Abstract
The subsea all-electric Christmas tree is key equipment in subsea production systems. If a failure occurs, the marine environment will be seriously polluted. Therefore, strict reliability analysis and measures to improve reliability must be performed before such equipment is launched, which is crucial to safe subsea production. A real-time reliability evaluation method for the all-electric Christmas tree mechanical system integrated with the static Bayesian network fault diagnosis stage is proposed in this paper, which realizes the identification of the fault type of the components and the real-time reliability evaluation of the mechanical system under different failure rates of the components. As a supplement to the method, by using mutual information to conduct sensitivity analysis on the reliability of the mechanical system, the importance of the basic events of each component on the reliability of the system is finally given. The proposed method provides significant theoretical support for the maintenance of the subsea all-electric Christmas tree and can be extended to the reliability evaluation of general subsea production systems.
Highlights
Process safety, risk analysis, and reliability evaluation have paramount significance in the modern process industries for preventing fatalities and loss of assets [1] and [2]
To study the influence of the important factor of “fault” on system reliability, a real-time reliability evaluation method integrated with online diagnosis is proposed by using Bayesian networks, and the subsea allelectric XT mechanical system is used as a case study to verify the practicability of the method
Research on a Real-time Reliability Evaluation Method Integrated with Online Fault Diagnosis: Subsea All-electric Christmas Tree System as a Case Study is the “SFailure” state probability of surface control subsea safety valve (SCSSV) > 80 %, which is consistent with the actual result
Summary
Risk analysis, and reliability evaluation have paramount significance in the modern process industries for preventing fatalities and loss of assets [1] and [2]. Data-driven methods (e.g., Bayesian Network) have been shown to solve problems in complex systems, which has been used for reliability or risk analyses of XT systems or other subsea equipment. Cai and Liu et al [17] proposed a reliability and resilience evaluation method by combining the Markov model with a Bayesian network and used this method to conduct a risk analysis and reliability evaluation of the subsea oil and gas pipelines. To study the influence of the important factor of “fault” on system reliability, a real-time reliability evaluation method integrated with online diagnosis is proposed by using Bayesian networks, and the subsea allelectric XT mechanical system is used as a case study to verify the practicability of the method. According to the Weibull distribution law, the reliability change trend of the normal degradation process of the component is obtained These data provide probability information for the Bayesian network.
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