Research on the Pattern Recognition Method of Slurry Pipeline Safety Status

DOI

10.2991/mmebc-16.2016.298How to use a DOI?

Keywords

Slurry pipeline, support vector machine, fault diagnosis.

Abstract

For the non-linear relationships of main influence factors of slurry pipeline safety status and the confusable problem of slurry pipeline safety status, this paper used the cross validation method to select the optimum parameters of the kernel function and combined the support vector machine to diagnose the security status of slurry pipeline. First standardize the test data that affect the slurry pipeline safety status. Then according to the accuracy of fault diagnosis and used cross validation method to optimize the kernel functions parameters. And as the parameters of support vector machine (SVM) to identify the slurry pipeline safety status. Through the analytic results of the normal state of slurry pipeline, high pressure diaphragm pumps wear and pipeline blockage show that using cross validation method to improve kernel function parameters of support vector machine (SVM) model can accurately identify pulp pipeline safety state and fault type.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chengjiang Zhou
AU  - Jiande Wu
AU  - Jingzong Yang
PY  - 2016/06
DA  - 2016/06
TI  - Research on the Pattern Recognition Method of Slurry Pipeline Safety Status
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 1459
EP  - 1463
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.298
DO  - 10.2991/mmebc-16.2016.298
ID  - Zhou2016/06
ER  -