Study on the Intelligent Identification Wear Particles Based on the K Value
作者: 齐运永 ：华洋海事中心，北京;
Abstract: A new method named K value method is adopted to classify the wear particles as the criterion of the wear particles. Through analyzing the K value obtained from wear particle image samples, the range of the three kinds of wear particle images’ K value are obtained. Although the K value ranges, the three different particles are still a little overlapped. It is better than the variable metric method, so it can be used as a good method to classify the three different kinds of wear particles. As a criterion to distinguish the above three kinds of wear particles, the K value method can effectively make up for ineffective results from fractal dimension. The K value method can improve the preci-sion of the identification. The K value method provides a new method for ferrographic wear par-ticles intelligent identification and has certain theoretical significance and practical value.
文章引用: 齐运永 (2015) 基于K值的铁谱磨粒智能识别研究。 交通技术， 4， 58-63. doi: 10.12677/OJTT.2015.44009
 王弢 (2001) 磨粒在线监测方法研究及试验装置的设计. 硕士论文, 武汉理工大学, 武汉.
 Xu, K., Luxmoore, A.R., Jones, L.M. and Deravi, F. (1998) Integration of neural networks and expert systems for microscopic wear partiele analysis. Knowledge-Based Systems, 11, 213-227.
 Podsiadio, P. and Stachowiak, G.W. (1999) Applications of Hurst orientation transform to the characterization of surface anisotropy. Tribology International, 32, 387-392.
Peng, Z. and Kirk, T.B. (1998) Computer image analysis of wear partieles in three-dimensions for ma-chine condition monitoring. Wear, 223, 157-166.
Peng, Z. and Rink, T.B. (1997) The development of three dimensional imaging techniques of wear particle analysis. Wear, 203-204, 418-424.
Stachowiak, G.P., Stachowiak, G.W. and Podsiadlo, P. (2008) Automated classification of wear particles based on their surface texture and shape features. Tribology International, 41, 34-43.