Estimation of Failure Probability in Water Pipes Network Using Statistical Model
《统计学》 各章关键术语(中英文对照)
第二部分 各章关键术语（中英文对照）第1章统计学（statistics）随机性（randomness）描述统计学（descriptive statistics）推断统计学（inferential statistics）总体（population）母体（parent）(parent population）样本、子样（sample）调查对象总体（respondents population）有限总体（finite population）调查的理论总体（survey’s heoretical population）超总体（super population）变量（variable）数据（data）原始数据（original data）派生数据（derived data）定类尺度（nominal scale）定类尺度变量（nominal scale level variable）定类尺度数据（nominal scale level data）定序尺度（ordinal scale）定序尺度变量（ordinal scale level variable）定序尺度数据（ordinal scale level data）定距尺度（interval scale）定距尺度变量（interval scale level variable）定距尺度数据（interval scale level data）定比尺度（ratio scale）定比尺度变量（ratio scale level variable）定比尺度数据（ratio scale level data）分类变量（categorical variable）定性变量、属性变量（qualitative variable）数值变量（numerical variable）定量变量、数量变量（quantitative variable）绝对数变量（absolute number level variable）绝对数数据（absolute number level data）比率变量（ratio level variable）比率数据（ratio level data）实验数据（experimental data）调查数据（survey data）观察数据（observed data）第2章随机性（randomness）随机现象（random phenomenon）随机试验（random experiment）事件（event）基本事件（elementary event）复合事件（union of event）必然事件（certain event）不可能事件（impossible event）基本事件空间（elementary event space）互不相容事件（mutually exclusive events）统计独立（statistical independent）统计相依（statistical dependence）概率（probability）古典方法概率（classical method probability）相对频数方法概率（relative frequency method probability）主观方法概率（subjective method probability）几何概率（geometric probability）条件概率（conditional probability）全概率公式（formula of total probability）贝叶斯公式（Bayes’ formula）先验概率（prior probability）后验概率（posterior probability）随机变量（random variable）离散型随机变量（discrete type random variable）连续型随机变量（continuous type random variable）概率分布（probability distribution）特征数（characteristic number）位置特征数（location characteristic number）数学期望（mathematical expectation）散布特征数（scatter characteristic number）方差（variance）标准差（standard deviation）变异系数（variable coefficient）贝努里分布（Bernoulli distribution）二点分布（two-point distribution）0-1分布（zero-one distribution）贝努里试验（Bernoulli trials）二项分布（binomial distribution）超几何分布（hyper-geometric distribution）正态分布（normal distribution）正态概率密度函数（normal probability density function）正态概率密度曲线（normal probability density curve）正态随机变量（normal random variable）卡方分布（chi-square distribution）F_分布（F-distribution）t_分布（t-distribution）“学生”氏t_分布（Student’s t-distribution）列联表（contingency table）联合概率分布（joint probability distribution）边缘概率分布（marginal probability distribution）条件分布（conditional distribution）协方差（covariance）相关系数（correlation coefficient）第3章统计调查（statistical survey）数据收集（collection of data）统计单位（statistical unit）统计个体（statistical individual）社会经济总体（socioeconomic population）调查对象总体（respondents population）有限总体（finite population）标志（character）标志值（character value）属性标志（attributive character ）品质标志（qualitative character ）数量标志（numerical indication）不变标志（invariant indication）变异（variation）调查条目（item of survey）指标（indicator）统计指标（statistical indicator）总量指标（total amount indicator）绝对数（absolute number）统计单位总量（total amount of statistical unit ）标志值总量（total amount of indication value）（total amount of character value）时期性总量指标（time period total amount indicator）流量指标（flow indicator）时点性总量指标（time point total amount indicator）存量指标（stock indicator）平均指标（average indicator）平均数（average number）相对指标（relative indicator）相对数（relative number）动态相对指标（dynamic relative indicator）发展速度（speed of development）增长速度（speed of growth）增长量（growth amount）百分点（percentage point）计划完成相对指标（relative indicator of fulfilling plan）比较相对指标（comparison relative indicator）结构相对指标（structural relative indicator）强度相对指标（intensity relative indicator）基期（base period）报告期（given period）分组（classification）（grouping）统计分组（statistical classification）（statistical grouping）组（class）（group）分组设计（class divisible design）（group divisible design）互斥性（mutually exclusive）包容性（hold）分组标志（classification character）（grouping character）按品质标志分组（classification by qualitative character）（grouping by qualitative character）按数量标志分组（classification by numerical indication）（grouping by numerical indication）离散型分组标志（discrete classification character）（discrete grouping character）连续型分组标志（continuous classification character）（continuous grouping character）单项式分组设计（single-valued class divisible design）（single-valued group divisible design）组距式分组设计（class interval divisible design）（group interval divisible design）组界（class boundary）（group boundary）频数（frequency）（frequency number）频率（frequency）组距（class interval）（group interval）组限（class limit）（group limit）下限（lower limit）上限（upper limit）组中值（class mid-value）（group mid-value）开口组（open class）（open-end class）（open-end group）开口式分组（open-end grouping）等距式分组设计（equal class interval divisible design）（equal group interval divisible design）不等距分组设计（unequal class interval divisible design）（unequal group interval divisible design）调查方案（survey plan）抽样调查（sample survey）有限总体概率抽样（probability sampling in finite populations）抽样单位（sampling unit）个体抽样（elements sampling）等距抽样（systematic sampling）整群抽样（cluster sampling）放回抽样（sampling with replacement）不放回抽样（sampling without replacement）分层抽样（stratified sampling）概率样本（probability sample）样本统计量（sample statistic）估计量（estimator）估计值（estimate）无偏估计量（unbiased estimator）有偏估计量（biased estimator）偏差（bias）精度（degree of precision）估计量的方差（variance of estimates）标准误（standard error）准确度（degree of accuracy）均方误差（mean square error）估计（estimation）点估计（point estimation）区间估计（interval estimate）置信区间（confidence interval）置信下限（confidence lower limit）置信上限（confidence upper limit）置信概率（confidence probability）总体均值（population mean）总体总值（population total）总体比例（population proportion）总体比率（population ratio）简单随机抽样（simple random sampling）简单随机样本（simple random sample）研究域（domains of study）子总体（subpopulations）抽样框（frame）估计量的估计方差（estimated variance of estimates）第4章频数（frequency）（frequency number）频率（frequency）分布列（distribution series）经验分布（empirical distribution）理论分布（theoretical distribution）品质型数据分布列（qualitative data distribution series）数量型数据分布列（quantitative data distribution series）单项式数列（single-valued distribution series）组距式数列（class interval distribution series）频率密度（frequency density）分布棒图（bar graph of distribution）分布直方图（histogram of distribution）分布折线图（polygon of distribution）累积分布数列（cumulative distribution series）累积分布图（polygon of cumulative distribution）位置特征（location characteristic）位置特征数（location characteristic number）平均值、均值（mean）平均数（average number）权数（weight number）加权算术平均数（weighted arithmetic average）加权算术平均值（weighted arithmetic mean）简单算术平均数（simple arithmetic average）简单算术平均值（simple arithmetic mean）加权调和平均数（weighted harmonic average）加权调和平均值（weighted harmonic mean）简单调和平均数（simple harmonic average）简单调和平均值（simple harmonic mean）加权几何平均数（weighted geometric average）加权几何平均值（weighted geometric mean）简单几何平均数（simple geometric average）简单几何平均值（simple geometric mean）绝对数数据（absolute number data）比率类型数据（ratio level data）中位数（median）众数（mode）耐抗性（resistance）散布特征（scatter characteristic）散布特征数（scatter characteristic number）极差、全距（range）四分位差（quartile deviation）四分间距（inter-quartile range）上四分位数（upper quartile）下四分位数（lower quartile）在外截断点（outside cutoffs）平均差（mean deviation）方差（variance）标准差（standard deviation）变异系数（variable coefficient）第5章随机样本（random sample）简单随机样本（simple random sample）参数估计（parameter estimation）矩（moment）矩估计（moment estimation）修正样本方差（modified sample variance）极大似然估计（maximum likelihood estimate）参数空间（space of paramete）似然函数（likelihood function）似然方程（likelihood equation）点估计（point estimation）区间估计（interval estimation）假设检验（test of hypothesis）原假设（null hypothesis）备择假设（alternative hypothesis）检验统计量（statistic for test）观察到的显著水平（observed significance level）显著性检验（test of significance）显著水平标准（critical of significance level）临界值（critical value）拒绝域（rejection region）接受域（acceptance region）临界值检验规则（test regulation by critical value）双尾检验（two-tailed tests）显著水平（significance level）单尾检验（one-tailed tests）第一类错误（first-kind error）第一类错误概率（probability of first-kind error）第二类错误（second-kind error）第二类错误概率（probability of second-kind error）P_值（P_value）P_值检验规则（test regulation by P_value）经典统计学（classical statistics）贝叶斯统计学（Bayesian statistics）第6章方差分析（analysis of variance，ANOV A）方差分析恒等式（analysis of variance identity equation）单因子方差分析（one-factor analysis of variance）双因子方差分析（two-factor analysis of variance）总变差平方和（total variation sum of squares）总平方和SST（total sum of squares）组间变差平方和（among class(group) variation sum of squares），回归平方和SSR （regression sum of squares）组内变差平方和（within variation sum of squares）误差平方和SSE（error sum of squares）皮尔逊χ2统计量（Pearson’s chi-statistic）分布拟合（fitting of distrbution）分布拟合检验（test of fitting of distrbution）皮尔逊χ2检验（Pearson’s chi-square test）列联表（contingency table）独立性检验（test of independence）数量变量（quantitative variable）属性变量（qualitative variable）对数线性模型（loglinear model）回归分析（regression analysis）随机项（random term）随机扰动项（random disturbance term）回归系数（regression coefficient）总体一元线性回归模型（population linear regression model with a single regressor）总体多元线性回归模型（population multiple regression model with a single regressor）完全多重共线性（perfect multicollinearity）遗漏变量（omitted variable）遗漏变量偏差（omitted variable bias）面板数据（panel data）面板数据回归（panel data regressions）工具变量（instrumental variable）工具变量回归（instrumental variable regressions）两阶段最小平方估计量（two stage least squares estimator）随机化实验（randomized experiment）准实验（quasi-experiment）自然实验（natural experiment）普通最小平方准则（ordinary least squares criterion）最小平方准则（least squares criterion）普通最小平方（ordinary least squares，OLS）最小平方（least squares）最小平方法（least squares method）第7章简单总体（simple population）复合总体（combined population）个体指数：价比（price relative），量比（quantity relative）总指数（general index）（combined index）统计指数（statistical indices）类指数、组指数（class index）动态指数（dynamic index）比较指数（comparison index）计划完成指数（index of fulfilling plan）数量指标指数（quantitative indicator index）物量指数（quantitative index）（quantity index）（quantum index）质量指标指数（qualitative indicator index）价格指数、物价指数（price index）综合指数（aggregative index）（composite index）拉斯贝尔指数（Laspeyres’ index）派许指数（Paasche’s index）阿斯·杨指数（Arthur Young’s index）马歇尔—埃奇沃斯指数（Marshall-Edgeworth’s index）理想指数（ideal index）加权综合指数（weighted aggregate index）平均指数（average index）加权算术平均指数（weighted arithmetic average index）加权调和平均指数（weighted harmonic average index）因子互换（factor-reversal）购买力平价（purchasing power parity，PPP）环比指数（chain index）定基指数（fixed base index）连环替代因素分析法（factor analysis by chain substitution method）不变结构指数、固定构成指数（index of invariable construction）结构指数、结构影响指数（structural index）第8章截面数据（cross-section data）时序数据（time series data）动态数据（dynamic data）时间数列（time series）发展水平（level of development）基期水平（level of base period）报告期水平（level of given period）平均发展水平（average level of development）序时平均数（chronological average）增长量（growth quantity）平均增长量（average growth amount）发展速度（speed of development）增长速度（speed of growth）增长率（growth rate）环比发展速度（chained speed of development）定基发展速度（fixed base speed of development）环比增长速度（chained growth speed）定基增长速度（fixed base growth speed）平均发展速度（average speed of development）平均增长速度（average speed of growth）平均增长率（average growth rate）算术图（arithmetic chart）半对数图（semilog graph）时间数列散点图（scatter diagram of time series）时间数列折线图（broken line graph of time series）水平型时间数列（horizontal patterns in time series data）趋势型时间数列（trend patterns in time series data）季节型时间数列（season patterns in time series data）趋势—季节型时间数列（trend-season patterns in time series data）一次指数平滑平均数（simple exponential smoothing mean）一次指数平滑法（simple exponential smoothing method）最小平方法（leas square method）最小平方准则（least squares criterion）原资料平均法（average of original data method）季节模型（seasonal model）（seasonal pattern）长期趋势（secular trends）季节变动（变差）（seasonal variation）季节波动（seasonal fluctuations）不规则变动（变差）（erratic variation）不规则波动（random fluctuations）时间数列加法模型（additive model of time series）时间数列乘法模型（multiplicative model of time series）11。
外文翻译外文文献英文文献国际建设工程风险分析
外文文献：This analysis used a case study methodology to analyze the issues surrounding the partial collapse of the roof of a building housing the headquarters of the Standards Association of Zimbabwe (SAZ). In particular, it examined the prior roles played by the team of construction professionals. The analysis revealed that the SAZ’s traditional construction project was generally characterized by high risk. There was a clear indication of the failure of a contractor and architects in preventing and/or mitigating potential construction problems as alleged by the plaintiff. It was reasonable to conclude that between them the defects should have been detected earlier and rectified in good time before the partial roof failure. It appeared justified for the plaintiff to have brought a negligence claim against both the contractor and the architects. The risk analysis facilitated, through its multi-dimensional approach to a critical examination of a construction problem, the identification of an effective risk management strategy for future construction prject and riskThe structural design of the reinforced concrete elements was done by consulting engineers Knight Piesold (KP). Quantity surveying services were provided by Hawkins, Leshnick & Bath (HLB). The contract was awarded to Central African Building Corporation (CABCO) who was also responsible for the provision of a specialist roof structure using patented “gang nail” roof trusses. The building construction proceeded to completion and was handed over to the owners on Sept. 12, 1991. The SAZ took effective occupation of the headquarters building without a certificate of occupation. Also, the defects liability period was only three months .The roof structure was in place 10 years At first the SAZ decided to go to arbitration, but this failed to yield an immediate solution. The SAZ then decided toproceed to litigate in court and to bring a negligence claim against CABCO. The preparation for arbitration was reused for litigation. The SAZ’s quantified losses stood at approximately $ 6 million in Zimbabwe dollars (US $1.2m) .After all parties had examined the facts and evidence before them, it became clear that there was a great probability that the courts might rule that both the architects and the contractor were lia ble. It was at this stage that the defendants’ lawyers requested that the matter be settled out of court. The plaintiff agreed to this suxamined the prior roles played by the project management function and construction professionals in preventing/mitigating potential construction problems. It further assessed the extent to which the employer/client and parties to a construction contract are able to recover damages under that contract. The main objective of this critical analysis was to identify an effective risk management strategy for future construction projects. The importance of this study is its multidimensional examination approach.Experience sugge be misleading. All construction projects are prototypes to some extent and imply change. Change in the construction industry itself suggests that past experience is unlikely to be sufficient on its own. A structured approach is required. Such a structure can not and must not replace the experience and expertise of the participant. Rather, it brings additional benefits that assist to clarify objectives, identify the nature of the uncertainties, introduces effective communication systems, improves decision-making, introduces effective risk control measures, protects the project objectives and provides knowledge of the risk history .Construction professionals need to know how to balance the contingencies of risk with their specific contractual, financial, operational and organizational requirements. Many construction professionals look at risks in dividually with a myopic lens and donot realize the potential impact that other associated risks may have on their business operations. Using a holistic risk management approach will enable a firm to identify all of the organization’s business risks. This will increas e the probability of risk mitigation, with the ultimate goal of total risk elimination .Recommended key construction and risk management strategies for future construction projects have been considered and their explanation follows. J.W. Hinchey stated th at there is and can be no ‘best practice’ standard for risk allocation on a high-profile project or for that matter, any project. He said, instead, successful risk management is a mind-set and a process. According to Hinchey, the ideal mind-set is for the parties and their representatives to, first, be intentional about identifying project risks and then to proceed to develop a systematic and comprehensive process for avoiding, mitigat and its location. This is said to be necessary not only to allow alternative responses to be explored. But also to ensure that the right questions are asked and the major risks identified. Heads of sources of risk are said to be a convenient way of providing a structure for identifying risks to completion of a participant’s pa rt of the project. Effective risk management is said to require a multi-disciplinary approach. Inevitably risk management requires examination of engineering, legal and insurance related solutions .It is stated that the use of analytical techniques based on a statistical approach could be of enormous use in decision making . Many of these techniques are said to be relevant to estimation of the consequences of risk events, and not how allocation of risk is to be achieved. In addition, at the present stage of the development of risk management, Atkinson states that it must be recognized that major decisions will be made that can not be based solely on mathematical analysis. The complexity ofconstruction projects means that the project definition in terms of both physical form and organizational structure will be based on consideration of only a relatively small number of risks . This is said to then allow a general structured approach that can be applied to any construction project to increase the awareness of participants .The new, simplified Construction Design and Management Regulations (CDM Regulations) which came in to f 1996, into a single regulatory package.The new CDM regulations offer an opportunity for a step change in health and safety performance and are used to reemphasize the health, safety and broader business benefits of a well-managed and co-ordinated approach to the management of health and safety in construction. I believe that the development of these skills is imperative to provide the client with the most effective services available, delivering the best value project possible.Construction Management at Risk (CM at Risk), similar to established private sector methods of construction contracting, is gaining popularity in the public sector. It is a process that allows a client to select a construction manager (CM) based on qualifications; make the CM a member of a collaborative project team; centralize responsibility for construction under a single contract; obtain a bonded guaranteed maximum price; produce a more manageable, predictable project; save time and money; and reduce risk for the client, the architect and the CM.CM at Risk, a more professional approach to construction, is taking its place along with design-build, bridging and the more traditional process of design-bid-build as an established method of project delivery.The AE can review to get the projec. Competition in the community is more equitable: all subcontractors have a fair shot at the work .A contingency within the GMP covers unexpected but justifiable costs, and a contingency above the GMP allows for client changes. As long as the subcontractors are within the GMP they are reimbursed to the CM, so the CM represents the client in negotiating inevitable changes with subcontractors.There can be similar problems where each party in a project is separately insured. For this reason a move towards project insurance is recommended. The traditional approach reinforces adversarial attitudes, and even provides incentives for people to overlook or conceal risks in an attempt to avoid or transfer responsibility.A contingency within the GMP covers unexpected but justifiable costs, and a contingency above the GMP allows for client changes. As long as the subcontractors are within the GMP they are reimbursed to the CM, so the CM represents the client in negotiating inevitable changes with subcontractors.There can be similar problems where each party in a project is separately insured. For this reason a move towards project insurance is recommended. The traditional approach reinforces adversarial attitudes, and even provides incentives for people to overlook or conceal risks in an attempt to avoid or transfer responsibility.It was reasonable to assume that between them the defects should have been detected earlier and rectified in good time before the partial roof failure. It did appear justified for the plaintiff to have brought a negligence claim against both the contractor and the architects.In many projects clients do not understand the importance of their role in facilitating cooperation and coordination; the desi recompense. They do not want surprises, and are more likely to engage in litigation when things go wrong.中文译文：国际建设工程风险分析索赔看来是合乎情理的。
几种常见的概率模型及应用
几种常见的概率模型及应用Common Probability Models and Their Applications.Probability models are mathematical representations of random phenomena that allow us to make predictions and inferences about future events. They are widely used in various fields, including statistics, machine learning, finance, and biology. Here are some of the most commonly used probability models and their applications:1. Binomial Model.The binomial model describes the probability of success in a sequence of independent trials, each of which has a constant probability of success. It is commonly used in situations where we are interested in the number of successes in a fixed number of trials, such as:Counting the number of defective items in a batch of production.Predicting the number of customers visiting a store in a particular day.Estimating the probability of winning a lottery.2. Poisson Model.The Poisson model describes the probability of observing a random number of events occurring over a fixed period of time or distance. It is often used in situations where the occurrence of events is rare and independent of each other, such as:Modeling the number of phone calls received by a call center in an hour.Estimating the number of accidents on a particular highway per week.Predicting the number of mutations in a DNA sequence.3. Normal Distribution.The normal distribution, also known as the Gaussian distribution, is a continuous probability distribution that describes the distribution of continuous variables that are normally distributed, such as:Heights of individuals.Weights of products.Test scores of students.It is widely used in statistical inference, hypothesis testing, and estimation of population parameters.4. Exponential Distribution.The exponential distribution is a continuousprobability distribution that describes the waiting time between events that occur randomly and independently at a constant rate. It is commonly used in situations where thetime between events is of interest, such as:Modeling the time between arrivals of customers in a queue.Estimating the time to failure of a machine.Predicting the lifespan of a light bulb.5. Markov Models.Markov models are a class of stochastic processes that describe the evolution of a system over time. They are defined by the current state of the system and the probability of transitioning to each possible next state. Markov models are widely used in various applications, such as:Modeling speech and language recognition.Simulating financial markets.Predicting customer behavior.中文回答：常见的概率模型及其应用。
FailureModeandEffectsAnalysis失效模式和影响分析
.1.Task / Objective目的-To define and establish basic requirements and a process to prepare, execute and follow-up Failure Mode and Effects Analyses.为了规定建立基本的要求和程序以便于对失效模式及后果分析的准备、执行和跟踪。
2.Scope of Application范围-Avim solar production Co.,Ltd, Gaomi/China埃孚光伏制造有限公司高密/中国3.Definitions术语和定义FMEA失效模式及后果分析The Failure Mode and Effects Analysis (FMEA) is an analytical method of preventive quality assurance. It helps to identify and evaluate risks in time, and to initiate or propose suitable actions for risk minimization. The FMEA consists of 5 steps: Structural analysis, functional analysis, failure analysis, risk assessment and optimization.失效模式及后果分析是预防性品质保障的一种分析方法，它可以帮助及时识别并评估风险，并启动或提议适当的行动把风险降低最低限度。
失效模式及后果分析有五个步骤，即结构分析，功能分析，失效分析，风险评估及优化。
Product FMEA产品的失效模式及后果分析The Product FMEA analyses the design of products, product parts and their interfaces with regard to their quality over the whole product life cycle.产品的FMEA是从产品寿命周期的角度来分析产品、产品部件、以及部件接合处的设计。
SLIDE软件说明翻译
SLIDE软件基本介绍说明SLIDE is a 2D slope stability program for evaluating the safety factor or probability of failure, of circular or non-circular failure surfaces in soil or rock slopes. SLIDE is very simple to use, and yet complex models can be created and analyzed quickly and easily. External loading, groundwater and support can all be modeled in a variety of ways.SLIDE是一个计算土、石质二维边坡稳定的程序，可计算边坡的安全系数、可能的破坏、可分析圆弧与非圆弧的潜在破坏滑动面。
SLIDE非常便于操作应用，即使复杂的模型也可以迅速简便的建立和计算分析。
外界荷载、地下水、支撑物都可以用不同的方式模拟。
SLIDE analyzes the stability of slip surfaces using vertical slice limit equilibrium methods (eg. Bishop, Janbu, Spencer etc). Individual slip surfaces can be analyzed, or search methods can be applied to locate the critical slip surface for a given slope. Deterministic (safety factor) or probabilistic (probability of failure) analyses can be carried out.SLIDE应用建立在极限平衡上面的竖向条分法（例如Bishop, Janbu, Spencer等不同的方法）来计算边坡的稳定。
常用危重症病情评分方法
常用危重症病情评分方法一．创伤严重程度评分和结局预测软件创伤严重程度评分方法1. 简明损伤程度评分（Abbreviated Injury Severity，AIS -2005版），评价创伤严重程度（解剖学角度）2. 损伤程度计分法和新损伤程度（Injury Severity Score——News Injury Severity Score，ISS & NISS），评价损伤程度严重程度（解剖学角度）3. 昏迷程度评价方法（Glasgow coma scale，GCS），评价中枢神经系统损害程度4. 创伤程度评价方法（Trauma Score，TS），评价创伤程度严重程度（解剖学，生理学角度）5. 修正创伤评价方法（Revised Trauma Score，RTS），评价损伤程度严重程度6. 小儿损伤程度评分（Paediatric Trauma Score，PTS），评价小儿损伤程度（解剖学，生理学角度）7. 院前创伤指数（Prehospital Index，PHI）8. 腹部创伤指数（Abdominal Trauma Index，ATI），评价腹部创伤程度9. 贯穿性腹部创伤指数（Penetrating Abdominal Trauma Index，PATI）10. 肺爆震伤评分（Respiratory Severity Score in Acute Blast Injury）11. 截肢指数（Limb Salvage Index，LSI）12. 下肢开放性骨折计分（NISSSA score for grading the severity of an open fracture of the lower extrmity）13. 骨盆骨折内脏伤计分（Risk of Visceral Injury with Pelvic Fracture）14. 脏器损伤计分（Organ Injury Scaling，OIS）15. 预测创伤病人的结局16. 创伤病人存活概率预测方法（Trauma Injury Severity Score , Surivival Probability，TRISS），预测创伤病人的结局（存活概率）－美国版和中国版17. 创伤严重程度特征评价方法（A Severity Characterization of Trauma, Surivival Probability ，ASCOT），预测创伤病人的结局（存活概率, Ps），美国版和中国版18. 烧伤病人结局预测方法二．综合性急危重症评分软件适用于各专科的危重病人、创伤病人、手术后病人，如ICU .CCU、SICU等。
基于风险分析检验 API580-Chapter 10
10 Assessing Probability of Failure (POF)失效概率评估Contents10 Assessing Probability of Failure10.1 Introduction to Probability Analysis10.2 Units of Measure in the POF Analysis10.3 Types of Probability Analysis10.4 Determination of POF10.1 Introduction to Probability Analysis 概率分析介绍The probability analysis in an RBI program is performed to estimate the probability of a specific adverse consequence resulting from a loss of containment that occurs due to a damage mechanism(s).RBI 程序的概率分析是评估一个特点的因一或多个损伤机理, 造成受压设备溶液流失的逆向结果.The probability that a specific consequence will occur is the product of the POF and the probability of the scenario under consideration assuming that the failure has occurred. This section provides guidance only on determining the POF. Guidance on determining the probability of specific consequences is provided in Section 12. 将发生的特定结果的概率依故障概率与发生故障后正在审议的方案中的概率Key words: Probability of specific consequence, POF, probability of the scenario.POFThe probability of the scenario under considerationThe probability ofspecific consequenceThe probability that a specific consequence will occur is the product of the POF and the probability of the scenariounder consideration/others/ndt_pressure_testing.htmlThe probability of specific consequenceThe probability of the scenario under considerationPOFThe probability ofspecific consequenceThe probability of the scenario underconsiderationPOFThe probability of specific consequenceThe probability of the scenario underconsiderationPOFThere could bemany scenarioGuidance on determining the probability of specific consequences is provided in Section 12.The POF analysis should address all damage mechanisms to which the equipment being studied is or can be susceptible. Further, it should address the situation where equipment is or can be susceptible to multiple damage mechanisms (e.g. thinning and creep). The analysis should be credible 可信, repeatable 能重复and documented 必须记录. It should be noted that damage mechanisms are not the only causes of loss of containment. Other causes of loss of containment could include but are not limited to: 但应注意的是, 损伤机理并不是唯一导致溶液流失的原因, 其他原因有a)seismic activity, 地震活动b)weather extremes, 极端天气c)overpressure due to pressure-relief device failure, 过压,由于减压装置故障d)operator error, 操作失误e)inadvertent substitution of materials of construction, 施工材料无意取代f)design error, 设计错误g)sabotage. 破坏活动These and other causes of loss of containment may have an impact on the POF and may be (but typically are not) included in the POF analysis for RBI.seismic activityweather extremesoverpressure (under pressure) due to pressure-relief device failure,design error/wiki/Military_use_of_childrensabotage10.2 Units of Measure in the POF Analysis 故障概率分析,的计量单位POF is typically expressed in terms of frequency. Frequency is expressed as a number of events occurring during a specific time frame. For probability analysis, the time frame is typically expressed as a fixed interval (e.g. one year) and the frequency is expressed as events per interval (e.g. 0.0002 failures per year). The time frame may also be expressed as an occasion (e.g.one run length) and the frequency would be events per occasion (e.g. 0.03 failures per run).Time Frame 期限:(1) fixed interval 固定时间间隔(2) Occasion 时机(per run etc.)故障概率(POF) 一般上是以频率表示,频率是在特定的时间范围内发生的事件的数次.For a qualitative analysis, the POF may be categorized (e.g. high, medium and low, or one through five). However, even in this case, it is appropriate to associate an event frequency with each probability category to provide guidance to the individuals who are responsible for determining the probability. If this is done, the change from one category to the next could be one or more orders of magnitude or other appropriate demarcations that will provide adequate discrimination. 个别类别(高低等)的相应特定概率(数次)关联, 有助于, 有效, 可信的故障频率计量的分配与变动.Two examples of this are listed in Table 1 and Table 2. Table 1—Three Levels of POFTable 2—Six Levels of POF10.3 Types of Probability Analysis 概率分析的类型10.3.1 General 概要The following paragraphs discuss different approaches to the determination of probability. For the purposes of the discussion, these approaches have been categorized as “qualitative”or “quantitative.”However, it should be recognized that “qualitative”and “quantitative”are the end points of a continuum rather than distinctive approaches (see Figure 3). Most probability assessments use a blend of qualitative and quantitative approaches. 大多数概率评估使用混合, 定性和定量方法The methodology used for the assessment should be structured such that a sensitivity analysis or other approach may be used to assure that realistic, though conservative, probability values are obtained (see 12.4).用于评估的方法,应该灵活操作; 灵敏度分析或其它的方法也可以并用, 以确保获得真实的, 尽管保守的概率值.10.3.2 Qualitative POF Analysis 故障概率定性分析A qualitative method involves identification of the units, systems or equipment, the materials of construction and the corrosive components of the processes. On the basis of knowledge of the operating history, future inspection and maintenance plans and possible materials deterioration, POF can be assessed separately for each unit, system, equipment grouping or individual equipment item. Engineering judgment is the basis for this assessment. A POF category can then be assigned for each unit, system, grouping or equipment item. Depending on the methodology employed, the categories may be described with words (such as high, medium, or low) or may have numerical descriptors (such as 0.1 to 0.01 times per year). 定性分析考虑包括识别单元,系统或设备,建材和腐蚀. 工程判断是评估依据. 计量单位可以为描述性如, 高/中/低或数值描述如0.1~0.01次数/年10.3.3 Quantitative POF Analysis 故障概率定量分析There are several approaches to a quantitative probability analysis. One example is to take a probabilistic approach where specific failure data or expert solicitations are used to calculate a POF. These failure data may be obtained on the specific equipment item in question or on similar equipment items. This probability may be expressed as a distribution rather than a single deterministic value.概率方法计算方法, 故障数据可在特定有问题的设备项目或类似设备项目获得. 得到的概率可以为一个分布来表示, 而不是一个单一的确定性概率值Another approach is used when inaccurate or insufficient failure data exists on the specific item of interest. In this case, general industry, company or manufacturer failure data are used.A methodology should be applied to assess the applicability of these general data. As appropriate, these failure data should be adjusted and made specific to the equipment being analyzed by increasing or decreasing the predicted failure frequencies based on equipment specific information. In this way, general failure data are used to generate an adjusted failure frequency that is applied to equipment for a specific application. Such modifications to general values may be made for each equipment item to account for the potential deterioration that may occur in the particular service and the type and effectiveness of inspection and/or monitoring performed. Knowledgeable personnel should make these modifications on a case-bycase basis.当不准确或不足的故障数据时, 另一种方法是使用一般行业,公司或制造商的故障数据.在适当情况下, 这些故障数据应按照正在分析的具体的设备进行调整. 这数据调整工作应当是由有相关知识的人员进行.故障概率定量分析有两种数据来源;⏹明确的设备故障数据概率方法计算; 故障数据可在特定有问题的设备项目或类似设备项目获得.分析到的概率可以一个分布来表示, 而不是单一的确定性概率值⏹一般行业,公司或制造商的故障数据当不准确或不足的故障数据时, 另一种方法是使用一般行业,公司或制造商的故障数据.在适当情况下, 这些故障数据应按照正在分析的具体的设备进行调整. 这数据调整工作应当是由有相关知识的人员进行.10.4 Determination of POF 确定故障概率10.4.1 General 总则Regardless of whether a more qualitative or a quantitative analysis is used, the POF is determined by two main considerations: 无论是是定性或定量分析, 故障概率的确定的两个因素是a)damage mechanisms and rates of the equipment item’s material ofconstruction, resulting from its operating environment (internal andexternal); 运作环境(内/外)所导致的损伤机理与腐蚀率b)effectiveness of the inspection program to identify and monitor the damagemechanisms so that the equipment can be repaired or replaced prior to failure.检查程序对损伤机理的识别和监控的有效性.使设备在在失效前能够进行修理或更换.Deterioration RateTimeProbability of Failure POF Damage Tolerance LimitPlanned InspectionUnacceptableinspection intervalFailure occurredMitigationAnalyzing the effect of in-service deterioration and inspection on the POF involves the following steps. 在分析在(1) 职恶化和(2) 检查对POF的影响包括以下步骤a)Identify active and credible damage mechanisms that are reasonablyexpected to occur during the time period being considered (considering normal and upset conditions). 识别在考虑时间段, 合理预期可能产生的可信损伤机理b) Determine the deterioration susceptibility and rate. For example, a fatiguecrack is driven by cyclic stress; corrosion damage is driven by thetemperature, concentration of corrosive, corrosion current, etc.A damage accumulation rule may be available to mathematically model this process.Rather than a given value of the magnitude of the damage mechanism driving forces, a statistical distribution of these forces may be available (see API 579-1/ASME FF2-1). 确定的恶化易感性和速率. 这可以通过损伤累积规则运用数学模拟过程计算数据. 除了确实值, 也能以统计分布体现数据.API 579-1/ASME FF2-1为例子.c)Using a consistent approach, quantify the effectiveness of the pastinspection, maintenance and process monitoring program and a proposed future inspection, maintenance and process monitoring program. It isusually necessary to evaluate the POF considering several alternativefuture inspection and maintenance strategies, possibly including a “noinspection or maintenance”strategy.使用一致的方法, 量化的过去的策略效力(检验/ 维护/ 工艺控制) 与未来预期成效. 评价POF也应考虑几种可供选择的未来的检查和维护策略, 可能包括-“不检查或维修”战略d)Determine the probability that with the current condition, continueddeterioration at the predicted/expected rate will exceed the damagetolerance of the equipment and result in a failure. The failure mode (e.g.small leak, large leak, equipment rupture) should also be determined based on the damage mechanism. It may be desirable in some cases todetermine the probability of more than one failure mode and combine the risks. 确定在当前的情况下, 持续恶化将超过设备的损伤容限, 并导致失效的概率. 相应失效模式也应根据损伤机制确定. 在某些情况, 可能会有多个失效模式, 并结合有关风险来分析.Deterioration Rate Time Probability of Failure POF Damage Tolerance LimitPlanned Inspection Extended inspection interval failure mode: Crack open if failure occurred leading to different failure scenarios thus consequencesFailure occurredMitigation Interval sufficient to detect pitting & SCC Extended Interval10.4.2 Determine the Deterioration Susceptibility and Rate确定退化敏感性和速率Combinations of process conditions and materials of construction for each equipment item should be evaluated to identify active and credible damage mechanisms. One method of determining these mechanisms and susceptibility is to group components that have the same material of construction and are exposed to the same internal and external environment. Inspection results from one item in the group can be related to the other equipment in the group.每个设备的工艺条件和材料组合应进行评估, 以确定活跃和可信的损伤机制. 设备拥有共同工艺条件和材料组合可以归类为一组, 从改组的一个设备检验结构同时能运用在其他在组里的设备.For many damage mechanisms, the rate of damage progression is generally understood and can be estimated for process plant equipment. Deterioration rate can be expressed in terms of corrosion rate for thinning or susceptibility for mechanisms where the deterioration rate is unknown or immeasurable (such as stress corrosion cracking).Susceptibility is often designated as high, medium or low based on the environmental conditions and material of construction combination. Fabrication variables and repair history are also important.设施设备的许多损伤机制, 损伤进展的速度一般都能探知和可可靠地估计. 劣化率可以腐蚀速率表示(mm/yr) 或未知或无法估量的劣化率(如应力腐蚀开裂等), 以高,中或低定位.The deterioration rate in specific process equipment is often not known with certainty. The ability to state the rate of deterioration precisely is affected by equipment complexity, type of damage mechanism, process andmetallurgical variations, inaccessibility for inspection, limitations ofinspection and test methods and the inspector’s expertise. 在特定的工艺设备恶化率往往不能真确的确知. 不确知的因素有⏹equipment complexity,设备的复杂性⏹type of damage mechanism,损伤机理类型⏹process and metallurgical variations, 工艺和冶金的变化⏹inaccessibility for inspection,难以进行检查⏹limitations of inspection and test methods and 检查和测试方法的局限性⏹the inspector’s expertise 检查员的专业知识equipment complexity,设备的复杂性type of damage mechanism,损伤机理类型process and metallurgical variations, 工艺和冶金的变化。
Financial Ratios as Predictors of Failure
71
bers consist of financial statement items. A third term, predictive ability, also reqiiires explanation but cannot be defined briefly. The various dimensions of predictive ability will be explored later.
非劣
Numeric Re sults for Non-Inferiority Te sts Ba sed on the Difference: P1 - P2H0: P1-P2<=D0. H1: P1-P2=D1>D0. Te st Statistic: Z te st (unpooled)Sample Sample Equiv. Actual Equiv. ActualSize Size Grp 2 Grp 1 Grp 1 Margin MarginGrp 1 Grp 2 Prop Prop Prop Diff Diff Target ActualPower N1 N2 P2 P1.0 P1.1 D0 D1 Alpha Alpha Beta 0.8013 619 207 0.5000 0.4000 0.5000 -0.1000 0.0000 0.0500 0.1987 0.8006 763 255 0.5000 0.4100 0.5000 -0.0900 0.0000 0.0500 0.1994 0.8009 967 323 0.5000 0.4200 0.5000 -0.0800 0.0000 0.0500 0.1991 0.8002 1261 421 0.5000 0.4300 0.5000 -0.0700 0.0000 0.0500 0.1998 0.8002 1717 573 0.5000 0.4400 0.5000 -0.0600 0.0000 0.0500 0.1998 0.8002 2473 825 0.5000 0.4500 0.5000 -0.0500 0.0000 0.0500 0.1998 0.8014 613 205 0.5500 0.4500 0.5500 -0.1000 0.0000 0.0500 0.1986 0.8000 755 252 0.5500 0.4600 0.5500 -0.0900 0.0000 0.0500 0.2000 0.8000 955 319 0.5500 0.4700 0.5500 -0.0800 0.0000 0.0500 0.2000 0.8004 1249 417 0.5500 0.4800 0.5500 -0.0700 0.0000 0.0500 0.1996 0.8001 1699 567 0.5500 0.4900 0.5500 -0.0600 0.0000 0.0500 0.1999 0.8003 2449 817 0.5500 0.5000 0.5500 -0.0500 0.0000 0.0500 0.1997 0.8001 593 198 0.6000 0.5000 0.6000 -0.1000 0.0000 0.0500 0.1999 0.8008 733 245 0.6000 0.5100 0.6000 -0.0900 0.0000 0.0500 0.1992 0.8008 928 310 0.6000 0.5200 0.6000 -0.0800 0.0000 0.0500 0.1992 0.8001 1210 404 0.6000 0.5300 0.6000 -0.0700 0.0000 0.0500 0.1999 0.8002 1648 550 0.6000 0.5400 0.6000 -0.0600 0.0000 0.0500 0.1998 0.8002 2374 792 0.6000 0.5500 0.6000 -0.0500 0.0000 0.0500 0.1998 0.8005 562 188 0.6500 0.5500 0.6500 -0.1000 0.0000 0.0500 0.1995 0.8005 694 232 0.6500 0.5600 0.6500 -0.0900 0.0000 0.0500 0.1995 0.8010 880 294 0.6500 0.5700 0.6500 -0.0800 0.0000 0.0500 0.1990 0.8001 1147 383 0.6500 0.5800 0.6500 -0.0700 0.0000 0.0500 0.1999 0.8000 1563 521 0.6500 0.5900 0.6500 -0.0600 0.0000 0.0500 0.2000 0.8003 2251 751 0.6500 0.6000 0.6500 -0.0500 0.0000 0.0500 0.1997 0.8014 520 174 0.7000 0.6000 0.7000 -0.1000 0.0000 0.0500 0.1986 0.8002 640 214 0.7000 0.6100 0.7000 -0.0900 0.0000 0.0500 0.1998 0.8004 811 271 0.7000 0.6200 0.7000 -0.0800 0.0000 0.0500 0.1996 0.8005 1060 354 0.7000 0.6300 0.7000 -0.0700 0.0000 0.0500 0.1995 0.8000 1442 481 0.7000 0.6400 0.7000 -0.0600 0.0000 0.0500 0.2000 0.8002 2077 693 0.7000 0.6500 0.7000 -0.0500 0.0000 0.0500 0.1998 0.8006 463 155 0.7500 0.6500 0.7500 -0.1000 0.0000 0.0500 0.1994 0.8000 571 191 0.7500 0.6600 0.7500 -0.0900 0.0000 0.0500 0.2000 0.8005 724 242 0.7500 0.6700 0.7500 -0.0800 0.0000 0.0500 0.1995 0.8004 946 316 0.7500 0.6800 0.7500 -0.0700 0.0000 0.0500 0.1996 0.8004 1288 430 0.7500 0.6900 0.7500 -0.0600 0.0000 0.0500 0.1996 0.8003 1855 619 0.7500 0.7000 0.7500 -0.0500 0.0000 0.0500 0.1997 0.8001 395 132 0.8000 0.7000 0.8000 -0.1000 0.0000 0.0500 0.1999 0.8000 487 163 0.8000 0.7100 0.8000 -0.0900 0.0000 0.0500 0.2000 0.8013 619 207 0.8000 0.7200 0.8000 -0.0800 0.0000 0.0500 0.1987 0.8009 808 270 0.8000 0.7300 0.8000 -0.0700 0.0000 0.0500 0.1991 0.8004 1099 367 0.8000 0.7400 0.8000 -0.0600 0.0000 0.0500 0.1996 0.8002 1582 528 0.8000 0.7500 0.8000 -0.0500 0.0000 0.0500 0.1998 0.8024 316 106 0.8500 0.7500 0.8500 -0.1000 0.0000 0.0500 0.1976 0.8002 388 130 0.8500 0.7600 0.8500 -0.0900 0.0000 0.0500 0.1998 0.8013 493 165 0.8500 0.7700 0.8500 -0.0800 0.0000 0.0500 0.1987 0.8005 643 215 0.8500 0.7800 0.8500 -0.0700 0.0000 0.0500 0.1995 0.8001 876 292 0.8500 0.7900 0.8500 -0.0600 0.0000 0.0500 0.1999 0.8003 1261 421 0.8500 0.8000 0.8500 -0.0500 0.0000 0.0500 0.1997Numeric Re sults for Non-Inferiority Te sts Ba sed on the Difference: P1 - P2H0: P1-P2<=D0. H1: P1-P2=D1>D0. Te st Statistic: Z te st (unpooled)Sample Sample Equiv. Actual Equiv. ActualSize Size Grp 2 Grp 1 Grp 1 Margin MarginGrp 1 Grp 2 Prop Prop Prop Diff Diff Target ActualPower N1 N2 P2 P1.0 P1.1 D0 D1 Alpha Alpha Beta 0.8030 223 75 0.9000 0.8000 0.9000 -0.1000 0.0000 0.0500 0.1970 0.8009 274 92 0.9000 0.8100 0.9000 -0.0900 0.0000 0.0500 0.1991 0.8002 348 116 0.9000 0.8200 0.9000 -0.0800 0.0000 0.0500 0.1998 0.8010 454 152 0.9000 0.8300 0.9000 -0.0700 0.0000 0.0500 0.1990 0.8013 619 207 0.9000 0.8400 0.9000 -0.0600 0.0000 0.0500 0.1987 0.8001 889 297 0.9000 0.8500 0.9000 -0.0500 0.0000 0.0500 0.1999 0.8013 80 27 0.9500 0.8500 0.9500 -0.1000 0.0000 0.0500 0.0348 0.1987 0.8035 145 49 0.9500 0.8600 0.9500 -0.0900 0.0000 0.0500 0.1965 0.8037 184 62 0.9500 0.8700 0.9500 -0.0800 0.0000 0.0500 0.1963 0.8000 239 80 0.9500 0.8800 0.9500 -0.0700 0.0000 0.0500 0.2000 0.8002 325 109 0.9500 0.8900 0.9500 -0.0600 0.0000 0.0500 0.1998 0.8005 469 157 0.9500 0.9000 0.9500 -0.0500 0.0000 0.0500 0.1995 Note: exact results based on the binomial were only calculated when both N1 and N2 were less than 100. ReferencesChow, S.C.; Shao, J.; Wang, H. 2003. Sample Size Calculations in Clinical Research. Marcel Dekker. New York. Farrington, C. P. and Manning, G. 1990. 'Test Statistics and Sample Size Formulae for Comparative Binomial Trials with Null Hypothesis of Non-Zero Risk Difference or Non-Unity Relative Risk.' Statistics in Medicine, Vol. 9, pages 1447-1454.Fleiss, J. L., Levin, B., Paik, M.C. 2003. Statistical Methods for Rates and Proportions. Third Edition. JohnWiley & Sons. New York.Gart, John J. and Nam, Jun-mo. 1988. 'Approximate Interval Estimation of the Ratio in Binomial Parameters: A Review and Corrections for Skewness.' Biometrics, Volume 44, Issue 2, 323-338.Gart, John J. and Nam, Jun-mo. 1990. 'Approximate Interval Estimation of the Difference in Binomial Parameters: Correction for Skewness and Extension to Multiple Tables.' Biometrics, Volume 46, Issue 3,637-643.Lachin, John M. 2000. Biostatistical Methods. John Wiley & Sons. New York.Machin, D., Campbell, M., Fayers, P., and Pinol, A. 1997. Sample Size Tables for Clinical Studies, 2nd Edition. Blackwell Science. Malden, Mass.Miettinen, O.S. and Nurminen, M. 1985. 'Comparative analysis of two rates.' Statistics in Medicine 4: 213-226. Report Definitions'Power' is the probability of rejecting a false null hypothesis. It should be close to one.'N1 and N2' are the sizes of the samples drawn from the corresponding groups.'P2' is the response rate for group two which is the standard, reference, baseline, or control group.'P1.0' is the smallest treatment-group response rate that still yields a non-inferiority conclusion.'P1.1' is the treatment-group response rate at which the power is calculated.'D0' is the non-inferiority margin. It is the difference P1-P2 assuming H0.'D1' is the actual difference, P1-P2, at which the power is calculated.'Target Alpha' is the probability of rejecting a true null hypothesis that was desired.'Actual Alpha' is the value of alpha that is actually achieved.'Beta' is the probability of accepting a false H0. Beta = 1 - Power.'Grp 1' refers to Group 1 which is the treatment or experimental group.'Grp 2' refers to Group 2 which is the reference, standard, or control group.'Equiv.' refers to a small amount that is not of practical importance.'Actual' refers to the true value at which the power is computed.Summary StatementsSample sizes of 619 in group one and 207 in group two achieve 80% power to detect anon-inferiority margin difference between the group proportions of -0.1000. The reference group proportion is 0.5000. The treatment group proportion is assumed to be 0.4000 under the null hypothesis of inferiority. The power was computed for the case when the actual treatment group proportion is 0.5000. The test statistic used is the one-sided Z test (unpooled). The significance level of the test was targeted at 0.0500. The significance level actually achievedby this design is NA.Chart Section。
风险英语名词解释汇总
风险英语名词解释汇总Risk: Risk refers to the possibility of loss or harm due to uncertain eventsor circumstances. It is an inherent part of any activity or decision-making process and can be quantified based on the probability of occurrence and the potential impact.Hazard: A hazard is a potential source of harm or danger that has thepotential to cause injury, illness, or damage to property or the environment. Hazards can be classified into different categories such as physical, chemical, biological, ergonomic, and psychosocial.Uncertainty: Uncertainty refers to the lack of knowledge or predictability about future events or outcomes. It is an important aspect of risk assessment as it affects the estimation of probabilities and potential impacts ofdifferent scenarios.Probability: Probability is a measure of the likelihood that a particularevent or outcome will occur. It is usually expressed as a fraction or percentage ranging from 0 to 1. High probability events are more likely to occur, while low probability events have a smaller chance of happening.Impact: Impact refers to the consequences or effects that result from a particular event or situation. In the context of risk, it represents the magnitude of the potential loss or harm that may occur if a certain risk materializes.Mitigation: Mitigation refers to the actions taken to reduce or minimize the potential risks or hazards. It involves implementing preventive measures, control measures, or safety procedures to mitigate the likelihood and severity of negative outcomes.Residual risk: Residual risk refers to the level of risk that remains after mitigation measures have been implemented. It represents the risk that cannot be completely eliminated and requires ongoing monitoring and management.Risk assessment: Risk assessment is the process of identifying, analyzing, and evaluating risks associated with a particular activity, decision, or situation. It involves identifying hazards, estimating probabilities and potential impacts, and determining the level of risk involved.Risk management: Risk management is the process of identifying, assessing, and controlling risks to minimize the potential for loss or harm. It involves implementing strategies and measures to mitigate risks, as well as monitoring and reviewing risk control measures.Control measure: Control measures are actions or precautions taken to prevent or reduce the likelihood and severity of a risk. They can include engineering controls, administrative controls, and personal protective equipment, among others.Vulnerability: Vulnerability refers to the degree of susceptibility or exposure to risks. It is influenced by various factors such as the nature of the hazard, the level of protection in place, and the capacity to respond effectively to potential risks.Contingency plan: A contingency plan is a predefined set of actions or procedures designed to be implemented in the event of a specific risk or emergency situation. It outlines the steps to be taken to minimize the impact and ensure business continuity.Insurance: Insurance is a financial arrangement in which an individual or organization transfers the risk of potential losses to an insurance company in exchange for regular premium payments. It provides financial protectionagainst unexpected events or accidents.Risk appetite: Risk appetite refers to an organization's willingness andability to tolerate and accept risks in pursuit of its objectives. It reflects the organization's risk culture and influences its risk management strategies and decision-making processes.Stakeholder: Stakeholders are individuals or groups who have an interest or concern in a particular activity, decision, or organization. They may be affected by the risks involved and play a role in risk assessment and management processes.Compliance: Compliance refers to the adherence to laws, regulations, standards, and policies. It ensures that organizations operate within legal and ethical boundaries and reduces the risk of penalties, fines, or reputational damage.Crisis management: Crisis management is the process of preparing for, responding to, and recovering from a crisis or emergency situation. Itinvolves establishing emergency response plans, training personnel, and coordinating resources to minimize the impact and restore normalcy.Business continuity: Business continuity refers to the ability of an organization to continue operating and providing essential services or products during and after a disruption or crisis. It involves implementing continuity plans, backup systems, and recovery strategies.Risk communication: Risk communication is the process of exchanging information and sharing knowledge about risks with relevant stakeholders. It aims to enhance understanding, facilitate informed decision-making, and promote appropriate risk management actions.Emerging risks: Emerging risks refer to new or evolving risks that have the potential to impact an organization's objectives or operations. They often arise from technological advancements, changing market conditions, or shifts in social and environmental trends.these risk-related terms play a crucial role in understanding and managing risks in various sectors and industries. They provide a common language and framework for assessing, mitigating, and communicating risks to ensure the safety, security, and success of individuals, organizations, and communities.。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
1157
World Appl. Sci. J., 11 (9): 1157-1163, 2010
the pipes are categorized based upon time of consuming start, geometric characteristic and material type and then the failure rate and the time of replacement after ending their useful age have been anticipated, [9]. Rostum has modeled the water network assuming it as repairable system and then he has compared the existing methods for anticipating the failure time of the pipes, [10]. He has assumed that the system has become “good-as-new” after any repair stage. It is indicated in Pulcini’s studies that if a system is repaired repeatedly it may result in producing a finite bound for increasing the failure intense failure function is introduced together with a Bounded Intensity Process (BIP) interval, [11]. In many of the above mentioned references it is assumed that the water network is repairable and has a form of Non-homogenous Poison Process (NHPP). In all methods of previous references, it has been assumed that the type of repair does not affect the improving process of the system and the system after any round of repair becomes “good-as-new”, [13]. In other words, the system after any repair process becomes the same as before failure. Such repairs are usually called Minimal Repair. But the reality is that the system may become better than its state before failure. In these models, it has been assumed that after repair, there has been no variation with respect to the base line, instead the function moves vertically along the intensity axis. Guo has presented a model for estimating the failure intensity in repairable systems using power rule. In this model the effects of failure for a failure mode has been presented, [14]. Recently a new statistical method called Trend Renewal Process (TRP) using a failure intensity function, (t) , similar to that of NHPP method has been presented by Lindqvist, [15, 16]. The difference is that in TRP method the effect of the type of failure has been considered in repairing the system. Also, in this model, the failure intensity has a uniform process. However, although this model as a package is able to consider the effects of the type of failure on the system, its application in engineering problems is complex and difficult. In this paper a practical model for estimating the failure intensity in water pipes network is presented. In this method the type of failure is considered in addition to the effects of repair on the model. Repairing the system decreases the number of failures in the future and including this fact in the method results in more realistic model. For this aim a combination of parameters is defined which is able to account for effect of repairing on the type of failure. After this stage, the full likelihood function is formed and the model parameters are calculated by
World Applied Sciences Journal 11 (9): 1157-1163, 2010 ISSN 1818-4952 © IDOSI Publications, 2010
Estimation of Failure Probability in Water Pipes Network Using Statistical Model
1
M.J. Fadaee and 2R. Tabatabaei
2
Department of Shahid Bahonar University of Kerman, Civil Engineering Department of Islamic Azad University, Kerman Branch, Civil Engineering, P.O. Box 76175-6114, Kerman, Iran
1
Abstract: In this paper, a statistical model is presented for decision making in repairing water pipes network. The water distribution system has been considered as a “repairable” system which is under repeating failure modes. From this, a practical model for anticipating the failure of the water pipes in repairable systems has been presented using the trend renewal process concept. In this process, the statistical Power law has been used for projecting the failure rate to account for the effects of repairs and for different failure modes in estimation of failure intensity. After finding the failures as a function of time, the reliability of the system efficiency is then estimated using survival analysis. At the end, a sample pipes network has been modeled using presented statistical model and the values of failure intensities with respect to time and the curve for reliability function has been found. Key words: Reliability function INTRODUCTION Various techniques regarding pipe reliability assessment have been developed through the years. Normally, in a pipe network the amount of failure is estimated by a statistical model with respect to time. Historically, the most often used models in the previous works are general statistical models such as Renewal Process (RP), power model of Weibull, Homogenous Poison Process (HPP) and recently, Non-homogenous Poison Process (NHPP). Another model that has been used by many references is modeling the failed pipes with Shamir and Howard method, [1]. In this method, the optimum time for pipe replacement is found. In Shamir and Howard model, the old replaced pipes are considered together with the replaced pipes. Walski et al. have presented a model similar to the model of Shamir and Howard except that the history of the failure has been also entered the model, [2, 3]. In several references the statistical model of Cox’s Semi-Parametric has been used for estimating the failure of the pipe. In this method the Proportional Hazard Model (PHM) has been adopted for calculating the risk rate in terms of time, [4]. Besides the Power law Repairable system Instantaneous failure intensity MLE