1. Bafroui, H.H. and Ohadi, A., "Application of wavelet energy and shannon entropy for feature extraction in gearbox fault detection under varying speed conditions", Neurocomputing, Vol. 133, (2014), 437-445.
2.
Heidari, M.,
Homaei, H.,
Golestanian, H.,
A. Heidari, "Fault diagnosis of gearboxes using wavelet support vector machine, least square support vector machine and wavelet packet transform",
Journal of Vibroengineering, Vol. 18, No. 2, (2016), 860-875..
3. Dou, D., Yang, J., Liu, J. and Zhao, Y., "A rule-based intelligent method for fault diagnosis of rotating machinery", Knowledge-Based Systems, Vol. 36, (2012), 1-8.
4. Tian, Y., Ma, J., Lu, C. and Wang, Z., "Rolling bearing fault diagnosis under variable conditions using lmd-svd and extreme learning machine", Mechanism and Machine Theory, Vol. 90, (2015), 175-186.
5. Kankar, P., Sharma, S.C. and Harsha, S., "Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform", Journal of Vibration and Control, Vol. 17, No. 14, (2011), 2081-2094.
6. Cripps, A. and Nguyen, N., "Fuzzy lattice reasoning (FLR) classification using similarity measures", Computational Intelligence Based on Lattice Theory, Vol. 67, (2007), 263-284.
7. Heinermann, J. and Kramer, O., "Machine learning ensembles for wind power prediction", Renewable Energy, Vol. 89, (2016), 671-679.
8. Yu, J., "Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning", Journal of Sound and Vibration, Vol. 382, (2016), 340-356.
9. Yijing, L., Haixiang, G., Xiao, L., Yanan, L. and Jinling, L., "Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data", Knowledge-Based Systems, Vol. 94, (2016), 88-104.
10. Rathore, S.S. and Kumar, S., "Linear and non-linear
heterogeneous ensemble methods to predict the number of faults in software systems", Knowledge-Based Systems, Vol. 119, (2017), 232-256.
11. Ala'raj, M. and Abbod, M.F., "A new hybrid ensemble credit scoring model based on classifiers consensus system approach", Expert Systems with Applications, Vol. 64, (2016), 36-55.
12. Rajeswari, C., Sathiyabhama, B., Devendiran, S. and Manivannan, K., "A gear fault identification using wavelet transform, rough set based GA, ANN and C4. 5 algorithm", Procedia Engineering, Vol. 97, (2014), 1831-1841.
13. Skowron, A. and Rauszer, C., "The discernibility matrices and functions in information systems, in: R. Slowinski (Ed.), Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, Kluwer, Dordrecht, (1992), 331-362.
14. Walczak, B. and Massart, D., "Rough sets theory", Chemometrics and Intelligent Laboratory Systems, Vol. 47, No. 1, (1999), 1-16.
15. Alencar, A.S., Neto, A.R.R. and Gomes, J.P.P., "A new pruning method for extreme learning machines via genetic algorithms", Applied Soft Computing, Vol. 44, (2016), 101-107.
16. Dou, D., Yang, J., Liu, J., Zhang, Z. and Zhang, H., "Soft-sensor modeling for separation performance of dense-medium cyclone by field data", International Journal of Coal Preparation and Utilization, Vol. 35, No. 3, (2015), 155-164.
17. Lu, L., Yan, J. and de Silva, C.W., "Feature selection for ECG signal processing using improved genetic algorithm and empirical mode decomposition", Measurement, Vol. 94, (2016), 372-381.
18. Tsymbal, A., Pechenizkiy, M. and Cunningham, P., "Diversity in search strategies for ensemble feature selection", Information fusion, Vol. 6, No. 1, (2005), 83-98.
19. Case Western Reserve University, Bearing data centre. http://www.eecs.cwru.edu/laboratory/ bearing.
20. Junsheng, C., Dejie, Y. and Yu, Y., "Research on the intrinsic mode function (IMF) criterion in EMD method", Mechanical systems and signal processing, Vol. 20, No. 4, (2006), 817-824.
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