连续型空间故障树中因素重要度分布的定义与认知

为了解工作环境条件因素对系统故障概率的影响的特征,同时丰富连续型空间故障树(CSFT)的理论框架,提出因素重要度分布的概念。因素重要度分布从经典故障树的概率重要度发展而来,目的是研究系统所处环境因素变化导致系统可靠性变化的程度。给出元件和系统的因素重要度分布概念、公式及所需基础数据,并分析因素重要度分布的正负分布特点。使用该概念研究元件X1和系统T的因素t重要度分布和因素c重要度分布。结果表明:在不同环境中,对于因素t或c变化影响元件或系统的故障概率变化程度是不同的。因素重要度分布有效地表达了t和c对元件或系统的故障概率影响特征。     

航天器总装事故原因事件重要度分析

为解决传统安全性定量分析方法不能描述航天器总装这一复杂人-机-环系统的失效数据波动性和非严格逻辑关系的问题,结合生产实际,采用问卷调查的方法找出影响总装事故的风险因素,即导致事故的事件发生可能性、输入事件对输出事件的影响程度等。在验证调查数据的有效性之后,应用基于模糊数的模糊因果图(FCD),计算某航天器与支架车连接作业的事故风险可能性。提出原因事件重要度的计算方法。通过计算发现,人员注意力和发动机保护罩作用是导致发动机损伤的关键事件。与模糊事故树(FFT)、贝叶斯网络(BN)重要度计算结果对比表明,模糊重要度计算结果能反映事故发生可能性对原因事件发生可能性值的增减的敏感度。     

断裂带中汞的地球化学特征

汞是揭示构造活动及强震孕育机制的重要化学示踪元素.研究断裂带中汞及其同位素,可以示踪地震断裂带汞异常来源、汞的迁移路径,从而揭示流体在地震孕育中的作用.本文总结了汞的观测技术,断裂带岩石、土壤和地下水中汞的特征,概述了汞同位素技术在深部断裂带研究中的进展,以及汞在地震监测和活断层探测方面的应用.断裂带中汞的研究,将有助于厘清汞-深部流体-构造活动-地震的可能关系及其机制,对提升汞在地震监测预测中的研究水平具有重要的理论意义和应用价值.     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

“两带”渗透率分布模型应用及渗透率规律研究*

为了研究覆岩破坏后形成“两带”的渗透率分布规律,根据理论推导应力-应变曲线以及采空区材料力学参数公式,建立“两带”渗透率分布模型,采用COMSOL软件分别对垮落带的渗透率分布和断裂带渗透率分布进行数值模拟。研究结果表明:垮落带渗透率呈“椭圆”分布,渗透率最大可达到1.07×10-6 m2,最小可达到1.5×10-8 m2;随着高度的增加,采空区两侧的渗透率变化幅度增加,中部的渗透率变化值较小。断裂带渗透率呈“铲状”分布,靠近工作面区域渗透率最大,断裂带的渗透率在上下隅角处最大可达到1.8×10-10 m2,垂直方向上随着高度的增加渗透率减小。研究结果可为西部典型浅埋煤层安全高效开采提供现场指导作用。     

基于CYCBD和麻雀搜索算法的滚动轴承故障特征提取方法

目的解决在较强的噪声环境下最大二阶循环平稳盲解卷积(MaximumSecond OrderCyclostationary Blind Deconvolution,CYCBD)算法在滚动轴承故障特征提取时效果欠佳的问题,为滚转尾翼导弹的尾翼滚动轴承故障诊断提供方法参考。方法提出一种利用麻雀搜索算法(SparrowSearchAlgorithm,SSA)优化CYCBD算法的方法,将CYCBD算法解卷积的包络谱熵作为SSA寻优的适应度函数,利用SSA高效地寻找出合适的循环频率以及滤波器长度,选择自适应参数后,再使用CYCBD算法有效解卷得到周期脉冲特征。结果同时对比SSA优化CYCBD前后进行故障特征提取的包络谱图,CYCBD的噪声幅值不超过0.13 m/s^(2),峰值不超过0.29 m/s^(2),用SSA优化CYCBD的噪声幅值不超过0.08 m/s^(2),峰值不超过0.32 m/s^(2),故障频率成分更加突显,无论是噪声幅度,还是峰值幅度特性,均较CYCBD有了较大改善。结论仿真实验验证了SSA优化CYCBD方法能够更加清晰地辨识到故障特征频率及其倍频成分,其具有良好的工程应用前景。     

一次大面积停电事故原因分析与防范措施

电力系统振荡故障引起的全系统周波、电压的剧烈变化,系统瓦解或大面积停电事故时有发生,无论对系统、电气设备还是用户都会造成极大危害.结合齐鲁公司电网2004年11月18日事故进行分析探讨,提出防范措施,以减少此类事故的发生.     

基于可靠性理论的区域大气环境质量风险评价模型探讨

为了用可靠性理论进行区域大气环境质量风险评价．从理论上研究了几个需要解决的基本问题。包括：明确区域环境质量风险评价的研究对象．确定区域环境质量可靠性的内涵,提出区域环境系统的“应力-强度-失效”假说．分析环境系统净化环节闻的串、并联关系。因此．从可靠性的视角,对区域环境质量的复杂性和不确定性进行新的探讨．构建环境科学与可靠性理论联系的桥梁。在理论分析的基础上．对福建省泉港区石化工业基地大气环境质量风险评价问题进行研究。从大气环境污染的主要问题出发。研究区域环境系统对大气污染物的净化环节及其串并联关系。在此基础上．构建区域大气环境污染的故障树,并以区域环境质量的可靠性作为评价指标对区域环境风险问题进行具体评价。这是一种以概率为基础的、新的区域环境质量风险评价模型．对于研究污染型工业系统的环境质量风险具有重要意义。     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

Joint analysis of process and operator performance in chemical process operational safety

Independent studies of case histories by the Health and Safety Commission in the UK and by a Honeywell led industrial consortium world-wide showed that human errors represent the major cause of failure in process plant operation. In contrast to this discovery the majority of previous studies on computer aided systems for fault detection and diagnosis has focused on the process side. This paper presents a methodology, which can involve human factors into the development of systems for automatic identification and diagnosis of abnormal operations and develops methods and techniques that can be used to simultaneously capture, characterise and assess the performance of operators as well as of the process. A joint process–operator simulation platform is developed which is used as a test-bed for carrying out the study. The process part is a simulator, which simulates in high fidelity the dynamic behaviour of the process that is subject to the influence of various disturbances and operators’ interventions. The operator module is developed as a real-time expert system, which emulates operator’s behaviour in interpretation of received signals, and planning and execution of decisions. The interaction between the two modules is managed through an interaction module, which handles the real-time exchange of data using Dynamic Data Exchange. The interaction module also contains the toolkits for analysing the dynamic behaviour of the joint process–operator system. The method and system are illustrated using a simulated case study.