A Bayesian network based method for reliability analysis of subsea blowout preventer control system

Aiming at the limitations of traditional reliability analysis theory in multi-state system, a method for reliability modeling and assessment of a multi-state system based on Bayesian Network (BN) is proposed with the advantages of uncertain reasoning and describing multi-state of event. Through the case of cell production line system, in this paper we will discuss how to establish and construct a multi-state system model based on Bayesian network, and how to apply the prior probability and posterior probability to do the bidirectional inference analysis, and directly calculate the reliability indices of the system by means of prior probability and Conditional Probability Table (CPT) . Thereby we can do the qualitative and quantitative analysis of the multi-state system reliability, identify the weak links of the system, and achieve assessment of system reliability.

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Reliability Analysis of Redundant Dynamic Positioning Control System With Human Factor Involved

Volume 3: Structures, Safety and Reliability ◽

10.1115/omae2016-54101 ◽

2016 ◽

Author(s):

Fang Wang ◽

Yong Bai ◽

Feng Xu

Keyword(s):

Control System ◽

Markov Process ◽

Reliability Analysis ◽

Oil And Gas ◽

Dynamic Positioning ◽

Positioning Control ◽

Safety And Reliability ◽

Long Time ◽

Component Failure Rates ◽

Technical Failures

Deepwater oil and gas explorations bring more safety and reliability problems for the dynamically positioned vessels. With the demands for the safety of vessel crew and onboard device increasing, the single control architecture of dynamic positioning (DP) system can not guarantee the long-time faultless operation for deeper waters, which calls for much more reliable control architectures, such as the Class 2 and Class 3 system, which can tolerate a single failure of system according to International Maritime Organization’s (IMO) DP classification. The reliability analysis of the main control station of DP Class 3 system is proposed from a general technical prospective. The fault transitions of the triple-redundant DP control system are modeled by Markov process. The effects of variation in component failure rates on the system reliability are investigated. Considering the DP operation involved a human-machine system, the DP operator factors are taken into account, and the human operation error failures together with technical failures are incorporated to the Markov process to predict the reliability of the DP control system.

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Operation-oriented reliability and availability evaluation for onboard high-speed train control system with dynamic Bayesian network

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x18800630 ◽

2018 ◽

Vol 233 (3) ◽

pp. 455-469 ◽

Cited By ~ 1

Author(s):

Lei Jiang ◽

Yiliu Liu ◽

Xiaomin Wang ◽

Mary Ann Lundteigen

Keyword(s):

Control System ◽

Bayesian Network ◽

High Speed ◽

Dynamic Bayesian Network ◽

Dynamic Bayesian Networks ◽

High Speed Train ◽

Train Control ◽

Train Control System ◽

Onboard System ◽

Reliability And Availability

The reliability and availability of the onboard high-speed train control system are important to guarantee operational efficiency and railway safety. Failures occurring in the onboard system may result in serious accidents. In the analysis of the effects of failure, it is significant to consider the operation of an onboard system. This article presents a systemic approach to evaluate the reliability and availability for the onboard system based on dynamic Bayesian network, with taking into account dynamic failure behaviors, imperfect coverage factors, and temporal effects in the operational phase. The case studies are presented and compared for onboard systems with different redundant strategies, that is, the triple modular redundancy, hot spare double dual, and cold spare double dual. Dynamic fault trees of the three kinds of onboard system are constructed and mapped into dynamic Bayesian networks. The forward and backward inferences are conducted not only to evaluate the reliability and availability but also to recognize the vulnerabilities of the onboard systems. A sensitivity analysis is carried out for evaluating the effects of failure rates subject to uncertainties. To improve the reliability and availability, the recovery mechanism should be paid more attention. Finally, the proposed approach is validated with the field data from one railway bureau in China and some industrial impacts are provided.

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