Bearing Fault Diagnosis Using Feature Ranking Methods and Fault Identification Algorithms
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[1] Liu Chengliang,et al. Adaptive wavelet filtering for bearing monitoring based on block bootstrapping and white noise test , 2012 .
[2] Raksha Upadhyay,et al. Channel optimization and nonlinear feature extraction for Electroencephalogram signals classification , 2015, Comput. Electr. Eng..
[3] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[4] Daming Lin,et al. A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .
[5] P. K. Kankar,et al. A comparison of feature ranking techniques for fault diagnosis of ball bearing , 2016, Soft Comput..
[6] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[7] J. Mathew,et al. Time domain methods for monitoring the condition of rolling element bearings , 1985 .
[8] Marko Robnik-Sikonja,et al. Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.
[9] P. K. Kankar,et al. A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings , 2015 .
[10] N. Tandon,et al. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .
[11] Satish C. Sharma,et al. Rolling element bearing fault diagnosis using wavelet transform , 2011, Neurocomputing.
[12] Robert X. Gao,et al. Base Wavelet Selection for Bearing Vibration Signal Analysis , 2009, Int. J. Wavelets Multiresolution Inf. Process..
[13] M.H. Hassoun,et al. Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.