Leakage Detection in Pipelines Using Decision Tree and Multi-Support Vector Machine
- 10.2991/ecae-17.2018.71How to use a DOI?
- leakage detection; decision tree; support vector machine; binary classification
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.
- © 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/).
Cite this article
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 -