Dynamic Bayesian network modeling of reliability of subsea blowout preventer stack in presence of common cause failures

A subsea blowout preventer (BOP) stack is used to seal, control and monitor oil and gas wells. It can be regarded as a series–parallel system consisting of several subsystems. This paper develops the dynamic Bayesian network (DBN) of a parallel system with n components, taking account of common cause failures and imperfect coverage. Multiple error shock model is used to model common cause failures. Based on the proposed generic model, DBNs of the two commonly used stack types, namely the conventional BOP and modern BOP are developed. In order to evaluate the effects of the failure rates and coverage factor on the reliability and availability of the stacks, sensitivity analysis is performed.     

基于Matlab的火灾探测报警系统可靠度Monte Carlo仿真

介绍了火灾探测报警系统的结构,指出了火灾探测报警系统失效原因,针对火灾探测报警系统的特点,确定了蒙特卡洛可靠度分析法.给出了系统的概率模型,确定了在Matlab下应用Monte Carlo方法对火灾探测报警系统的可靠度进行仿真的具体步骤,得到火灾探测报警系统可靠度仿真曲线.将仿真结果与试验结果相比较,基本一致,验证了该仿真方法能正确描述火灾探测报警系统的可靠度随使用寿命的变化规律,为进一步应用该方法来分析火灾探测报警系统的的各可靠性指标提供了理论基础.     

Reliability evaluation of fire alarm systems using dynamic Bayesian networks and fuzzy fault tree analysis

In almost all industries, fire alarm systems play a vital role in the reducing the risks associated with fires and damages. Therefore, it is necessary to evaluate their reliability and performance in emergency situations. The present study aimed to use fault tree analysis (FTA) to determine the root causes involved in the failure of fire alarm systems, to use Fuzzy set theory and expert elicitation to determine relative probabilities, and finally, to evaluate the reliability of a fire alarm system using dynamic Bayesian networks (BNs) during a thirty-six months period. A total of 29 basic events were detected from the FT. The reliability of the fire alarm system was estimated at 0.954 according to the FT and 0.957 according to conventional BNs. The reliability of the fire alarm system after 36 months was estimated at 0.375 according to dynamic BNs. All the events involved in the failure of fire alarm systems were drawn in the fault tree diagram. The results indicate that remodeling of these systems and simultaneous construction activities are the most important factors in the failure of the fire alarm system. System reliability can also be increased to 0.965 by providing preventive and control measures to reduce the probability of critical events.     

基于蒙特卡洛方法的着陆擦机尾风险预测

为解决对航班和机队着陆时擦机尾风险客观预测问题，基于飞行QAR数据和蒙特卡洛模拟方法，建立擦机尾风险预测模型，将某航空公司Boeing737-800机队的380套QAR数据作为样本数据，运用MATLAB编程进行5 000次模拟抽样试验，得到不同机队着陆时俯仰角的分布和擦机尾风险预测曲线。研究结果表明:当着陆俯仰角大于4.5°时，机队2擦机尾风险较大；运用蒙特卡洛模拟预测的飞机着陆俯仰角更为稳定和准确。该模型可进一步软件化，为航空公司的擦机尾超限事件管理提供可靠性指标参考，实现对飞行员操作风险的动态管理。     

基于Monte Carlo与Markov法参数不确定性条件下SIL评估

为了避免由参数不确定性因素导致较大的Markov法SIL评估偏差,且减少计算工作量和复杂性,采用Monte Carlo（MC）仿真方法处理含不确定性参数的Markov模型,并借助Matlab GUI编程开发MC仿真处理参数不确定性条件下Markov法SIL评估可视化仿真计算软件。在理论研究的基础上,为说明该研究方法与计算软件的可行性,以石油天然气工业高完整性压力保护系统（HIPPS）为算例进行SIL评估。结果表明:MC仿真方法可以有效处理Markov法SIL评估中参数不确定性问题;基于Matlab GUI编程设计出的仿真计算软件在一定程度上可以提高计算效率。     

Quantitative sensitivity and reliability analysis of sensor networks for well kick detection based on dynamic Bayesian networks and Markov chain

Kick is considered as an early warning sign to the blowout that is among the most undesired and feared accidents during drilling operations. Kick detection system is commonly used to timely identify the occurrence of a kick. The method commonly used for kick detection relies on the proper selection of monitoring indicators. A kick detection system should not only have very high accuracy but also maintain reliable over a long time. Different from the existing studies focusing on improving the detection accuracy, this paper presents a frame emphasizing on quantitatively analyzing and enhancing the reliability of the kick detection sensor networks. The dynamic Bayesian network (DBN) for the sensor networks is established that employs Markov chain to obtain the reliability degradation of measurement sensors over time. The proposed method is applied and evaluated by case studies to conduct reliability and sensitivity analysis for kick detection sensor networks. The reliability analysis results demonstrate that the proposed method can quantitatively analyze the reliability of a kick detection sensor networks consisting of various sensors over given time periods. The sensitivity analysis results indicate that the proposed method is effective in identifying the critical sensors that have the greatest effect on the reliability of one certain kick detection system. Based on the analysis results, optimized logical combination of sensors of a kick detection system can be achieved. An improved sensor network for the unreliable case was proposed and evaluated.     

基于Monte Carlo的海洋平台圆柱落物落点分析及危险区快速评价*

为实现海洋平台落物风险快速精确评价,针对传统推荐性规范经验化、过于简化、平面化及未体现工程随机性等问题,以圆柱落物为研究对象开展先导研究,基于3D运动理论模型,提出1种采用Monte Carlo方法表征工程随机性以实现危险区快速精确评价的方法,自主开发形成海洋平台圆柱落物危险区快速评价工具（MREDP）,着重分析落点分布规律和危险区等级划分。结果表明:落点分布整体呈现“倒工字圆弧型”、两端“一密一疏”、关于X轴对称特点;MREDP快速评价结果可信,评价周期大幅降低;MREDP快速评价危险区相对较大、存在多峰值点,DNVGL-RP-F107推荐危险区相对保守、存在单峰值点。     

基于TF-熵权法的矿井通风系统可靠性可拓评价

为准确评价煤矿通风系统可靠性,提高可靠性评价的客观性与准确性,有效防止因通风系统故障而引起的煤矿安全事故,提出基于三角模糊数(TF)-熵权法的矿井通风系统可靠性可拓评价模型,首先利用德尔菲法从通风动力、通风网络等6个方面,选取29个评价指标确定矿井通风系统可靠性评价指标体系;然后,通过三角模糊数与熵权法相结合的方式确定...     

A reliability evaluation method based on the weakest failure modes for side-by-side offloading mooring system of FPSO

Offshore structures are complex systems, and numerous failure modes must be taken into consideration when reliability analysis in different loads and environment conditions are conducted on them. It is difficult to obtain structural system reliability with respect to complicated systems with numerous failure modes and dependency consideration among them. This paper applies the combination of the weakest failure modes theory with structural reliability theory to conduct reliability analysis on of side-by-side offloading mooring system of FPSO. Firstly, the numerical simulation of the system in different conditions is addressed to acquire the statistical data of time-history stress of components including hawsers, fenders and yoke, based on which, the reliability indexes of all the failure modes and correlation coefficient matrix are derived. Then the weakest failure modes, i.e. the representative failure modes that have significant impact on the system, are located through Probability Network Evaluation Technique. The probability of structural system is estimated through the weakest failure modes by considering the system as series. The analysis results indicate that two environment conditions (0°&0°&0° and 0°&30°&45° in combinations of wave, wind and current) are relative dangerous, which is in good correspondence with the practical expertise. The method is verified to be an effective and convenient evaluation approach for structural reliability analysis in terms of complex systems. It is beneficial for the identification of structure indicators from the weakest failure mode group and conduct optimum of structural system configurations in the design stage.