Earticle

초록

영어

Subsea blowout preventer is an important tool for ensuring safety of drilling activities and rig personal. In case of faults, it might cause severe damages to the environment and oil companies. This paper presents the method to perform fault diagnosis of subsea blowout preventer (BOP) based on artificial neural network (ANN).BP ANN of the BOP are proposed on the basis of the typical faults of the BOP in the process of opening and closing. In order to obtain higher training speed and precision, BP ANN are improved with gradient descent with momentum and adaptive LR gradient descent methods. Besides, RBF network is also presented for getting a better model for diagnosis. Compared with BP network, RBF network has better performance concerning training speed and precision in this case. However, BP network will show stronger flexibility in the complex model with plenty of fault types.

목차

Abstract
 1. Introduction
 2. Typical Fault Analysis of Subsea BOP
 3. Fault Diagnosis based on Neural Network
  3.1. BP Network
  3.2. The Improvement of BP Network
  3.3. RBF Network
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Zengkai Liu [ College of Mechanical and Electronic Engineering, China University of Petroleum, China ]
• Yonghong Liu [ College of Mechanical and Electronic Engineering, China University of Petroleum, China ]
• Baoping Cai [ College of Mechanical and Electronic Engineering, China University of Petroleum, China ]
• Dawei Zhang [ College of Mechanical and Electronic Engineering, China University of Petroleum, China ]
• Junlei Li [ Wanhua Chemical Group Co., Ltd ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605