Analysis of the influence of crack location for diagnosis in rotating shafts based on 3 x energy

Abstract The aim of condition monitoring is to detect faults before a catastrophic failure occurs. Cracks in rotating shafts are especially critical. The present work studies vibration signals obtained from a rotating shaft under different crack depths and locations. Tests were performed in a rig called Rotokit at a steady state at different rotation speeds. Signals obtained are analyzed by means of energy using the Wavelet theory, specifically the Wavelet Packets Transform. Nine crack depths in the shafts were tested, from 4% to 50% of the shaft diameter. Previous related work showed good reliability for crack diagnosis using 3 x energy for cracks in the middle section. In the present work, previous results are compared to the obtained for a crack in a change of section at one side. In both crack locations, large changes in energy are observed at 3 x at high speeds. Energy levels at this harmonic were used for the inverse process of crack detection, and probability of detection curves were calculated by thresholding. Cracks with depths above 12% can be detected with reliability in the locations tested using this method.

[1]  L. Romdhane,et al.  A combined genetic algorithm-fuzzy logic method (GA-FL) in mechanisms synthesis , 2004 .

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

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

[4]  Fadi Al-Badour,et al.  Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques , 2011 .

[5]  Anders la Cour-Harbo,et al.  Ripples in Mathematics , 2003 .

[6]  Xuejun Li,et al.  Research on Feature Extraction Experiment for Acoustic Emission Signal of Rotor Crack Fault , 2010 .

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

[8]  Grzegorz Litak,et al.  Multiresolution Wavelet Analysis of the Dynamics of a Cracked Rotor , 2009 .

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

[10]  Rajiv Tiwari,et al.  Identification of a multi-crack in a shaft system using transverse frequency response functions , 2010 .

[11]  K. S. Srinivasan,et al.  Role of an Artificial Neural Network and a Wavelet Transform for Condition Monitoring of the Combined Faults of Unbalance and Cracked Rotors , 2010 .

[12]  Dong-Sik Gu,et al.  Evaluation of the use of envelope analysis and DWT on AE signals generated from degrading shafts , 2012 .

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

[14]  Ian K. Jennions,et al.  Unbalance localization through machine nonlinearities using an artificial neural network approach , 2014 .

[15]  S. Sekhar.  Shaft Crack Identification using Artificial Neural Networks and Wavelet Transform data of a Transient Rotor , 2007 .

[16]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[17]  Fanrang Kong,et al.  Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier , 2013 .

[18]  Cristina Castejón,et al.  Crack detection in rotating shafts based on 3 × energy: Analytical and experimental analyses , 2016 .

[19]  Jean-Jacques Sinou,et al.  An Experimental Investigation of Condition Monitoring for Notched Rotors Through Transient Signals and Wavelet Transform , 2009 .

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

[21]  C. Nagaraju,et al.  Application of 3D wavelet transforms for crack detection in rotor systems , 2009 .

[22]  Yang Yu,et al.  The application of energy operator demodulation approach based on EMD in machinery fault diagnosis , 2007 .

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

[24]  Dan Guo,et al.  Vibration analysis of a cracked rotor using Hilbert–Huang transform , 2007 .

[25]  A. S. Sekhar,et al.  Hilbert–Huang transform for detection and monitoring of crack in a transient rotor , 2008 .

[26]  Babak Eftekharnejad,et al.  Shaft crack diagnostics in a gearbox , 2012 .

[27]  Cristina Castejón,et al.  New stopping criteria for crack detection during fatigue tests of railway axles , 2015 .