Research on Wear Debris Recognition Algorithm Based on IFNN

Abstract:

According to the qualitative characterization of the morphological features of the wear debris, Feature vector mathematics model of wear debris are built,which are based on Foruier parameter refining methord.Then the advantages of fuzzy system and neural network are taken to establish a kind of improved fuzzy neural network (IFNN) models and algorithm.which is used to realize the automatic classification and recognition of wear debris. An improved learning algorithm with the modified fuzzy weight is proposed on the basis of the fuzzy neurons model for the max-min fuzzy operator. The amount of calculation for the improved FNN model is reduced greatly and the convergence velocity is improved. At last ,the experiment results show that the recognition method based on the IFNN is good at algorithm convergence speed and recognition accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 4043-4047
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.4043
• Online since: October 2011
• Authors: Xin Wang
• Keywords: Characteristic Parameters, Convergence Velocity, IFNN, Shape Recognition, Wear Debris

References

[1] Raadnui S, Roylance B J: Lubrication Engineering Vol 51(1995), p: 432-437.

[2] Weihua Wang, Yonghui Yin, Chengtao Wang: Journal of China University of Mining & Technology Vol 32(2003), p: 200-203, in Chinese.

[3] Zhenfeng Wu, Hongfu Zuo et. al: TRIBOLOGY Vol 20(2000), p: 143-146, in Chinese.

[4] Xuefeng Chen, Peijun Liang: Ship Electronic Engineering Vol 29(2009), p: 129-131, in Chinese.

[5] Jiadao Wang, Xianmei Kong et. al: Journal of Tsing hua University(Sci &Tech) Vol 38(1998), p: 42-46, in Chinese.

[6] Weihua Wang, Yonghui Yin et. al: TRIBOLOGY Vol 23(2003), p: 340-343, in Chinese.

[7] Hongwei Yang el. al: LUBRICATION ENGINEERING Vol 32(2007), p: 162-164, in Chinese.

[8] Yibo Cao, Xiaopeng Xie: LUBRICATION ENGINEERING Vol 5(2006, )p: 64-67, in Chinese.

[9] Jianli Kang et. al: Precise Manufacturing & Automation Vol 3(2004), p: 40-42, in Chinese.

[10] Akihiko Umeda et. al: Wear Vol 216(1998), p: 220-228.

[11] Puyin Liu et. al: Fuzzy System and mathematics Vol 12(1998), p: 77-87, in Chinese.

[12] Yunhui Liu, et. al: Journal of Huaqiao University(Natural Science) Vol 31(2010), p: 256-259, in Chinese.

[13] Jiayu Jiang et. al: Journal of Naval Aeronautical and Astronautical University Vol 25 (2010), p: 220-224, in Chinese.

[14] Li Ma et. al: COMPUTEREN GINEERING & SCIENCE Vol 132( 2010), p: 137-140, in Chinese.