本书重点论述了深水防喷器系统的可靠性建模与评估理论与方法，采用markov、bayesiannetworks等工具研究了深水防喷器系统的可靠性和可用性，给出了考虑共因失效、不完全覆盖、不完全维修、预防性维护等的可靠性建模方法，并研究得到了系统参数对可靠性的影响规律关系，给出了提高深水防喷器系统可靠性的措施。
Foreword
Preface
Chapter 1 Development of Automatic Subsea Blowout Preventer Stack Control System Using PLC Based SCADA
1.1 Introduction
1.2 Hardware architecture
1.2.1 Overall system architecture
1.2.2 Triple redundant controllers
1.2.3 Dual redundant Ethemet
1.2.4 Redundant subsea electronic modules
1.3 Voting algorithm
1.3.1 Discrete output voting
1.3.2 Discrete input voting
1.3.3 Analog input voting
1.4 Software methodology
1.4.1 Control logic
1.4.2 I-IMI program
1.4.3 Redundant databases
1.4.4 Remote access
1.5 Results and discussions
1.6 Conclusions
References
Chapter 2 Reliability Analysis of Subsea Blowout Preventer Control Systems Subjected to Multiple Error Shocks
2.1 Introduction
2.2 System description
2.2.1 Architecture of subsea BOP control system
2.2.2 Configuration of subsea BOP control system
2.3 System modelling and reliability analysis
2.3.1 Assumptions
2.3.2 System modelling and reliability analysis for TMR system
2.3.3 System modelling and reliability analysis for DDMR system
2.4 Results and discussions
2.5 Conclusions
References
Chapter 3 Performance Evaluation of Subsea Blowout Preventer Systems with Common Cause Failures
3.1 Introduction
3.2 System description
3.3 Reliability modelling and analysis
3.3.1 Assumptions
3.3.2 Failure rate and repair rate calculations
3.3.3 Reliability modelling
3.3.4 Performance evaluation
3.4 Results and discussions
3.5 Conclusions
References
Chapter 4 Using Bayesian Networks in Reliability Evaluation for Subsea Blowout Preventer Control System
4.1 Introduction
4.2 System description
4.2.1 Subsea BOP system
4.2.2 Configuration of subsea BOP control system
4.3 Bayesian networks modelling for reliability analysis
4.3.1 Overview of Bayesian networks
4.3.2 Bayesian networks modelling for redundant systems
4.3.3 Bayesian networks modelling for subsea BOP control systems
4.4 Results and discussions
4.4.1 Reliability of subsea BOP control systems
4.4.2 Difference between posterior and prior probabilities
4.4.3 Effects of coverage factors on the reliability
4.4.4 Effects of failure rate on the reliability
4.5 Conclusions
References
Chapter 5 Dynamic Bayesian Networks Based Performance Evaluation of Subsea Blowout Preventers in Presence of Imperfect Repair
5.1 Introduction
5.2 Dynamic Bayesian networks with imperfect repair
5.2.1 Overview of BN and DBN
5.2.2 DBN modeling of series and parallel systems
5.2.3 Imperfect repair modeling
5.2.4 Conditional probability table
5.2.5 Reliability and availability
5.3 Case study
5.3.1 Fault tree
5.3.2 Corresponding DBN
5.3.3 Evaluation and validation
5.3.4 Results and discussions
5.4 Conclusions
References
Chapter 6 Performance Evaluation of Subsea BOP Control Systems Using Dynamic Bayesian Networks with Imperfect Repair and Preventive Maintenance
6.1 Introduction
6.2 DBN modeling of series, parallel and voting systems
6.2.1 DBN modeling with common-cause failure
6.2.2 Imperfect repair and preventive maintenance modeling
6.2.3 Conditional probability table
6.2.4 Reliability and availability
6.3 Case study
6.3.1 Configuration ofsubsea BOP control system
6.3.2 DBN modeling of subsea BOP control system
6.3.3 Evaluation and validation
6.3.4 Results and discussions
6.4 Conclusions
References
Chapter 7 Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations
7.1 Introduction
7.2 Proposed methodology
7.3 Case study
7.3.1 Subsea BOP operations
7.3.2 Modeling and analysis
7.3.3 Results and discussions
7.4 Conclusions
References
Chapter 8 Research on the Dynamic Bayesian Networks Based Real-Time Reliability Evaluation Methodology
8.1 Introduction
8.2 Proposed methodology
8.3 Case study
8.3.1 Subsea pipe ram BOP system
8.3.2 Modelling
8.3.3 Results and discussions
8.4 Conclusions
References
Chapter 9 A Dynamic Bayesian Networks Modelling of Human Factors on Offshore Blowouts
9.1 Introduction
9.2 Pseudo-fault tree
9.2.1 Human factors in offshore drilling
9.2.2 Pseudo-fault tree of safety barriers
9.3 Dynamic Bayesian networks
9.3.1 Introduction of dynamic Bayesian networks
9.3.2 Translating pseudo-fault tree into dynamic Bayesian networks
9.3.3 Evaluation and validation of dynamic Bayesian networks
9.4 Results and discussions
9.4.1 Quantitative analysis results
9.4.2 Effect of repair on the HFBF
9.4.3 Mutual information investigation
9.4.4 Validation of the model
9.5 Conclusions
References
Chapter 10 Application of Bayesian Networks to Reliability Evaluation of Software System for Subsea Blowout Preventers
10.1 Introduction
10.2 Software development
10.2.1 Subsea BOP control system
10.2.2 Control logics
10.2.3 HMI programs and remote access
10.2.4 Redundant databases
10.3 BN modeling for reliability analysis
10.3.1 BN modeling for software system
10.3.2 Reliability evaluation
10.3.3 Validation of modeling
10.4 Results and discussions
10.4.1 Probability of software failure
10.4.2 Mutual information investigation
10.4.3 Validation of the model
10.5 Conclusions
References