The Research on Fault Diagnosis for Gas Recovery of Single Coal Bed Methane well Based on Improved Particle Swarm Optimizing Support Vector Machine

DOI

10.2991/icmemtc-16.2016.100How to use a DOI?

Keywords

coal-bed methane; support vector machine; fault diagnosis; Particle Swarm Optimization

Abstract

As a new type of energy, coal-bed gas plays an important role in the national resource structure. This paper introduce the principle and process of gas recovery of single coal-bed methane well, according to the analysis of faults occurred in the system of gas recovery of single coal-bed methane well, by analyzing the characteristics parameters of gas recovery of single coal bed methane well system, combining with the advantages of support vector machine theory can solve the problems of nonlinear and high dimension. Because of the parameters selection of support vector machine has great influence on fault diagnosis, this article use particle swarm algorithm to optimize the parameters of support vector machine. In order to improve the shortcoming of Particle Swarm Optimization (PSO) algorithm which is easy to fall into local optimal, this article proposed that utilize improved particle swarm optimization support vector machine model for gas recovery of single coal-bed methane well system. The simulation results show that the new diagnosis model has a good fault diagnosis practicality and can be applied to fault diagnosis of single well.

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/).

Download article (PDF)

TY  - CONF
AU  - Yu Miao
AU  - Jianhua Yang
AU  - Wei Lu
PY  - 2016/04
DA  - 2016/04
TI  - The Research on Fault Diagnosis for Gas Recovery of Single Coal Bed Methane well Based on Improved Particle Swarm Optimizing Support Vector Machine
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 516
EP  - 521
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.100
DO  - 10.2991/icmemtc-16.2016.100
ID  - Miao2016/04
ER  -