How to cite this paper
Thelaidjia, T., Moussaoui, A & Chenikher, S. (2016). Feature extraction and optimized support vector machine for severity fault diagnosis in ball bearing.Engineering Solid Mechanics, 4(4), 167-176.
Refrences
Abe, S. (2005). Support vector machines for pattern classification (Vol. 53). London: Springer.
Angeline, P. J. (1998, March). Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. In International Conference on Evolutionary Programming (pp. 601-610). Springer Berlin Heidelberg.
Bell, R. N., McWilliams, D. W., O'donnell, P., Singh, C., & Wells, S. J. (1985). Report of large motor reliability survey of industrial and commercial installations. I. IEEE Transactions on Industry applications, 21(4), 853-864.
Case Western Reserve University. (Last accessed in 2014), Bearing data center.
Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on Evolutionary Computation, 6(1), 58-73.
Chen, F., Tang, B., Song, T., & Li, L. (2014). Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization. Measurement, 47, 576-590.
Chebil, J., Noel, G., Mesbah, M., & Deriche, M. (2009). Wavelet decomposition for the detection and diagnosis of faults in rolling element bearings. Jordan Journal of Mechanical and Industrial Engineering, 3(4), 260-267.
Djebala, A., Babouri, M. K., & Ouelaa, N. (2015). Rolling bearing fault detection using a hybrid method based on empirical mode decomposition and optimized wavelet multi-resolution analysis. The International Journal of Advanced Manufacturing Technology, 79(9-12), 2093-2105.
Dong, S., & Luo, T. (2013). Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement, 46(9), 3143-3152.
He, S., Wu, Q. H., Wen, J. Y., Saunders, J. R., & Paton, R. C. (2004). A particle swarm optimizer with passive congregation. Biosystems, 78(1), 135-147.
Kankar, P. K., Sharma, S. C., & Harsha, S. P. (2011). Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing, 11(2), 2300-2312.
Kankar, P. K., Sharma, S. C., & Harsha, S. P. (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 38(3), 1876-1886.
Lei, Y., He, Z., Zi, Y., & Hu, Q. (2007). Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing, 21(5), 2280-2294.
Malhi, A., & Gao, R. X. (2004). PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, 53(6), 1517-1525.
Pandya, D. H., Upadhyay, S. H., & Harsha, S. P. (2014). Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft Computing, 18(2), 255-266.
Prieto, M. D., Cirrincione, G., Espinosa, A. G., Ortega, J. A., & Henao, H. (2013). Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Transactions on Industrial Electronics, 60(8), 3398-3407.
Saidi, L., Ali, J. B., & Fnaiech, F. (2015). Application of higher order spectral features and support vector machines for bearing faults classification. ISA transactions, 54, 193-206.
Sharma, A., Amarnath, M., & Kankar, P. K. (2016). Feature extraction and fault severity classification in ball bearings. Journal of Vibration and Control, 22(1), 176-192.
Stepanic, P., Latinovic, I. V., & Djurovic, Z. (2009). A new approach to detection of defects in rolling element bearings based on statistical pattern recognition. The International Journal of Advanced Manufacturing Technology, 45(1-2), 91-100.
Thelaidjia, T., & Chenikher, S. (2013, December). A New approach of preprocessing with SVM optimization based on PSO for bearing fault diagnosis. In Hybrid Intelligent Systems (HIS), 2013 13th International Conference on (pp. 319-324). IEEE.
Vapnik, V. (2013). The nature of statistical learning theory. Springer Science & Business Media.
Vapnik, V.N. (1998). Statistical Learning Theory, Springer, New York.
Wang, L. (Ed.). (2005). Support vector machines: theory and applications (Vol. 177). Springer Science & Business Media.
Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2), 151-157.
Zhang, Z., Wang, Y., & Wang, K. (2013). Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks. The International Journal of Advanced Manufacturing Technology, 68(1-4), 763-773.
Zhi-qiang, J., Hang-guang, F., & Ling-jun, L. I. (2005). Support Vector Machine for mechanical faults classification. Journal of Zhejiang University Science A, 6(5), 433-439.
Zhou, W., Habetler, T. G., & Harley, R. G. (2007, September). Bearing condition monitoring methods for electric machines: A general review. In Diagnostics for Electric Machines, Power Electronics and Drives, 2007. SDEMPED 2007. IEEE International Symposium on (pp. 3-6). IEEE.
Zhou, Z., Liu, D., & Shi, X. (2014). Fault Diagnosis Based on Principal Component Analysis and Support Vector Machine for Rolling Element Bearings. In Practical Applications of Intelligent Systems (pp. 795-803). Springer Berlin Heidelberg.
Angeline, P. J. (1998, March). Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. In International Conference on Evolutionary Programming (pp. 601-610). Springer Berlin Heidelberg.
Bell, R. N., McWilliams, D. W., O'donnell, P., Singh, C., & Wells, S. J. (1985). Report of large motor reliability survey of industrial and commercial installations. I. IEEE Transactions on Industry applications, 21(4), 853-864.
Case Western Reserve University. (Last accessed in 2014), Bearing data center.
Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on Evolutionary Computation, 6(1), 58-73.
Chen, F., Tang, B., Song, T., & Li, L. (2014). Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization. Measurement, 47, 576-590.
Chebil, J., Noel, G., Mesbah, M., & Deriche, M. (2009). Wavelet decomposition for the detection and diagnosis of faults in rolling element bearings. Jordan Journal of Mechanical and Industrial Engineering, 3(4), 260-267.
Djebala, A., Babouri, M. K., & Ouelaa, N. (2015). Rolling bearing fault detection using a hybrid method based on empirical mode decomposition and optimized wavelet multi-resolution analysis. The International Journal of Advanced Manufacturing Technology, 79(9-12), 2093-2105.
Dong, S., & Luo, T. (2013). Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement, 46(9), 3143-3152.
He, S., Wu, Q. H., Wen, J. Y., Saunders, J. R., & Paton, R. C. (2004). A particle swarm optimizer with passive congregation. Biosystems, 78(1), 135-147.
Kankar, P. K., Sharma, S. C., & Harsha, S. P. (2011). Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing, 11(2), 2300-2312.
Kankar, P. K., Sharma, S. C., & Harsha, S. P. (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 38(3), 1876-1886.
Lei, Y., He, Z., Zi, Y., & Hu, Q. (2007). Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing, 21(5), 2280-2294.
Malhi, A., & Gao, R. X. (2004). PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, 53(6), 1517-1525.
Pandya, D. H., Upadhyay, S. H., & Harsha, S. P. (2014). Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft Computing, 18(2), 255-266.
Prieto, M. D., Cirrincione, G., Espinosa, A. G., Ortega, J. A., & Henao, H. (2013). Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Transactions on Industrial Electronics, 60(8), 3398-3407.
Saidi, L., Ali, J. B., & Fnaiech, F. (2015). Application of higher order spectral features and support vector machines for bearing faults classification. ISA transactions, 54, 193-206.
Sharma, A., Amarnath, M., & Kankar, P. K. (2016). Feature extraction and fault severity classification in ball bearings. Journal of Vibration and Control, 22(1), 176-192.
Stepanic, P., Latinovic, I. V., & Djurovic, Z. (2009). A new approach to detection of defects in rolling element bearings based on statistical pattern recognition. The International Journal of Advanced Manufacturing Technology, 45(1-2), 91-100.
Thelaidjia, T., & Chenikher, S. (2013, December). A New approach of preprocessing with SVM optimization based on PSO for bearing fault diagnosis. In Hybrid Intelligent Systems (HIS), 2013 13th International Conference on (pp. 319-324). IEEE.
Vapnik, V. (2013). The nature of statistical learning theory. Springer Science & Business Media.
Vapnik, V.N. (1998). Statistical Learning Theory, Springer, New York.
Wang, L. (Ed.). (2005). Support vector machines: theory and applications (Vol. 177). Springer Science & Business Media.
Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2), 151-157.
Zhang, Z., Wang, Y., & Wang, K. (2013). Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks. The International Journal of Advanced Manufacturing Technology, 68(1-4), 763-773.
Zhi-qiang, J., Hang-guang, F., & Ling-jun, L. I. (2005). Support Vector Machine for mechanical faults classification. Journal of Zhejiang University Science A, 6(5), 433-439.
Zhou, W., Habetler, T. G., & Harley, R. G. (2007, September). Bearing condition monitoring methods for electric machines: A general review. In Diagnostics for Electric Machines, Power Electronics and Drives, 2007. SDEMPED 2007. IEEE International Symposium on (pp. 3-6). IEEE.
Zhou, Z., Liu, D., & Shi, X. (2014). Fault Diagnosis Based on Principal Component Analysis and Support Vector Machine for Rolling Element Bearings. In Practical Applications of Intelligent Systems (pp. 795-803). Springer Berlin Heidelberg.