Condition Monitoring of Gears and Advanced Signal Processing Techniques towards More Effective Diagnostic Schemes

A diagnostic methodology of artificial defects in a single stage gearbox operating under various load levels and different defect states is proposed in the present work based on vibration recordings as well as advanced signal analysis techniques. Two different wavelet-based signal processing methodologies, using the discrete as well as the continuous wavelet transform, were utilised for the analysis of the recorded vibration signals and useful diagnostic information were extracted out of them. Both wavelet analysis techniques provided the ability of distinguishing between the healthy and the artificially defected gears. In this way, the health monitoring potential of vibration monitoring in the case of rotating machinery and gearboxes obtains a new dynamic under the prism of sophisticated time-frequency signal processing schemes, rather than conventional FFT-based approaches.

[1]  David,et al.  Observations of acoustic emission activity during gear defect diagnosis , 2003 .

[2]  David,et al.  A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears , 2007 .

[3]  Amiya R Mohanty,et al.  Monitoring gear vibrations through motor current signature analysis and wavelet transform , 2006 .

[4]  P. D. McFadden,et al.  APPLICATION OF WAVELETS TO GEARBOX VIBRATION SIGNALS FOR FAULT DETECTION , 1996 .

[5]  Naim Baydar,et al.  DETECTION OF INCIPIENT TOOTH DEFECT IN HELICAL GEARS USING MULTIVARIATE STATISTICS , 2001 .

[6]  Amiya R Mohanty,et al.  Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform , 2008 .

[7]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[8]  W. J. Wang,et al.  Application of orthogonal wavelets to early gear damage detection , 1995 .

[9]  Naim Baydar,et al.  A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution. , 2001 .

[10]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[11]  David,et al.  Application of acoustic emission to seeded gear fault detection , 2005 .

[12]  Naim Baydar,et al.  DETECTION OF GEAR FAILURES VIA VIBRATION AND ACOUSTIC SIGNALS USING WAVELET TRANSFORM , 2003 .

[13]  Naim Baydar,et al.  Detection of Gear Deterioration Under Varying Load Conditions by Using the Instantaneous Power Spectrum , 2000 .

[14]  P. D. McFadden,et al.  Early Detection of Gear Failure by Vibration Analysis--I. Calculation of the Time Frequency Distribution , 1993 .

[15]  Ibrahim Esat,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROTATING MACHINERY USING WAVELET TRANSFORMS AS A PREPROCESSOR , 1997 .

[16]  P. D. McFadden,et al.  Early detection of gear failure by vibration analysis--ii. interpretation of the time-frequency distribution using image processing techniques , 1993 .

[17]  V. Kostopoulos,et al.  Wavelet analysis of head acceleration response under dirac excitation for early oedema detection. , 2008, Journal of biomechanical engineering.