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.

KEYWORDS:

• Dynamic modeling
• equipment wear tests
• wear particle analysis
• oil condition monitoring

Acknowledgement

The author acknowledges all of the members of the Tribology Research Group in the School of Mechanical & Manufacturing Engineering, the University of New South Wales, for very helpful discussions.

Funding

Financial support for the present research was provided by the National Science Foundation of China (Grant Nos. 51275381, 50905135) and Fundamental Research Funds for the Central Universities of China.