Rotating machinery failure detection using different vibration analysis techniques

Proceedings of the 2002 International Conference on Noise and Vibration Engineering, ISMA

Volumn , Issue , 2002, Pages 1539-1544

  Del Castillo, L ^a   Artes M ^a   Garcia Prada J C ^a  

^a UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

FAILURE ANALYSIS; FREQUENCIES; FUNCTIONS; NEURAL NETWORKS; STATISTICAL METHODS; VIBRATIONS (MECHANICAL); WAVELET TRANSFORMS;

CLUSTERS; DISCRIMINANT ANALYSIS; STATISTICAL MOMENTS; VARIABLES;

ROTATING MACHINERY;

EID: 13444249739     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. B. Al-Najjar, Improved effectiveness of vibration monitoring of rolling bearing in paper mills. Journal of Engineering Tribology. Vol 212, (1998), pp. 111-120.
2. M. Artés, L. Castillo, J. C. García, Diagnosis del estado de máquinas rotativas mediante distintas técnicas de análisis de sus espectros de vibración. V Congreso Iberoamericano de Ingeniería Mecánica. Mérida, Venezuela, 2001 October 23-26, Mérida (2001 ), pp. 1513-1518.
3. S. G. Braun, The signature analysis of sonic bearing vibrations. IEEE Transactions on Sonics and ultrasonic. Vol. 65, N°. 1, (1980)pp. 37-50.
4. M. R. Dellomo, Helicopter gearbox fault detection: a neural network based approach. Transactions of the ASME, Journal of Vibration and Acoustic. Vol. 121, (1999) pp. 265-271.
5. B. Everitt, Cluster analysis. Heinamann Educational Books. London (1974).
6. J. C. García, Análisis de armónicos según órdenes de rotación en sistemas mecánicos. Tesis doctoral. ETSII. UNED. Madrid. (1991)
7. R. M. Hamer, J. W. Cunningham, Cluster analyzing profile data confounded with interrater differences: a comparison of profile association measures. Applied Psychological Measurement. Vol. 5, (1981), pp. 63-72.
8. P. Koikkalainen, J. Heikkonen, T. Honkanen, Fault diagnostics of rotating machines via self-organisation. Proceeding of SPIE. International Society for Optical Engineering. Vol. 2904, (1996) pp. 460-468.
9. E. Laloum, N. Dao, M. Daudon, Cluster analysis of gallstone ft-ir spectra: tests on simulated mixture spectra and comparison between spectral and morphological classification of human gallstones. Applied spectroscopy. Vol. 52, No. 9, (1998), pp. 1210-1221.
10. J. Lin, L. Qu, Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. Journal of Sound and Vibration. Vol. 234, No. 1, Academic Press (2000), pp. 135-148.
11. H. R. Martin, F. Honorvar, Application of statistical moments to bearings failure detection. Applied acoustics. 44, pp. 67-77. 1995.
12. B. A. Paya, I. I. Esat, M. N. M. Badi, Artificial neural network based fault diagnostics of rotating machinery using wavelets transforms as a preprocessor. Mechanical Systems & Signal Processing. Vol. 11, N°. 5, Academic Press (1997), pp. 751-765.
13. W. J. Staszewski, Wavelet based compression and feature selection for vibration analysis. Journal of Sound and Vibration. Vol. 211, N°. 5, Academic Press (1998), pp. 735-760.
14. G. White, Amplitude demodulation. A new tool for predictive maintenance. Sound and vibration. Vol. 25, No. 9, (1991), pp. 14-19.