Study on Oil Quality Evaluation System and its Implementation Based on Multi-Information Fusion

Article Preview

Abstract:

This paper presents the oil quality evaluation system and establishes the two-stage fusion model based on multi-sensor information fusion technology. It also develops the oil quality evaluation model based on neural network model. With the advantages of multi-source information technology, the model implements comprehensive evaluation for oil quality, and provides a set of neural network training process and its results which achieve the oil quality evaluation based on information fusion. The case study shows that the prediction results for four kinds of oil samples by evaluation model based on multi-source fusion are consistent with the actual results. The comparison between operation test trend predictions and actual tests also shows the correctness of the oil quality evaluation model. The proposed multi-information fusion technology for oil quality evaluation system improves the evaluation accuracy and reduces dependence on technical personnel’s analysis experience, which is of great importance for improving the technical management level and the awareness of oil lubrication properties.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 197-198)

Pages:

1486-1493

Citation:

Online since:

February 2011

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Changsong Zheng, Biao Ma, Xianlin Sun and Ju Yinfang. China Mechanical Engineering, Vol. 19(2008) , pp.1054-1057 (In Chinese).

Google Scholar

[2] Xinping Yan, Chunhua Zhao and Lv Zhiyong, et al. Tribology International Vol. 38(2005) , pp.31-34.

Google Scholar

[3] Zhifang Wang, Xinping Yan and Chengqing Yuan. China Mechanical Engineering, Vol. 18(2007) , pp.1962-1965 (In Chinese).

Google Scholar

[4] Wang yang Wu and Wei Zhang. Chinese Hydraulics & Pneumatics, Vol. (9)(2009) , pp.77-79. (In Chinese) (In Chinese).

Google Scholar

[5] Vanhanen J, Rinkio M and Aumanen J, et al. Applied Optics, Vol. 43(2004) , pp.4718-4722.

Google Scholar

[6] V. Macian, B Tormos and P Olmeda, et al. Tribology, Vol. 36(2003) , pp.771-776.

Google Scholar

[7] Jinlong Xu, Bin Su and Chen Cheng. Lubrication Engineering, Vol. 34(2009), pp.105-109 (In Chinese).

Google Scholar

[8] Alexander P Rotshtein. Reliability Engineering and System Safety, Vol. 91(2006), pp.1095-1101.

Google Scholar