Early Detection of Fatigue Damage on Rolling Element Bearings Using Adapted Wavelet

Among the advanced techniques of the predictive maintenance, the vibratory analysis proves to be very effective, in particular, for monitoring rotating components such as the bearings. Their damage creates cyclic efforts which are at the origin of the processing of vibratory measurements. This processing can be made by temporal methods, frequential methods, or by time-scale methods using the wavelets for 2 decades. The wavelet transform is a very effective processing, however, the difficulties of application and interpretation of the results slow down their employment. The determination of the parameters of the wavelets makes its use all the more difficult. Moreover, the use of these time-scale methods is very expensive in time computation. This paper proposes a wavelet adapted to the mechanical shock response of a structure with n degrees of freedom. In addition, we developed a procedure for analysis of signals by this wavelet which makes it possible to accelerate the process and to improve detection in the case of disturbed signals. This methodology is compared with the traditional time-scale methods and is implemented to detect defects of different sizes on outer rings and inner rings of ball bearings.

[1]  Ioannis Antoniadis,et al.  Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings Using Complex Shifted Morlet Wavelets , 2002 .

[2]  Umberto Meneghetti,et al.  Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings , 2001 .

[3]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

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

[5]  Qiao Sun,et al.  SINGULARITY ANALYSIS USING CONTINUOUS WAVELET TRANSFORM FOR BEARING FAULT DIAGNOSIS , 2002 .

[6]  W. Staszewski WAVELET BASED COMPRESSION AND FEATURE SELECTION FOR VIBRATION ANALYSIS , 1998 .

[7]  L. D. Meyer,et al.  An Analytic Model for Ball Bearing Vibrations to Predict Vibration Response to Distributed Defects , 1980 .

[8]  Y. Ueno,et al.  Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals , 1996 .

[9]  Jean-Michel Poggi,et al.  Wavelets and their applications , 2007 .

[10]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

[11]  N. Tandon,et al.  A Theoretical Model to Predict Vibration Response of Rolling Bearings to Distributed Defects Under Radial Load , 1998 .

[12]  D. Nélias,et al.  Experimental and Theoretical Investigation on Rolling Contact Fatigue of 52100 and M50 Steels Under EHL or Micro-EHL Conditions , 1998 .

[13]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[14]  R. M. Stewart,et al.  Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis , 1978 .