Experimental Analysis and Validation of a Vibration-Based Technique for Crack Detection in a Shaft

Detection of cracks in shafts plays a critical role in maintenance. A crack can cause a catastrophic failure with costly processes of reparation. The aim of condition monitoring and fault diagnostics is to detect and to distinguish different kinds of faults. In this work vibration signals are obtained from an analytical Jeffcott rotor model and a real rotating machine during working. The aim was to identify indicators of the presence of a crack, to allow the inverse process of detecting a crack and its size for the machine tested. Signals were processed using the Wavelet Packets Transform. In signals obtained from the analytical model, the best indicators of crack were frequencies related to the first’s harmonics of the rotation speed. However, when matching the theoretical results with the experimental ones, only harmonics higher than the 2× component of the rotation speed seemed to feel that changes in practice.

[1]  Eric A. Butcher,et al.  New breathing functions for the transverse breathing crack of the cracked rotor system: Approach for critical and subcritical harmonic analysis , 2011 .

[2]  A. S. Sekhar.  On-Line Rotor Fault Identification , 2004 .

[3]  C Castejón,et al.  Automatic detection of cracked rotors combining multiresolution analysis and artificial neural networks , 2015 .

[4]  Paolo Pennacchi,et al.  Crack effects in rotordynamics , 2008 .

[5]  Yanhui Feng,et al.  Normalized wavelet packets quantifiers for condition monitoring , 2009 .

[6]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble , 2007 .

[7]  Balbir S. Dhillon,et al.  Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network , 2012 .

[8]  Cristina Castejón,et al.  Incipient Fault Detection in Bearings Through the use of WPT Energy and Neural Networks , 2014 .

[9]  C. A. Papadopoulos,et al.  The strain energy release approach for modeling cracks in rotors: A state of the art review , 2008 .

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

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

[12]  S. Audebert,et al.  A comprehensive theoretical, numerical and experimental approach for crack detection in power plant rotating machinery , 2008 .

[13]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[14]  S. Mallat.  VI – Wavelet zoom , 1999 .

[15]  R. Gordon Kirk,et al.  Cracked shaft detection and diagnostics: A literature review , 2004 .

[16]  A. Zambrano,et al.  The influence of crack-imbalance orientation and orbital evolution for an extended cracked Jeffcott rotor , 2004 .

[17]  I. Mayes,et al.  Analysis of the Response of a Multi-Rotor-Bearing System Containing a Transverse Crack in a Rotor , 1984 .