How to cite this paper
Kaewkongka, T. (2016). A train bearing fault detection and diagnosis using acoustic emission.Engineering Solid Mechanics, 4(2), 63-68.
Refrences
Choe, H. C., Wan, Y., & Chan, A. K. (1997, April). Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: Comparison of FFT, CWT, and DWT features. In AeroSense & apos; 97 (pp. 480-496). International Society for Optics and Photonics.
Holroyd, T. J. (2002). Acoustic emission sensors for OEM applications. Engineering science and education journal, 11(1), 29-35.
Li, Y. S. C. T. S., Billington, S., Zhang, C., Kurfess, T., Danyluk, S., & Liang, S. (1999). Adaptive prognostics for rolling element bearing condition. Mechanical systems and signal processing, 13(1), 103-113.
Pachaud, C., Salvetat, R., & Fray, C. (1997). Crest factor and kurtosis contributions to identify defects inducing periodical impulsive forces. Mechanical Systems and Signal Processing, 11(6), 903-916.
Paya, B. A., Esat, I. I., & Badi, M. N. M. (1997). Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mechanical systems and signal processing, 11(5), 751-765.
Qu, L., Liu, X., Peyronne, G., & Chen, Y. (1989). The holospectrum: a new method for rotor surveillance and diagnosis. Mechanical Systems and Signal Processing, 3(3), 255-267.
Safizadeh, M. S., Lakis, A. A., & Thomas, M. (1900). Using short-time Fourier transforms in machinery fault diagnosis. COMADEM, International Journal, 3(1), 5-16.
Scholey, J. J., Wilcox, P. D., Wisnom, M. R., Friswell, M. I., Pavier, M., & Aliha, M. R. (2009). A generic technique for acoustic emission source location. J Acoust Emis, 27, 291-298.
Shiroishi, J. Y. S. T., Li, Y., Liang, S., Kurfess, T., & Danyluk, S. (1997). Bearing condition diagnostics via vibration and acoustic emission measurements. Mechanical systems and signal processing, 11(5), 693-705.
Wang, Y., & Wu, Z. R. (2013, October). A Train Hot Bearing Detection System Based on Infrared Array Sensor. In Applied Mechanics and Materials (Vol. 347, pp. 672-676).
Holroyd, T. J. (2002). Acoustic emission sensors for OEM applications. Engineering science and education journal, 11(1), 29-35.
Li, Y. S. C. T. S., Billington, S., Zhang, C., Kurfess, T., Danyluk, S., & Liang, S. (1999). Adaptive prognostics for rolling element bearing condition. Mechanical systems and signal processing, 13(1), 103-113.
Pachaud, C., Salvetat, R., & Fray, C. (1997). Crest factor and kurtosis contributions to identify defects inducing periodical impulsive forces. Mechanical Systems and Signal Processing, 11(6), 903-916.
Paya, B. A., Esat, I. I., & Badi, M. N. M. (1997). Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mechanical systems and signal processing, 11(5), 751-765.
Qu, L., Liu, X., Peyronne, G., & Chen, Y. (1989). The holospectrum: a new method for rotor surveillance and diagnosis. Mechanical Systems and Signal Processing, 3(3), 255-267.
Safizadeh, M. S., Lakis, A. A., & Thomas, M. (1900). Using short-time Fourier transforms in machinery fault diagnosis. COMADEM, International Journal, 3(1), 5-16.
Scholey, J. J., Wilcox, P. D., Wisnom, M. R., Friswell, M. I., Pavier, M., & Aliha, M. R. (2009). A generic technique for acoustic emission source location. J Acoust Emis, 27, 291-298.
Shiroishi, J. Y. S. T., Li, Y., Liang, S., Kurfess, T., & Danyluk, S. (1997). Bearing condition diagnostics via vibration and acoustic emission measurements. Mechanical systems and signal processing, 11(5), 693-705.
Wang, Y., & Wu, Z. R. (2013, October). A Train Hot Bearing Detection System Based on Infrared Array Sensor. In Applied Mechanics and Materials (Vol. 347, pp. 672-676).