Application of image registration methods in monitoring the progression of surface fatigue failures in geared transmission systems

Surface fatigue failure occurs in geared transmission systems due to factors such as high contact stress, and monitoring its progression is vital if the eventual failure of the tooth flank is to be prevented. Techniques involving the analysis of vibration, acoustic emission and oil debris have been used to successfully monitor the progression of the different phases of gear surface fatigue failure. However, during monitoring a suitable assessment method is required to correlate the characteristics of the signal with the condition of the gear surface. The most commonly used such methods are gear flank profile scanning, replica sample analysis and conventional image analysis, with each technique having both advantages and disadvantages. Responding to the demand for effective assessment methods, this study experimentally evaluates the development of micro-pitting in gears using a new image registration technique in an online health monitoring system. Given a set of captured images of gear surface degradation with different exposure times and geometric deformations, an image registration approach is proposed to cope with inter-image illumination changes of arbitrary shape. Then correlation between the resulting aligned images is compared to a reference one before testing obtained. The results validate the system's capabilities to detect early gear defects and reliably identify the gradual development of micro-pitting in gears, so that it could be used in predictive health monitoring (PHM) systems.

[1]  Wenyi Wang.  An Evaluation of Some Emerging Techniques for Gear Fault Detection , 2003 .

[2]  Mohamed M. Fouad,et al.  Image registration under local illumination variations using robust bisquare M-estimation , 2010, 2010 IEEE International Conference on Image Processing.

[3]  David,et al.  Identification of the acoustic emission source during a comparative study on diagnosis of a spur gearbox , 2005 .

[4]  Paresh Girdhar.  Practical Machinery Vibration Analysis and Predictive Maintenance , 2004 .

[5]  James D. Smith,et al.  Gears and Their Vibration, A Basic Approach to Understanding Gear Noise , 1983 .

[6]  Mohamed M. Fouad,et al.  Geometric registration of images with arbitrarily-shaped local intensity variations from shadows , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[7]  David,et al.  A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size , 2006 .

[8]  Chris K. Mechefske,et al.  Using acoustic emission to monitor sliding wear , 2002 .

[9]  Chee Keong Tan,et al.  Diagnostics and prognostics with Acoustic Emission, Vibration and Spectrometric Oil Analysis for spur gears; a comparative study , 2005 .

[10]  Mohamed M. Fouad,et al.  Image Registration Under Illumination Variations Using Region-Based Confidence Weighted $M$-Estimators , 2012, IEEE Transactions on Image Processing.

[11]  Zhou Wang,et al.  Translation insensitive image similarity in complex wavelet domain , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[12]  H. Saunders,et al.  Mechanical Signature Analysis—Theory and Applications , 1988 .

[13]  David,et al.  A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears , 2007 .

[14]  Brian A. Shaw,et al.  Intelligent diagnostic health management of power transmission systems: An experimental validation , 2010 .

[15]  Mohamed M. Fouad,et al.  Two-step super-resolution technique using bounded total variation and bisquare M-estimator under local illumination changes , 2011, 2011 18th IEEE International Conference on Image Processing.

[17]  J. R. Jordan,et al.  Wear-debris detection and analysis techniques for lubricant-based condition monitoring , 1988 .

[18]  Robert B. Randall,et al.  State of the art in monitoring rotating machinery. Part 2 , 2004 .

[19]  Chen Xu,et al.  Automatic registration of aerial image series using geometric invariance , 2008, 2008 IEEE International Conference on Automation and Logistics.

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

[21]  Brian A. Shaw,et al.  A novel intelligent health monitoring system for gear fatigue failure prediction , 2010 .

[22]  Mohamed M. Fouad,et al.  Geometric image registration under arbitrarily-shaped locally variant illuminations , 2012, Signal Image Video Process..

[23]  Luca Lucchese,et al.  Estimation of Two-Dimensional Affine Transformations Through Polar Curve Matching and Its Application to Image Mosaicking and Remote-Sensing Data Registration , 2006, IEEE Transactions on Image Processing.

[24]  Mohamed M. Fouad,et al.  Geometric Image Registration under Locally Variant Illuminations Using Huber M-estimator , 2010, ICISP.