Roller Bearing Fault Diagnosis Method Based on Chemical Reaction Optimization and Support Vector Machine

AbstractSupport vector machine (SVM) parameter optimization has always been a demanding task in machine learning. The chemical reaction optimization (CRO) method is an established metaheuristic for the optimization problem and is adapted to optimize the SVM parameters. In this paper, a SVM parameter optimization method based on CRO (CRO-SVM) is proposed. The CRO-SVM classifier is applied to some real-world benchmark data sets, and promising results are obtained. Furthermore, the CRO-SVM is applied to diagnose the roller bearing fault by combining with the local characteristic–scale decomposition (LCD) method. The experimental results show that the combination of CRO-SVM classifiers and the LCD method obtains higher classification accuracy and lower cost time compared to the other methods.

[1]  N. R. Sakthivel,et al.  Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine , 2011, Expert Syst. Appl..

[2]  Jian Zhang,et al.  Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman–Pearson criteria and a support vector machine , 2012 .

[3]  Yujing Wang,et al.  Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine , 2012 .

[4]  Victor O. K. Li,et al.  Evolutionary artificial neural network based on Chemical Reaction Optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[5]  Victor O. K. Li,et al.  Chemical-Reaction-Inspired Metaheuristic for Optimization , 2010, IEEE Transactions on Evolutionary Computation.

[6]  Hossein Nezamabadi-pour,et al.  Facing the classification of binary problems with a GSA-SVM hybrid system , 2013, Math. Comput. Model..

[7]  Victor O. K. Li,et al.  Power-Controlled Cognitive Radio Spectrum Allocation with Chemical Reaction Optimization , 2013, IEEE Transactions on Wireless Communications.

[8]  Zhao Wei,et al.  Chemical Reaction Optimization for the Fuzzy Rule learning problem , 2012, 2012 IEEE Congress on Evolutionary Computation.

[9]  P. Konar,et al.  Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) , 2011, Appl. Soft Comput..

[10]  Kang Ryoung Park,et al.  New focus assessment method for iris recognition systems , 2008, Pattern Recognit. Lett..

[11]  Yu Yang,et al.  The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis , 2015 .

[12]  K. R. Al-Balushi,et al.  Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .

[13]  S. Sathiya Keerthi,et al.  Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms , 2002, IEEE Trans. Neural Networks.

[14]  F. Imbault,et al.  A stochastic optimization approach for parameter tuning of support vector machines , 2004, ICPR 2004.

[15]  S. Sathiya Keerthi,et al.  Evaluation of simple performance measures for tuning SVM hyperparameters , 2003, Neurocomputing.

[16]  B. Samanta,et al.  Artificial neural networks and genetic algorithms for gear fault detection , 2004 .

[17]  Sheng-De Wang,et al.  Choosing the Parameters of 2-norm Soft Margin Support Vector Machines According to the Cluster Validity , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[18]  Tung Khac Truong,et al.  Chemical reaction optimization with greedy strategy for the 0-1 knapsack problem , 2013, Appl. Soft Comput..

[19]  Fatima Ardjani,et al.  Optimization of SVM MultiClass by Particle Swarm (PSO-SVM) , 2010, 2010 2nd International Workshop on Database Technology and Applications.

[20]  Victor O. K. Li,et al.  Chemical Reaction Optimization: a tutorial , 2012, Memetic Computing.

[21]  Wei-Chang Yeh,et al.  Knowledge Discovery Employing Grid Scheme Least Squares Support Vector Machines Based on Orthogonal Design Bee Colony Algorithm , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[23]  Q. Henry Wu,et al.  Local prediction of non-linear time series using support vector regression , 2008, Pattern Recognit..

[24]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[25]  Huanhuan Chen,et al.  Evolving Least Squares Support Vector Machines for Stock Market Trend Mining , 2009, IEEE Trans. Evol. Comput..

[26]  Goutam Saha,et al.  Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier , 2010, Expert Syst. Appl..

[27]  Xiaoli Zhang,et al.  An ACO-based algorithm for parameter optimization of support vector machines , 2010, Expert Syst. Appl..

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

[29]  Wei Liu,et al.  Characteristic Analysis of High Voltage Circuit Breaker with Hydraulic Operating Mechanism , 2010 .

[30]  Anoushiravan Farshidianfar,et al.  Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine , 2007 .

[31]  Seyed Mohammad Hosseini,et al.  A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification , 2012, Comput. Math. Methods Medicine.

[32]  Jinde Zheng,et al.  A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy , 2013 .

[33]  Sheng-De Wang,et al.  Choosing the Kernel parameters of Support Vector Machines According to the Inter-cluster Distance , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[34]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[35]  Zne-Jung Lee,et al.  Parameter determination of support vector machine and feature selection using simulated annealing approach , 2008, Appl. Soft Comput..

[36]  Andries P. Engelbrecht,et al.  Image Classification using Particle Swarm Optimization , 2002, SEAL.

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

[38]  Konstantinos C. Gryllias,et al.  A Support Vector Machine approach based on physical model training for rolling element bearing fault detection in industrial environments , 2012, Eng. Appl. Artif. Intell..

[39]  Ioannis Antoniadis,et al.  Rolling element bearing fault diagnosis using wavelet packets , 2002 .

[40]  B. Venkataramani,et al.  Design of a real time automatic speech recognition system using Modified One Against All SVM classifier , 2011, Microprocess. Microsystems.

[41]  Balbir S. Dhillon,et al.  Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network , 2012 .

[42]  Salah Bouhouche,et al.  Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process , 2010 .

[43]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[44]  Kai-Tai Song,et al.  A New Information Fusion Method for Bimodal Robotic Emotion Recognition , 2008, J. Comput..

[45]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[46]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[47]  V. Sugumaran,et al.  Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine , 2008, Expert Syst. Appl..

[48]  Qiang Miao,et al.  Research on features for diagnostics of filtered analog circuits based on LS-SVM , 2011, 2011 IEEE AUTOTESTCON.

[49]  Fatima Ardjani,et al.  Optimization of SVM Multiclass by Particle Swarm (PSO-SVM) , 2010 .

[50]  Christian Igel,et al.  Evolutionary tuning of multiple SVM parameters , 2005, ESANN.

[51]  K. Loparo,et al.  HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings , 2005 .

[52]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[53]  Junsheng Cheng.  Empirical Envelope Demodulation Approach Based on Local Characteristic-scale Decomposition and Its applications to Mechanical Fault Diagnosis , 2012 .

[54]  Lei Guo,et al.  Rolling Bearing Fault Classification Based on Envelope Spectrum and Support Vector Machine , 2009 .

[55]  Gong Dun-wei.  Coal Demand Prediction Based on a Support Vector Machine Model , 2007 .

[56]  R. Casey,et al.  Advances in Pattern Recognition , 1971 .

[57]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[58]  Yang Yu.  A nonstationary signal analysis approach——the local characteristic-scale decomposition method , 2012 .