Leakage Detection in Pipelines Using Decision Tree and Multi-Support Vector Machine

DOI

10.2991/ecae-17.2018.71How to use a DOI?

Keywords

leakage detection; decision tree; support vector machine; binary classification

Abstract

In order to solve the problem of leakage detection in the case of complex conditions and limited training samples, a multivariate classification recognition model was built by using Decision Tree and Support Vector Machine, which has advantages of rapid speed and high efficiency in classification and outstanding characteristics in small samples binary classification. The model was trained with a fault feature vector which is a dimensionless value extracted from the pipeline pressure signal characteristic parameters, and then using the model to test the samples. The results show that this method not only can complete the model learning training in the case of small samples, but also has been greatly improved over the neural network method in terms of the recognition performance, and can be effectively applied to leakage detection in pipelines.

Copyright

© 2018, 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  - Zhigang Chen
AU  - Xu Xu
AU  - Xiaolei Du
AU  - Junling Zhang
AU  - Miao Yu
PY  - 2017/12
DA  - 2017/12
TI  - Leakage Detection in Pipelines Using Decision Tree and Multi-Support Vector Machine
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)
PB  - Atlantis Press
SP  - 327
EP  - 331
SN  - 2352-5401
UR  - https://doi.org/10.2991/ecae-17.2018.71
DO  - 10.2991/ecae-17.2018.71
ID  - Chen2017/12
ER  -