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Reliability Analysis of Submarine Mud-Lifting System Based on Bayesian Network

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DOI: 10.23977/ssge.2020.020101 | Downloads: 66 | Views: 3452

Author(s)

Dongbo Wang 1, Chuan Wang 1, Chao Liu 2, Wen Hou 2, Anyi Wang 2, Yupeng Liu 1, Bohan Zhang 1

Affiliation(s)

1 College of Mechanical and Electrical Engineering, Southwest of Petroleum University, Chengdu Sichuan 610500, China
2 National Engineering Research Center for Oil and Gas Drilling Equipment, Baoji Petroleum Machinery Co., LTD, Baoji 721002, China

Corresponding Author

Chuan Wang

ABSTRACT

The seabed mud lifting system is the key equipment of offshore drilling, and the failure will cause serious economic loss. In order to improve its reliability, fault tolerant technology is adopted to design the electronic control system redundancy. The fault tree model of the system is established and a quantitative evaluation method based on the fault tree transformed into Bayesian network is proposed. Taking OREDA as the prior data of equipment failure, the failure probability of the system after 5 years operation is deduced by Bayesian network. According to the Bayesian network, the posterior probability of basic events is deduced backward, and the weak links of the system are analyzed. Considering the uncertainty of failure rate, the sensitivity of the system is analyzed. The results show that after five years of operation, the reliability of the newly designed redundant system with forward reasoning is 8.56% higher than that of the traditional system, and the most prone fault points of the electronic control system and mechanical system are the PLC controller and the submarine pump module. The sensitivity analysis verifies the correctness of the model.

KEYWORDS

Redundant systems, Fault Tree Analysis, Bayesian networks, Failure probability, Reliability

CITE THIS PAPER

Dongbo Wang, Chuan Wang, Chao Liu, Wen Hou, Anyi Wang, Yupeng Liu and Bohan Zhang. Reliability Analysis of Submarine Mud-Lifting System Based on Bayesian Network. Smart Systems and Green Energy (2020) Vol. 2: 1-10. DOI: http://dx.doi.org/10.23977/ssge.2020.020101.

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