Bearing Fault Diagnosis using Wavelet Packet Transform, Hybrid PSO and Support Vector Machine

Mori, 1996, - Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals, – J Wear, 195, 162, 10.1016/0043-1648(95)06817-1 Mcfadden, 1984, Model for the vibration produced by a single point defect in a rolling element bearing, Journal of Sound and Vibration, 96, 69, 10.1016/0022-460X(84)90595-9 J.K. Halme - Condition Monitoring of Oil Lubricated Ball Bearing Using Wear Debris and Vibration Analysis - 6th International Tribology Conference, Austrib 02. 2-5.(2002). Seryasat, 2010, “Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multi-class support vector machine (MSVM),” in Proc. of the IEEE International, The Scientific World Journal Conference on Systems, Man and Cybernetics (SMC ‘10), 4300 Z. L. Liu, Q. Z. Lu,Y. L.Wang, andC.Y.Wei, “Adirect selection method of feature frequency,” in Emerging Research in Artificial, Intelligence and Computational Intelligence—International Conference,vol. 315, pp. 479-486, 2012. Yang, 2007, Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension, Mech. Syst. Signal Process., 21, 2012, 10.1016/j.ymssp.2006.10.005 Tse, 2004, Machine fault diagnosis through an effective exact wavelet analysis, Journal of Sound and Vibration, 277, 1005, 10.1016/j.jsv.2003.09.031 Peng, 2004, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography, Mech. Systems And Signal Processing, 11, 751 Samanta, 2003, Artificial neural network based fault diagnostics of rolling element bearings using time domain features, Mechanical Systems and Signal Processing, 17, 317, 10.1006/mssp.2001.1462 Zhiwen Liu, 2013, Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings, J. Neurocomputing, 99, 399, 10.1016/j.neucom.2012.07.019 Peng, 2004, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography, Mechanical Systems And Signal Processing, 11, 751 Lin, 2004, Mechanical Fault Detection Based on the Wavelet De-noising Technique, J. of Vib and Acou., 26, 9, 10.1115/1.1596552 Liu, 2005, Selection of wavelet packet basis for rotating machinery fault diagnosis, Journal of Sound and Vibration, 284, 567, 10.1016/j.jsv.2004.06.047 Wang pan-pan, 2012, Feature extraction of induction motor stator fault based on particle, Swarm optimization and wavelet packet, J. of coal Sci & Eng(china), 18, 432, 10.1007/s12404-012-0418-z Xiaoran Zhu*, 2012, Intelligent fault diagnosis of rolling bearing based on kernel neighborhood rough sets and statistical features, Journal of Mechanical Science and Technology, 26 Bottou, 1994, Comparison of Classifier Methods A Case Study in Handwriting Digit Recognition, Proc. international Conference on Pattern Recognition, 77, 10.1109/ICPR.1994.576879 Krepel, 1999, Pairwise Classification and Support Vector Machines, 255 Hsu, 2002, A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks, 13, 415, 10.1109/72.991427 Samanta, 2004, Gear Fault Detection using Artificial Neural Networks and Support Vector Machines with Genetic Algorithms, Mechanical Systems and Signal Processing, 18, 625, 10.1016/S0888-3270(03)00020-7 Y. Shi, R.C. Eberhart, A modified particle swarm optimizer,in: Proceedings of the IEEE International Conference on Evolutionary Computation Anchorage, 4-9, May, AK, USA,1998, pp. 69-73. Han, 2004, Feature subset selection based on relative dependency between attributes, rough sets and current trends in computing, Lecture Notes in ComputerScience, 3066, 176, 10.1007/978-3-540-25929-9_20 Hannah Inbarani, 2014, Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis, J. of .comp methods and programs in bio medicine, 113, 175, 10.1016/j.cmpb.2013.10.007