Gear fault diagnosis using transmission error and ensemble empirical mode decomposition

Abstract Classification of spall and crack faults of gear teeth is studied by applying the ensemble empirical mode decomposition (EEMD) to the transmission error (TE) measured by the encoders of the input and output shafts. Finite element models of the gears with the two faults are built, and TE’s are obtained by simulation of the faulty gears under loaded contact to identify the different characteristics. A simple test bed for a pair of spur gears is prepared to illustrate the approach, in which the TE’s are measured for the gears with seeded spall and crack, respectively. EEMD is applied to extract fault features under the noise from the measured TE. The differences of the spall and crack are clearly identified by the selected features of the intrinsic mode functions based on the class separability criterion. The k-nearest neighbor method is applied for the classification of the faults and normal gears using the features. The proposed method is advantageous over the existing practices in the sense that the TE signal measures the gear faults more directly with less noise, enabling successful diagnosis.

[1]  Ahmed Felkaoui,et al.  Contribution of angular measurements to intelligent gear faults diagnosis , 2018, J. Intell. Manuf..

[2]  Hui Li,et al.  Gear Fault Detection Based on Ensemble Empirical Mode Decomposition and Hilbert-Huang Transform , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

[3]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[4]  Gedong Jiang,et al.  Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method , 2011 .

[5]  Yaguo Lei,et al.  The use of ensemble empirical mode decomposition to improve bispectral analysis for fault detection in rotating machinery , 2010 .

[6]  Sergios Theodoridis,et al.  Introduction to Pattern Recognition: A Matlab Approach , 2010 .

[7]  David Siegel,et al.  A Systematic Methodology for Gearbox Health Assessment and Fault Classification , 2009 .

[8]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[9]  David He,et al.  Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study , 2014, Sensors.

[10]  Theodoros Loutas,et al.  Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurements , 2009 .

[11]  S. J. Loutridis,et al.  Damage detection in gear systems using empirical mode decomposition , 2004 .

[12]  Darryll J. Pines,et al.  A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .

[13]  R. B. Randall,et al.  Differential diagnosis of spall versus cracks in the gear tooth fillet region , 2004 .

[14]  Yaguo Lei,et al.  Gear crack level identification based on weighted K nearest neighbor classification algorithm , 2009 .

[15]  Robert B. Randall,et al.  Differential diagnosis of spall vs. cracks in the gear tooth fillet region: Experimental validation , 2009 .

[16]  Fabrice Ville,et al.  Gear tooth pitting modelling and detection based on transmission error measurements , 2013 .

[17]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[18]  T Tao,et al.  Fault diagnosis of a machine tool rotary axis based on a motor current test and the ensemble empirical mode decomposition method , 2011 .

[19]  David,et al.  Application of acoustic emission to seeded gear fault detection , 2005 .

[20]  C. James Li,et al.  ESTIMATING SIZE OF GEAR TOOTH ROOT CRACK USING EMBEDDED MODELLING , 2002 .

[21]  Sandeep M. Vijayakar,et al.  Detecting Gear Tooth Breakage Using Acoustic Emission: a Feasibility and Sensor Placement Study , 1999 .