Modeling Wear State Evolution Using Real-Time Wear Debris Features

ABSTRACT Because wear is one of the most typical causes of decreasing performance in running machines, monitoring wear is regarded as a crucial technology in maintaining the health of machines. However, monitoring wear is not a fully mature process because quantifying the development of wear in real time is a challenging task because there is no universal indicator. To meet this need, wear-oriented dynamic modeling with online ferrographic images was used to investigate and then describe a real-time wear state. This investigation was carried out by combining three wear indices to describe the wear rate, the wear mechanism, and the severity of wear. A binary classifier method is also proposed to classify these wear stages in the three extracted indices. A strategy to identify the dynamic transition of wear states with adaptive parameters is also developed and then a four-ball wear test is carried out to verify the method. The results indicate that this modeling strategy can accurately identify a developing wear state that is characterized by stages. This proposed method is better at monitoring the health evolution of a machine system than just detecting faults.

[1]  Dorin Comaniciu,et al.  An Algorithm for Data-Driven Bandwidth Selection , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Kenneth C. Ludema,et al.  Mechanism-based modeling of friction and wear , 1996 .

[3]  Ying Du,et al.  Progress and trend of sensor technology for on-line oil monitoring , 2013 .

[4]  Hongkun Wu,et al.  Imaged wear debris separation for on-line monitoring using gray level and integrated morphological features , 2014 .

[5]  Zhongxiao Peng,et al.  An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis , 2003 .

[6]  J.S.H. Tsai,et al.  A boundary method for outlier detection based on support vector domain description , 2009, Pattern Recognit..

[7]  J. L. Miller,et al.  In-line oil debris monitor for aircraft engine condition assessment , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[8]  Hongkun Wu,et al.  Watershed-Based Morphological Separation of Wear Debris Chains for On-Line Ferrograph Analysis , 2013, Tribology Letters.

[9]  K. Ludema,et al.  Wear models and predictive equations: their form and content , 1995 .

[10]  Mayorkinos Papaelias,et al.  Condition monitoring of wind turbines: Techniques and methods , 2012 .

[11]  Jiaoyi Wu,et al.  A New On-Line Visual Ferrograph , 2009 .

[12]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Gavin Traynor,et al.  Condition monitoring of wind turbines , 2014 .

[14]  Junhong Mao,et al.  Wear Characterization by an On-Line Ferrograph Image , 2011 .

[15]  P. N. Hobson,et al.  Engineering for profit from waste. Proceedings of the Institution of Mechanical Engineers , 1988 .

[16]  Abdollah A. Afjeh,et al.  Integrating Oil Debris and Vibration Gear Damage Detection Technologies Using Fuzzy Logic , 2004 .

[17]  J. Williams,et al.  Wear debris and associated wear phenomena—fundamental research and practice , 2000 .

[18]  Yang Dong,et al.  Application of relevance vector machine in the engine oil wear particle fault diagnosis , 2013, 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA).

[19]  Sylvester Abanteriba,et al.  Determining inductive sensor wear debris limits for rolling contact fatigue of bearings , 2015 .

[20]  Tonghai Wu,et al.  Full-life dynamic identification of wear state based on on-line wear debris image features , 2014 .

[21]  Hong Zhang,et al.  Application of grey modeling method to fitting and forecasting wear trend of marine diesel engines , 2003 .

[22]  Aparecido Carlos Gonçalves,et al.  Predictive maintenance of a reducer with contaminated oil under an excentrical load through vibration and oil analysis , 2011 .

[23]  Thomas Brett Kirk,et al.  Wear particle classification in a fuzzy grey system , 1999 .

[24]  Michael F. Ashby,et al.  Wear-rate transitions and their relationship to wear mechanisms , 1987 .