A comparative experimental study on the use of three denoising methods for bearing defect detection

Meccanica

Volumn 45, Issue 2, 2010, Pages 265-277

  Dron, Jean Paul ^a   Rasolofondraibe, Lanto ^a   Chiementin, Xavier ^a   Bolaers, Fabrice ^b  

^a Groupe de Recherche En Sciences Pour l'Ingénieur   (France)

^b UNIVERSITÉ DE REIMS CHAMPAGNE ARDENNE   (France)

Author keywords

Crest factor; Kurtosis; Self adaptive noise cancellation; Spectral substraction; Vibratory analysis; Wavelet

Indexed keywords

ACCELEROMETER SENSOR; ANALYSIS TIME; BEARING DEFECT; CREST FACTORS; DE-NOISING; DENOISING METHODS; DETECTION OF DEFECTS; EXPERIMENTAL STUDIES; MECHANICAL COMPONENTS; POTENTIAL DEFECTS; SCALAR PARAMETERS; SELF-ADAPTIVE; SPECTRAL SUBSTRACTION; STATISTICAL ANALYSIS; TEMPORAL SIGNALS; TIME FREQUENCY;

POLYCHLORINATED BIPHENYLS; SIGNAL DETECTION; SPURIOUS SIGNAL NOISE;

DEFECTS;

EID: 77952242740     PISSN: 00256455     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11012-009-9243-x     Document Type: Article

References (21)

1. Pachaud C (1997) Crest factor and kurtosis contributions to identify defects inducing periodical impulsive forces.Mech Syst Signal Process 11(6):903-916 (Pubitemid 127376011)
2. Mathiew J, Alfredson RJ (1984) The condition monitoring of rolling element bearings using vibration analysis. Trans ASME J Vib Acoust Stress Reliab Des 106:447-453
3. McFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearings by the high frequency resonance technique. Tribol Int 17(1):3-10
4. Kim PY (1984) A review of rolling element bearing health monitoring (II): preliminary test results on current technologies. In: Proceedings of machinery vibration monitoring and analysis meeting, vibration institute, New Orleans, 26-28 June 1984, pp 127-137
5. Tandon N (1994) A comparison of some vibration parameters for the condition monitoring of rolling element bearings. Measurement 12:285-289
6. Boullenger A, Pachaud C (1998) Diagnostic vibratoire en maintenance préventive. Dunod, Paris
7. Ho D, Randall RB (2000) Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mech Syst Signal Process 14(5):763-788
8. Campagna-Varenne (1985) 3 Etude des roulements par analyse spectrale. In: Vibrations actualités
9. Shahina A, Yegnanarayana B (2005) Language identification in noisy environments using throatmicrophone signals. IEEE, New York
10. Holzrichter JF, Ng LC (2001) Speech articular and user gesture measurements using micropower, interferometric EMsensors. IEEE, New York
11. Widrow B, Glover J, Mccool JM, Kaunitz J, Williams C, Hean BH, Zeidler J, Dong E, Goodlin R (1975) Adaptive noise cancelling: principles and applications. Proc IEEE 63(12)
12. Vorobyov S, Cichocki A, Bodyanskiy Y (2001) Adaptative nois cancellation for multisensory signals. In: Fluctuation and noise letters. World Scientific, Singapore
13. Xu R, Ren Z, Dai W, Lao D, Kwan C (2004) Multimodal speech enhancement in noisy environment. IEEE, New York
14. Hlawatsch F, Auger F (2005) Temps-fréquence: outils et concepts Livre traitement du signa et de l'image. Lavoier, Paris
15. Ranta R, Louis-Dorr V, Heinrich C, Wolf D, Guillemin F (2003) Débruitage par ondelettes et segmentation de signaux non stationnaires. Trait Signal 20(2):119-134
16. Coifman R,WickerhauserM(1995) Adapted waveform denoising for medical signals et images. IEEE Eng Med Biol 14(5):578-586
17. Coifman R, Wickerhauser M (1998) Experiments with adapted wavelet de-noising for medical signals and images. In: Akay M (ed), Time-frequency and wavelets in biomedical engineering, pp 323-346
18. Donoho D, Johnstone I (1954) Ideal spatial adaptation via wavelet shrinkage. Biometrika 81:425-455
19. Allen JB, Rabiner LR (1977) A unified approach to shorttime Fourier analysis and synthesis. Proc IEEE 65(11)
20. Boll JB (1979) Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans Acoust Speech Signal Process 27(22)
21. Cai L, Vincent C (1994) Débruitage de signaux chocs par soustraction spectrale du bruit. Méc Ind Matér 47(2)