[Study on the antioxidation property of engine oil by infrared spectroscopy].
The relationship between the engine oil' s infrared spectroscopic characteristics and its antioxidation property was studied, and the technical method to obtain the spectral information in accordance with the contribution of lubricating-oil composition to antioxidation property is presented. Quantitative self-organization neural networks was developed by combining the advantages of BP neural networks and those of self-organization neural networks. Quantitative self-organization neural networks possesses both the qualitative clustering function of self-organization neural networks and the quantitative analysis function of BP neural networks, and featuFes better robustness, so the quantitative analysis result of quantitative self-organization neural networks was better than that of BP neural networks. The result has provided a kind of new technology means for fast testing lubricating-oil performance.
Li
Zi-cun
ZC
Fuel and Oil Testing Center, Logistic Engineering University, Chongqing 400016, China. hgycfx@163.com
Feng
Xin-lu
XL
Xiong
Gang
G
D000975:Antioxidants / Q000032:analysis*; D005648:Fuel Oils / Q000032:analysis*; D016571:Neural Networks, Computer; D013055:Spectrophotometry, Infrared / Q000379:methods*
D000975:Antioxidants; D005648:Fuel Oils