inchoate fault detection based on principal component analysis (PCA) , 2014, 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014).

[21]  Hicham Medromi,et al.  Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine , 2013 .