Servo Valve Nozzle Fuzzy Clustering Analysis Pairing Algorithm

Article Preview

Abstract:

Electro-hydraulic servo valve is the key component of an electro-hydraulic servo control system, and double nozzle flapper valve is the main type of electro-hydraulic servo valve. Nozzles are the important part of a double nozzle flapper valve, the pairing quality is directly related to the performance of the servo valve .Servo valve is of symmetrical structure, and the use of nozzles is also in pairs. The process of nozzles matched in pairs is called pairing process. Because the orifice diameter of the nozzle is only about 0.2 to 0.3 mm, it is difficult to pair with the method of direct measurements. Besides, as a kind of hydraulic components, nozzles are usually paired by flow rate measured under differential pressure. Two nozzles will be matched to a pair if their pressure-flow rate characteristic curve is within the allowed tolerance. To ensure the success rate of pairing, it usually needs a large number of machined nozzles to be sifted. According to the principle of clustering analysis, we propose a new paring algorithm which can match nozzles efficiently and automatically.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

476-480

Citation:

Online since:

October 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2016 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Ramu Narayanan, Gunho Sohn, Heungsik B. Kim, John R Miller. Soft classification of mixed seabed objects based on fuzzy clustering analysis using airborne LIDAR bathymetry data[J]. Journal of Applied Remote Sensing. 2011, 5(1): 2-12.

DOI: 10.1117/1.3595267

Google Scholar

[2] Manish Joshi, Pawan Lingras, C. Raghavendra Rao. Analysis of rough and fuzzy clustering[C]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2010: 679-686.

DOI: 10.1007/978-3-642-16248-0_92

Google Scholar

[3] Wei Wei, Yajie Zhang, Guilian Wu, Mingjuan Tong. Ultra-short-term/short-term wind power continuous prediction based on fuzzy clustering analysis[C]. 2012 IEEE Innovative Smart Grid Technologies - Asia, ISGT Asia 2012. (2012).

DOI: 10.1109/isgt-asia.2012.6303399

Google Scholar

[4] Sun Dongwang, Li Zhigang, Sun Xunjun, Huang Shanshan[C]. 2011 1st International Conference on Electric Power Equipment - Switching Technology. 2011: 391-395.

DOI: 10.1109/icepe-st.2011.6123015

Google Scholar

[5] Li Liu, Jianzhong Zhou, Xueli An, Yinghai Li, Qiang Liu. Improved fuzzy clustering method based on entropy coefficient and its application[C]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008: 11-20.

DOI: 10.1007/978-3-540-87734-9_2

Google Scholar