Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors

Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and application of WT, the selection of the parameters used, and the key achievements in using WT for crack diagnosis.

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

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

[3]  William Q. Meeker,et al.  A Statistical Method for Crack Detection from Vibrothermography Inspection Data , 2012 .

[4]  P. Laguna,et al.  Signal Processing , 2002, Yearbook of Medical Informatics.

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

[6]  William Q. Meeker,et al.  Detecting cracks in aircraft engine fan blades using vibrothermography nondestructive evaluation , 2014, Reliab. Eng. Syst. Saf..

[7]  Imran Touqir,et al.  Selection of optimal wavelet bases for image compression using SPIHT algorithm , 2015, Other Conferences.

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

[9]  Vikas Rastogi,et al.  A Brief Review on Dynamics of a Cracked Rotor , 2009 .

[10]  M. Feldman,et al.  Damage Diagnosis of Rotors: Application of Hilbert Transform and Multihypothesis Testing , 1999 .

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

[12]  Bin Zhao The Application of Wavelet Finite Element Method on the Crack Recognition of the Gate Rotor Shaft of the Single Screw Compressor , 2011 .

[13]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  J. Rafiee,et al.  Application of mother wavelet functions for automatic gear and bearing fault diagnosis , 2010, Expert Syst. Appl..

[15]  K. V. Gangadharan,et al.  Gear Fault Detection Using Vibration Analysis and Continuous Wavelet Transform , 2014 .

[16]  M. Salman Leong,et al.  Analysis of Residual Wavelet Scalogram for Machinery Fault Diagnosis , 2013 .

[17]  Adriano Camps,et al.  RFI Mitigation in Microwave Radiometry Using Wavelets , 2009, Algorithms.

[18]  Robert Gasch,et al.  Dynamic behaviour of the Laval rotor with a transverse crack , 2008 .

[19]  P. Gács,et al.  Algorithms , 1992 .

[20]  A. Sekhar,et al.  Crack detection and vibration characteristics of cracked shafts , 1992 .

[21]  Shyamala C. Doraisamy,et al.  Multi-level basis selection of wavelet packet decomposition tree for heart sound classification , 2013, Comput. Biol. Medicine.

[22]  Cristina Castejón,et al.  Experimental Analysis and Validation of a Vibration-Based Technique for Crack Detection in a Shaft , 2015 .

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

[24]  Cristina Castejón,et al.  Automated diagnosis of rolling bearings using MRA and neural networks , 2010 .

[25]  A. S. Sekhar,et al.  Multiple cracks effects and identification , 2008 .

[26]  M. Elforjani,et al.  Detecting natural crack initiation and growth in slow speed shafts with the Acoustic Emission technology , 2009 .

[27]  Paolo Pennacchi,et al.  Cracked Rotors: A Survey on Static and Dynamic Behaviour Including Modelling and Diagnosis , 2010 .

[28]  Smith,et al.  Mathematics of the Discrete Fourier Transform (DFT) with Audio Applications , 2007 .

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

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

[31]  Bin Li,et al.  Research on Feature Recognition of Nuclear Power Equipment Based on the Optimal Wavelet Basis , 2013 .

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

[33]  Engin Avci,et al.  Speech recognition using a wavelet packet adaptive network based fuzzy inference system , 2006, Expert Syst. Appl..

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

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

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

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

[38]  Rodney H. G. Tan,et al.  Power system transient analysis using scale selection wavelet transform , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.

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

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

[41]  Phil E. Irving,et al.  Detection and Monitoring of Fatigue Cracks in Metallic Structures Using Acoustic Emission: Routes to Quantification of Probability of Detection , 2014 .

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

[43]  Bing Li,et al.  Quantitative Identification of Multiple Cracks in a Rotor Utilizing Wavelet Finite Element Method , 2012 .

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

[45]  Amit Kumar,et al.  Optimal Selection of Wavelet Function and Decomposition Level for Removal of ECG Signal Artifacts , 2015 .

[46]  S. Loutridis,et al.  CRACK IDENTIFICATION IN BEAMS USING WAVELET ANALYSIS , 2003 .

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

[48]  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 .

[49]  Bo-Hyung Cho,et al.  An innovative approach for characteristic analysis and state-of-health diagnosis for a Li-ion cell based on the discrete wavelet transform , 2014 .

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

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

[52]  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 .

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

[54]  Jerzy T. Sawicki,et al.  Rigid Finite Element Model of a Cracked Rotor , 2012 .

[55]  Paolo Pennacchi,et al.  Some remarks on breathing mechanism, on non-linear effects and on slant and helicoidal cracks , 2008 .

[56]  Weidong Jiao,et al.  Detecting a cracked rotor with HHT-based time-frequency representation , 2008, 2008 IEEE International Conference on Automation and Logistics.

[57]  Fethi Bereksi-Reguig,et al.  An automatic wavelet denoising scheme for heart sounds , 2015, Int. J. Wavelets Multiresolution Inf. Process..

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

[59]  Paolo Pennacchi,et al.  A model based identification method of transverse cracks in rotating shafts suitable for industrial machines , 2006 .

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

[61]  Mohammad Ali Tinati,et al.  A Wavelet Packets Approach to Electrocardiograph Baseline Drift Cancellation , 2006, Int. J. Biomed. Imaging.

[62]  Christofer Toumazou,et al.  Noise Reduction for Nonlinear Nonstationary Time Series Data using Averaging Intrinsic Mode Function , 2013, Algorithms.

[63]  Michael P. Dessauer,et al.  Discriminative features and classification methods for accurate classification , 2010, Defense + Commercial Sensing.

[64]  Paolo Mercorelli,et al.  A denoising procedure using wavelet packets for instantaneous detection of pantograph oscillations , 2013 .

[65]  A. S. Sekhar,et al.  Identification of Unbalance and Crack Acting Simultaneously in a Rotor System: Modal Expansion versus Reduced Basis Dynamic Expansion , 2005 .

[66]  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 .

[67]  Yanbo Huang,et al.  Advances in Artificial Neural Networks - Methodological Development and Application , 2009, Algorithms.

[68]  Diego H. Milone,et al.  Genetic wavelet packets for speech recognition , 2013, Expert Syst. Appl..

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

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

[71]  Shih-Fu Ling,et al.  Machinery Diagnosis Based on Wavelet Packets , 1997 .

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

[73]  Fethi Bereksi-Reguig,et al.  An Automatic Wavelet Selection Scheme for Heart Sounds Denoising , 2014, IWBBIO.

[74]  Zhengjia He,et al.  Crack detection in a shaft by combination of wavelet-based elements and genetic algorithm , 2008 .

[75]  Yukio Ishida Cracked rotors: Industrial machine case histories and nonlinear effects shown by simple Jeffcott rotor , 2008 .