7. REFERENCES
1. Yang, H., Mathew, J. and Ma, L., "Fault diagnosis of rolling
element bearings using basis pursuit", Mechanical Systems and
Signal Processing, Vol. 19, No. 2, (2005), 341-356.
2. Liang, M. and Soltani Bozchalooi, I., "An energy operator
approach to joint application of amplitude and frequency demodulation
for bearing fault detection",Mechanical Systems and Signal Processing,Vol.24,No.5,(2010),1473-1494.
3. Su, W., Wang, F., Zhu, H., Zhang, Z. and Guo, Z., "Rolling
element bearing faults diagnosis based on optimal morlet
wavelet filter and autocorrelation enhancement", Mechanical
Systems and Signal Processing, Vol. 24, No. 5, (2010), 14581472.
4. Dou, D., Yang, J., Liu, J. and Zhao, Y., "A rule-based intelligent
method for fault diagnosis of rotating machinery", KnowledgeBased
Systems,Vol.36,(2012),1-8.
5. Li, B., Liu, P.-y., Hu, R.-x., Mi, S.-s. and Fu, J.-p., "Fuzzy lattice
classifier and its application to bearing fault diagnosis", Applied
Soft Computing, Vol. 12, No. 6, (2012), 1708-1719.
6. Wang, D., Tse, P.W. and Tsui, K.L., "An enhanced kurtogram
method for fault diagnosis of rolling element bearings",
Mechanical Systems and Signal Processing, Vol. 35, No. 1,
(2013), 176-199.
7. Xu, H. and Chen, G., "An intelligent fault identification method
of rolling bearings based on lssvm optimized by improved pso",
Mechanical Systems and Signal Processing, Vol. 35, No. 1,
(2013), 167-175.
8. Al-Bugharbee, H. and Trendafilova, I., "A fault diagnosis
methodology for rolling element bearings based on advanced
signal pretreatment and autoregressive modelling", Journal of
Sound and Vibration, Vol. 369, (2016), 246-265.
9. Baraldi, P., Cannarile, F., Di Maio, F. and Zio, E., "Hierarchical
k-nearest neighbours classification and binary differential
evolution for fault diagnostics of automotive bearings operating
under variable conditions", Engineering Applications of
Artificial Intelligence, Vol. 56, (2016), 1-13.
10. Vakharia, V., Gupta, V.K. and Kankar, P.K., "Bearing fault
diagnosis using feature ranking methods and fault identification
algorithms", Procedia Engineering, Vol. 144, (2016), 343-350.
11. Singh, J., Darpe, A.K. and Singh, S.P., "Rolling element bearing
fault diagnosis based on over-complete rational dilation wavelet
transform and auto-correlation of analytic energy operator",
Mechanical Systems and Signal Processing, Vol. 100, (2018),
662-693.
12. Patil, S. and Phalle, V., "Fault detection of anti-friction bearing
using ensemble machine learning methods", International
Journal of Engineering, Transaction B: Applications, Vol. 31,
No. 11, (2018), 1972-1981.
13. Heidari, M., "Fault detection of bearings using a rule-based
classifier ensemble and genetic algorithm", International
Journal of Engineering, Transactions A: Basics, Vol. 30, No.
4, (2017), 604-609.
14. Li, X., Han, L., Xu, H., Yang, Y. and Xiao, H., "Rolling bearing
fault analysis by interpolating windowed dft algorithm",
International Journal of Engineering, Transactions A: Basics,
Vol. 32, (2019), 121-126.
15. Moshrefzadeh, A. and Fasana, A., "The autogram: An effective
approach for selecting the optimal demodulation band in rolling
element bearings diagnosis", Mechanical Systems and Signal
Processing, Vol. 105, (2018), 294-318.
16. Antoni, J., "The spectral kurtosis of nonstationary signals:
Formalisation, some properties, and application", in 2004 12th
European Signal Processing Conference., (2004), 1167-1170.
17. Antoni, J. and Randall, R.B., "The spectral kurtosis: Application
to the vibratory surveillance and diagnostics of rotating
machines", Mechanical Systems and Signal Processing, Vol.
20, No. 2, (2006), 308-331.
18. Antoni, J., "Fast computation of the kurtogram for the detection
of transient faults", Mechanical Systems and Signal Processing,
Vol. 21, No. 1, (2007), 108-124.