Application of dynamic risk analysis in offshore drilling processes

Process safety is the common global language used to communicate the strategies of hazard identification, risk assessment and safety management. Process safety is identified as an integral part of process development and focuses on preventing and mitigating major process accidents such as fires, explosions, and toxic releases in process industries. Accident probability estimation is the most vital step to all quantitative risk assessment methods. Drilling process for oil is a hazardous operation and hence safety is one of the major concerns and is often measured in terms of risk. Dynamic risk assessment method is meant to reassess risk in terms of updating initial failure probabilities of events and safety barriers, as new information are made available during a specific operation. In this study, a Bayesian network model is developed to represent a well kick scenario. The concept of dynamic environment is incorporated by feeding the real-time failure probability values (observed at different time intervals) of safety barriers to the Bayesian network in order to obtain the corresponding time-dependent variations in kick consequences. This study reveals the importance of real-time monitoring of safety barrier performances and quantitatively shows the effect of deterioration of barrier performance on kick consequence probabilities. The Macondo blowout incident is used to demonstrate how early warnings in barrier probability variations could have been observed and adequately managed to prevent escalation to severe consequences.     

基于IPSO-BP模型的火灾气体传感器气压补偿算法

为完善飞机火灾检测系统,设计一套方案,模拟试验不同气压下CO、CO2气体传感器采集气体的体积分数值,并与理论值比较,进而提出一种根据粒子适应度值动态调整学习因子的粒子群算法.采用改进的粒子群(IPSO)算法寻找反向传播(BP)神经网络的最优初始权值阈值,再利用寻优后的BP神经网络修正CO、CO2气体传感器的检测结果,消...     

Optimization of gas detector placement considering scenario probability and detector reliability in oil refinery installation

Gas detection system is a critical layer of protection in process safety. Leak scenario probability and detector reliability are two key factors in the optimization of gas detector placement. However, they are easily neglected in previous studies, which may lead to an inaccurate evaluation of the optimization solutions. In this study, a stochastic programming (SP) optimization method is proposed considering these two factors. In order to quantitatively represent the probability of leak scenarios, a complete accident scenario set (CASS) is built combining leak sources and wind fields. Then, the computational fluid dynamics (CFD) method is adopted for consequence modeling of gas dispersion. The Markov model is developed to predict the detector reliability. With the objective of minimal cumulative detection time (MCDT), the SP formulation considering scenario probability and detector reliability (MCDT-SPR) is proposed. By introducing the particle swarm optimization (PSO) algorithm, the optimization formulations can be solved. A case study is investigated on a diesel hydrogenation refining unit. Results validate this approach is promising to improve the detection efficiency. This method is more practical and matching with the actual industrial environment, where the leak scenarios and the detector reliability can change dynamically in real process setting.     

基于无线传感网络的安全监控系统应用研究

对某特种气体厂生产过程中潜在的危险有害因素进行了辨识分析,提出了相应的对策措施,提出了基于MEMS传感器、无线传感网络和3G（GIS、GPS、GPRS）技术的安全监控系统方案,包括安全监控系统架构、MEMS传感器和无线传感网络节点的设置方案、无线传感网络接入和通信机制和安全监控系统的软件功能。所提的技术方案已在该特种气体厂进行示范应用,取得良好运行效果。     

Uncertainty analysis framework for tubular connection sealability of underground gas storage wells

The seal failure of tubing and casing connections compromises underground gas storage well safety. This work proposes a systematic uncertainty analysis framework for connection sealability assessment. The framework covers reliability analysis and reliability sensitivity analysis and attempts to provide more effective support for the reliability design of connection seals. The reliability analysis introduces an adaptive Kriging with stopping criterion P-Monte Carlo simulation (AKP-MCS) method, which can provide a satisfactory estimate of failure probability with a small number of performance function evaluations. This metamodeling technology can effectively reduce the numerical efforts required for the reliability assessment of connection sealability. In the reliability sensitivity analysis, the refined metamodel obtained from the reliability analysis is coupled into a single-loop Monte Carlo simulation (MCS) method. The classifier attribute of this metamodel can meet the requirement of the single-loop MCS method to classify the signs of sampling points. This attribute enables sample matrices to be evaluated on this metamodel instead of the performance function, making the reliability sensitivity assessment more feasible. The proposed method is first demonstrated with four academic examples with promising results. Next, an illustrative tubular connection case is provided. The proposed scheme gives estimates of the failure probability and reliability sensitivity close to the classical model but requires less computational cost. The results of the analysis can provide useful information for the scheme decision-making and reliability optimization of connection seal design.     

建立钢丝绳断丝信号数据库的探讨

钢丝绳是提升、运输及牵引设备中的重要构件,钢丝绳断丝直接影响钢丝绳的强度,霍尔元件的漏磁通法可检测钢丝绳断丝根数和位置。整个系统由检测传感器、漏磁传感器、速度传感器、位移传感器等,数据采集、处理、分析系统,工控机等组成。钢丝绳断丝数据库的主要内容有检测信号的峰值、阈值波宽、钢丝绳直径、钢丝直径等7个基本因素。     

区域轨道交通系统节点重要度及故障恢复研究

为提高区域轨道交通系统(RRTS)正常运营下的可靠性和故障情况下的弹性,分析区域轨道交通网络(RRTN)的拓扑特性,构建拓扑结构弹性评价模型,实现突发条件下系统弹性的定量评价;提出一种基于系统弹性的结构评价方法,根据节点重要度评价,识别出系统关键节点;应用弹性损失三角形,进行多种故障情况下系统最优恢复策略研究。结果表明:RRTN在Space L模型下具有无标度网络特性;系统弹性分析结合科普兰评分法,可以提高RRTS节点重要度评价的准确性;最优故障恢复策略从全局角度寻求不同恢复策略下系统弹性的最大值,优化了RRTS结构弹性和系统恢复能力。     

基于F-R-M法的刚构-连续梁桥施工期结构强度风险分析

为了解决刚构-连续梁桥施工过程中的风险问题,提出了一种神经网络与有限元结合的定量风险分析方法（F-R-M法）。以八盘峡黄河特大桥为工程背景建立有限元模型,运用参数敏感性分析确定出最不利施工阶段的结构强度主要风险随机变量,然后以蒙特卡洛原理为基础,通过径向基（RBF）神经网络计算结构失效概率,最后对桥梁施工风险进行定量分析。结果表明:该桥在最大悬臂状态下,结构应力仿真次数超过 1 000 万次时,梁体在最大悬臂端应力失效概率仅约为 1.17×10-5,失效概率极低。基于参数敏感性的F-R-M法可以快速、较高精度地对高速铁路刚构-连续梁桥施工期结构强度风险进行定量分析。     

基于设备网(DeviceNet)总线技术的矿井瓦斯传感器的设计

介绍瓦斯传感器的国内外研究现状,给出以设备网(DeviceNet)总线构成的瓦斯传感器检测网络结构。简要分析了DeviceNet总线协议特点以及其关键技术,提出具有DeviceNet总线规范的矿井瓦斯传感器的设计及实现方案。硬件电路主要由信号调理电路、数字信号处理器,以及能实现DeviceNet网络通信的接口电路构成,用对象建模的方法构建了矿井瓦斯传感器的对象模型,并用模块化编程技术实现了矿井瓦斯传感器作为一个网络从设备的应用层的软件设计。该传感器作为从设备和带有PCI-CAN网卡的计算机组成主/从连接网络,能对多个矿井瓦斯传感器的数据实现远距离的控制。     

Simulation-based fault propagation analysis—Application on hydrogen production plant

Recently production of hydrogen from water through the Cu–Cl thermochemical cycle is developed as a new technology. The main advantages of this technology over existing ones are higher efficiency, lower costs, lower environmental impact and reduced greenhouse gas emissions. Considering these advantages, the usage of this technology in new industries such as nuclear and oil is increasingly developed. Due to hazards involved in hydrogen production, design and implementation of hydrogen plants require provisions for safety, reliability and risk assessment. However, very little research is done from safety point of view. This paper introduces fault semantic network (FSN) as a novel method for fault diagnosis and fault propagation analysis by using evolutionary techniques like genetic programming (GP) and neural networks (NN), to uncover process variables’ interactions. The effectiveness, feasibility and robustness of the proposed method are demonstrated on simulated data obtained from the simulation of hydrogen production process in Aspen HYSYS^®. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables.     

面向网络的过程工业安全与可靠性分析及实现

根据过程工业安全与可靠性的特点,介绍过程工业几种典型的安全分析与评价方法,建立过程工业的系统可靠性模型和基于Bayes方法的可靠性评价方法。通过收集工业现场的信息,进行安全与可靠性的预评估,建立模型与进行分析,预测后果与评估。过程工业的安全与可靠性分析和评价过程需要处理大量的现场数据信息以及完成复杂的计算,利用工业控制网络,基于B/S与C/S相结合的网络体系结构,提出面向网络的过程工业安全与可靠性模型。运用该模型,构建一个应用于过程工业的安全与可靠性分析与评价系统,描述系统实现的主要功能,该系统的实现是过程工业在安全与可靠性领域开展数字化维护的实际应用。     

A new fault detection method for non-Gaussian process based on robust independent component analysis

Conventional fault detection method based on fast independent component analysis (FastICA) is sensitive to outliers in the modeling data and thus may perform poorly under the adverse effects of outliers. To solve such problem, a new fault detection method for non-Gaussian process based on robust independent component analysis (RobustICA) is proposed in this paper. A RobustICA algorithm which can effectively reduce the effects of outliers is firstly developed to estimate the mixing matrix and extract non-Gaussian feature called independent components (ICs) by robust whitening and robust determination of the maximum non-Gaussian directions. Furthermore, a monitoring statistic for each extracted IC is constructed to detect process faults. Simulations on a simple example of the mixing matrix estimation and a fault detection example in the continuous stirred tank reactor system demonstrate that the RobustICA achieves much higher estimation accuracy for the mixing matrix and the ICs than the commonly used FastICA algorithm, and the RobustICA-based fault detection method outperforms the conventional FastICA-based fault detection method in terms of the fault detection time and fault detection rate.     

石化装置火灾爆炸贝叶斯网络与防护层集成分析

复杂的石油化工装置在运转过程中存在诸多不确定因素,易发生火灾、爆炸等重大事故,给安全生产带来极大威胁。考虑到传统的系统安全分析方法在风险评估中存在一定局限性,引入贝叶斯网络与防护层集成分析模型。应用GeNIe软件将系统故障树转成贝叶斯网络,根据贝叶斯双向推理进行故障预测和诊断,快速识别系统薄弱环节并确定为风险贝叶斯故障节点,结合防护层分析提出相应的独立防护层,确定剩余风险水平。实例应用表明,所构建的贝叶斯网络与防护层集成分析模型对复杂系统进行风险评估是可行的,较传统的事件树、故障树分析方法更加科学、合理。     

声发射检测灵敏度校准声源实验分析

在声发射技术的应用中,传感器的工作频率和灵敏度对AE检测的结果具有决定性的作用,传感器的校准是AE定量研究的基础,也是声发射检测技术研究中的重要课题。根据二级校准的相关要求设计了三个不同声源的实验:落球、断铅和压断毛细玻璃管法。通过重复性实验,测量了不同大小的钢球从不同高度下落;不同粗细、硬度以及不同长度的铅芯的断裂;不同粗细的毛细玻璃管被压断产生的脉冲声源,分别比较以上三个实验在不同状态条件下产生的脉冲声源信号,分析其实时特征和频谱特征(峰值,上升时间),通过计算确定其不确定度和稳定度,总结优化操作方法,同时分析了实验误差。在此基础上得出最适合做声发射传感器灵敏度校准的脉冲声源,为声发射传感器校准中的参考声源提供性能评判的依据。     

基于802.11n无线传感器网络的巷道人员定位技术研究

针对现阶段矿井人员定位系统定位精度低、成本高、可靠性差等问题,提出了一种基于802.11n无线传感器网络技术的真三维人员定位系统,该系统根据功能特征划分为现场设备管理层、系统运行监控层和地面监控管理层。工作时,定位分站将移动节点通过无线网络传输过来的位置坐标信息通过RS-485总线传输到光纤冗余网中,紧接着将位置坐标信息通过CAN现场总线传输到地面数据服务器内进行解压、分析处理和可视化显示。实验结果表明:当加权因子k取1.1时,该系统在井下巷道测试环境中的平均误差仅为1.15 m,且长期运行无故障,具有较高的定位精度和可靠性,降低了成本。     

高速网络环境下的入侵检测

实现高速网络中实时入侵检测与预警已经成为目前网络安全所面临的问题。笔者首先分析了当前入侵检测系统中模式匹配算法 ,对模式匹配算法作了改进 ;接着对Rete算法进行了分析 ,并针对Rete算法的不足 ,在Rete网算法的基础上引入了FRete网算法 ,在此基础上建立了一种基于FRete匹配算法的推理机 ,对可疑事实进行推理 ;然后在此基础上设计了一种基于高速网络环境的入侵检测系统 ;最后建立了对入侵检测系统进行性能测试的仿真实验环境。通过上述的改进 ,提高了入侵检测系统的运行速度和效率 ,能较好地适应高速网络环境下的入侵检测。     

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.     

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.     

Design of Intelligent Fault Diagnostic System (FDS)

This research work presents useful framework and mechanism for integrated fault diagnostic system, or FDS. The proposed system is composed of three major subsystems: fault detection, root cause and consequence analyzer, and maintenance analyzer. Learning mechanisms are proposed to extract knowledge about deviations/failure modes from real time process and equipment monitoring data. Fault semantic network is proposed to represent failure modes and fault propagation models as integrated with process and equipment models. Qualitative rules are defined and associated with fault semantic networks for practical Actual maintenance findings are used to tune training data for more accurate fault detection and root cause and consequence analysis. Case study is used to illustrate the proposed idea.