Oğuzhan Yüce;Utku Aslan;Cemal Hanilçi;Engin Korkmaz;Oğuz Alper İsen;Emin Cantez
References
[1] R. B. Randall, J. Antoni, and S. Chobsaard, “A comparison of cyclostationary and envelope analysis in the diagnostics of rolling element bearings,” in 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), vol. 6, June 2000, pp. 3882–3885 vol.6.
[2] R. Randall, J. Antoni, and S. Chobsaard, “The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals,” Mechanical Systems and Signal Processing, vol. 15, no. 5, pp. 945 – 962, 2001.
[3] J. Duan, T. Shi, H. Zhou, J. Xuan, and Y. Zhang, “Multiband envelope spectra extraction for fault diagnosis of rolling element bearings,” Sensors, vol. 18, no. 5, p. 1466, 2018.
[4] X. Fan, M. Liang, T. Yeap, and B. Kind, “A joint wavelet lifting and independent component analysis approach to fault detection of rolling element bearings,” Smart Materials and Structures, vol. 16, p. 1973, 09 2007.
[5] D. Wang, Q. Miao, X. Fan, and H.-Z. Huang, “Rolling element bearing fault detection using an improved combination of hilbert and wavelet transforms,” Journal of Mechanical Science and Technology, vol. 23, no. 12, pp. 3292–3301, Dec 2009.
[6] R. B. Randall and J. Antoni, “Rolling element bearing diagnosticsaa tutorial,” ˆ Mechanical Systems and Signal Processing, vol. 25, no. 2, pp. 485 – 520, 2011.
[7] Y. Wang, J. Xiang, R. Markert, and M. Liang, “Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications,” Mechanical Systems and Signal Processing, vol. 66-67, pp. 679 – 698, 2016.
[8] J. Antoni, G. Xin, and N. Hamzaoui, “Fast computation of the spectral correlation,” Mechanical Systems and Signal Processing, vol. 92, pp. 248 – 277, 2017.
[9] D. Wang, P. W. Tse, and K. L. Tsui, “An enhanced kurtogram method for fault diagnosis of rolling element bearings,” Mechanical Systems and Signal Processing, vol. 35, no. 1, pp. 176 – 199, 2013.
[10] Y. Lei, J. Lin, Z. He, and Y. Zi, “Application of an improved kurtogram method for fault diagnosis of rolling element bearings,” Mechanical Systems and Signal Processing, vol. 25, no. 5, pp. 1738 – 1749, 2011.
[11] B.-A. Behrens, S. Hübner, and K. Wölki, “Acoustic emission promising and challenging technique for process monitoring in sheet metal forming,” Journal of Manufacturing Processes, vol. 29, pp. 281 – 288, 2017.
[12] A. Lofqvist and B. Mandersson, “Long-time average spectrum of speech and voice analysis,” 1987.
[13] T. Leino, “Long-term average spectrum in screening of voice quality in speech: Untrained male university students,” Journal of Voice, vol. 23, no. 6, pp. 671 – 676, 2009.
[14] T. Kinnunen, V. Hautamaki, and P. Franti, “On the use of long-term average spectrum in automatic speaker recognition,” in In 5th Int. Symposium on Chinese Spoken Language processing (ISCSLP06), 2006, pp. 559–567.
[15] L. Rabiner and R. Schafer, Theory and Applications of Digital Speech Processing, 1st ed. Upper Saddle River, NJ, USA: Prentice Hall Press, 2010.
[16] I. Daubechies, “The wavelet transform, time-frequency localization and signal analysis,” IEEE Transactions on Information Theory, vol. 36, no. 5, pp. 961–1005, Sep. 1990.