Gear tooth diagnosis using wavelet multi-resolution analysis enhanced by Kaiser’s windowing

Wavelet multi-resolution analysis shows promising results for gear tooth damage diagnostics. However, selecting an accurate mother wavelet, defining dynamic threshold value and identifying the resolution levels to be considered in gear fault detection and diagnosis are still challenging tasks. This paper proposes an enhanced wavelet-based technique for detecting, locating and estimating the severity of defects in gear tooth fracture. The proposed technique improves the wavelet multi-resolution analysis by decomposing the noisy data into different resolution levels with data sliding through Kaiser’s window. Only the maximum expansion coefficients at each resolution level are used in de-noising, detecting and measuring the severity of the defects. A small set of coefficients is used in the monitoring process without assigning threshold values or performing signal reconstruction. The proposed technique shows accurate results in detecting and localizing gear tooth fracture.

[1]  S. Mitra,et al.  Handbook for Digital Signal Processing , 1993 .

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

[3]  Giorgio Dalpiaz,et al.  Effectiveness and Sensitivity of Vibration Processing Techniques for Local Fault Detection in Gears , 2000 .

[4]  Gaoyong Luo,et al.  Real-Time Condition Monitoring by Significant and Natural Frequencies Analysis of Vibration Signal with Wavelet Filter and Autocorrelation Enhancement , 2000 .

[5]  Li Zhen,et al.  Customized wavelet denoising using intra- and inter-scale dependency for bearing fault detection , 2008 .

[6]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[7]  Robert G. Parker,et al.  Impact of tooth friction and its bending effect on gear dynamics , 2009 .

[8]  J. Rafiee,et al.  A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system , 2009, Expert Syst. Appl..

[9]  Ming J. Zuo,et al.  Simulation of spur gear dynamics and estimation of fault growth , 2008 .

[10]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[11]  Mir Mohammad Ettefagh,et al.  Asynchronous input gear damage diagnosis using time averaging and wavelet filtering , 2008 .

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

[13]  Ibrahim Esat,et al.  Gear condition monitoring by a new application of the Kolmogorov—Smirnov test , 2001 .

[14]  Dong Wang,et al.  Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition , 2009 .

[15]  Amiya R Mohanty,et al.  Multistage gearbox condition monitoring using motor current signature analysis and Kolmogorov–Smirnov test , 2006 .

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

[17]  Fakher Chaari,et al.  Analytical modelling of spur gear tooth crack and influence on gearmesh stiffness , 2009 .

[18]  M. Salama,et al.  Wavelet-Based Signal Processing Techniques For Disturbance Classification and Measurement , 2000 .

[19]  Anand Parey,et al.  Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect , 2006 .

[20]  K. I. Ramachandran,et al.  A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box , 2008, Expert Syst. Appl..

[21]  A. Y. Chikhani,et al.  Genetic Algorithms Based Economic Dispatch for Cogeneration Units Considering Multiplant , 2022 .

[22]  R. Bartnikas,et al.  On-line detection and measurement of partial discharge signals in a noisy environment , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[23]  Magdy M. A. Salama,et al.  Wavelet-based signal processing for disturbance classification and measurement , 2002 .

[24]  Ibrahim Esat,et al.  A NEW APPROACH TO TIME-DOMAIN VIBRATION CONDITION MONITORING: GEAR TOOTH FATIGUE CRACK DETECTION AND IDENTIFICATION BY THE KOLMOGOROV–SMIRNOV TEST , 2001 .

[25]  Donald R. Houser,et al.  Mathematical models used in gear dynamics—A review , 1988 .