高可靠性软件的极值统计分析

提出了极值统计分析的方法．对于失效数据极值分布函数的拟合检验，采用了相关系数法；对于母体分布的参数估计，则分别采用了最小二乘估计（LSE）和极大似然估计（MLE）；最后通过一个实例说明了统计过程，并用模拟的方法证明了最小二乘估计较好。     

An approach for using risk assessment in risk-informed decisions on plant-specific changes to the licensing basis

This paper discusses an acceptable approach that the US Nuclear Regulatory Commission staff has proposed for using Probabilistic Risk Assessment in making decisions on changes to the licensing basis of a nuclear power plant. First, the overall philosophy of risk-informed decision-making, and the process framework are described. The philosophy is encapsulated in five principles, one of which states that, if the proposed change leads to an increase in core damage frequency or risk, the increases must be small and consistent with the intent of the Nuclear Regulatory Commission's Safety Goal Policy Statement. The second part of the paper discusses the use of PRA to demonstrate that this principle has been met. The discussion focuses on the acceptance guidelines, and on comparison of the PRA results with those guidelines. The difficulties that arise because of limitations in scope and analytical uncertainties are discussed and approaches to accommodate these difficulties in the decision-making are described.     

Getting beyond ‘grin and bear it’ in the practice of risk management

Risk perception researchers frequently notice major discrepancies between expert judgment and lay opinion concerning the identity of ‘large’ risks. These discrepancies are particularly disconcerting because they lead to a significant misallocation of scarce resources devoted to public risk management. Yet, democratic decision-making also appeals to many risk perception researchers, and some see such little prospect for a solution thaty they seem close to surrendering to decision-making based on lay risk perception. This article argues that such a ‘grin and bear it’ strategy is also unsatisfactory; that risk perceptions are unstable grounds for decision-making; that expert risk assessment and lay risk perception differ by design rather than accident; that risk experts' search for ‘virtuous lawmakers’ who would make public decisions for the greatest public good is a vain one; that the dissonance risk perception researchers now observe will likely get worse in the new information age; and that risk perception researchers need to return to first principles to find solutions to these dilemmas. In particular, the gap between expert judgment and lay perception will close only when non-experts are permitted and expected to make their own risk management decisions, so far as that is possible, while bearing full responsibility for their choices.     

A framework for the establishment of organizational risk indicators

Organizational risk indicators are proposed as a tool for risk control during operation of offshore installations, as a complement to QRA-based indicators. An organizational factor framework is developed based on a review of existing organizational factor frameworks, research on safety performance indicators, and previous work on QRA-based indicators. The results comprise a qualitative organizational model, proposed organizational risk indicators, and a quantification methodology for assessing the impact of the organization on risk. The risk indicators, when validated, will aid in a frequent control of the risk in the periods between the updating of the quantitative risk assessments.     

Risk indicators as a tool for risk control

This paper presents a general methodology for the establishment of risk indicators that can be used as a tool for risk control during operation of offshore petroleum installations. The risk indicators established are based on the platform specific quantitative risk analysis (QRA). The general methodology is evaluated against comparable approaches both in offshore and nuclear industry. There are two distinct features of this methodology. The first is that it is truly risk-based with the intention of covering the total risk picture. The second is that the identification of the risk factors contributing most to the total risk is based on realistic changes of each factor assessed by the platform personnel, not a theoretically assumed change. The set of risk indicators for one specific installation is presented along with test results.     

Proposal of a risk-factor-based analytical approach for integrating occupational health and safety into project risk evaluation

Excluding occupational health and safety (OHS) from project management is no longer acceptable. Numerous industrial accidents have exposed the ineffectiveness of conventional risk evaluation methods as well as negligence of risk factors having major impact on the health and safety of workers and nearby residents. Lack of reliable and complete evaluations from the beginning of a project generates bad decisions that could end up threatening the very existence of an organization.     

Prioritizing and quantifying the risk of outstanding corrective actions

This paper presents methods for assessing the risk of outstanding corrective actions, and describes how the methods were applied at a nuclear power station. The methods are motivated by a current industry focus on risk-informed operation and a current NRC focus on risk-informed regulation. Application of the methods provides: (1) a risk-informed basis for establishing schedules and allocating resources to implement corrective actions; (2) a risk-informed basis for justifying continued operation until corrective actions are implemented.     

An integrated probabilistic risk analysis decision support methodology for systems with multiple state variables

Probabilistic risk analysis (PRA) methods have been proven to be valuable in risk and reliability analysis. However, a weak link seems to exist between methods for analysing risks and those for making rational decisions. The integrated decision support system (IDSS) methodology presented in this paper attempts to address this issue in a practical manner. In consists of three phases: a PRA phase, a risk sensitivity analysis (SA) phase and an optimisation phase, which are implemented through an integrated computer software system. In the risk analysis phase the problem is analysed by the Boolean representation method (BRM), a PRA method that can deal with systems with multiple state variables and feedback loops. In the second phase the results obtained from the BRM are utilised directly to perform importance and risk SA. In the third phase, the problem is formulated as a multiple objective decision making problem in the form of multiple objective reliability optimisation. An industrial example is included. The resultant solutions of a five objective reliability optimisation are presented, on the basis of which rational decision making can be explored.     

Earthquake risk assessment of building structures

During the past two decades, probabilistic risk analysis tools have been applied to assess the performance of new and existing building structural systems. Structural design and evaluation of buildings and other facilities with regard to their ability to withstand the effects of earthquakes requires special considerations that are not normally a part of such evaluations for other occupancy, service and environmental loads. This paper reviews some of these special considerations, specifically as they pertain to probability-based codified design and reliability-based condition assessment of existing buildings. Difficulties experienced in implementing probability-based limit states design criteria for earthquake are summarized. Comparisons of predicted and observed building damage highlight the limitations of using current deterministic approaches for post-earthquake building condition assessment. The importance of inherent randomness and modeling uncertainty in forecasting building performance is examined through a building fragility assessment of a steel frame with welded connections that was damaged during the Northridge Earthquake of 1994. The prospects for future improvements in earthquake-resistant design procedures based on a more rational probability-based treatment of uncertainty are examined.     

The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study

The objective of this research was to develop a decision support framework (DSF) to assess quantitative risk in multimodal green logistics. This risk assessment is the combination of a number of models, the failure mode and effects analysis, the risk contour plot, the quantitative risk assessment, the analytic hierarchy process and the data envelopment analysis which can support a user to perform risk assessment in various decisions. The contribution of this research is that the risk assessment model can generate an optimal green logistics route in accordance with weight from the user. The highlight of this DSF is that the quantitative assessment model can reduce bias on risk assessment of logistics route. An in-depth case study, recommendations, limitations and further research are also provided.     

An interval probability-based FMEA model for risk assessment: A real-world case

Failure mode and effect analysis (FMEA) is an effective quality tool to eliminate the risks and enhance the stability and safety in the fields of manufacturing and service industry. Nevertheless, the conventional FMEA has been criticized for its drawbacks in the evaluation process of risk factors or the determination of risk priority number (RPN), which may lead to inaccurate evaluation results. Therefore, in this paper, we develop a novel FMEA method based on rough set and interval probability theories. The rough set theory is adopted to manipulate the subjectivity and uncertainty of experts' assessment and convert the evaluation values of risk factors into interval numbers. Meanwhile, the interval exponential RPN (ERPN) is used to replace the traditional RPN due to its superior properties, eg, solving the problems of duplicate numbers and discontinuity of RPN values. Furthermore, an interval probability comparison method is proposed to rank the risk priority of each failure mode for avoiding the information loss in the calculation process of RPN. Finally, a real case study is presented, and the comparison analysis among different FMEA methods is conducted to demonstrate the reliability and effectiveness of the proposed FMEA method.     

Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries

In this paper a unified methodology is presented for the modelling of the evolution of road safety in 30 European countries. For each country, annual data of the best available exposure indicator and of the number of fatalities were simultaneously analysed with the bivariate latent risk time series model. This model is based on the assumption that the amount of exposure and the number of fatalities are intrinsically related. It captures the dynamic evolution in the fatalities as the product of the dynamic evolution in two latent trends: the trend in the fatality risk and the trend in the exposure to that risk. Before applying the latent risk model to the different countries it was first investigated and tested whether the exposure indicator at hand and the fatalities in each country were in fact related at all. If they were, the latent risk model was applied to that country; if not, a univariate local linear trend model was applied to the fatalities series only, unless the latent risk time series model was found to yield better forecasts than the univariate local linear trend model. In either case, the temporal structure of the unobserved components of the optimal model was established, and structural breaks in the trends related to external events were identified and captured by adding intervention variables to the appropriate components of the model. As a final step, for each country the optimally modelled developments were projected into the future, thus yielding forecasts for the number of fatalities up to and including 2020.     

Combining the cost of reducing uncertainty with the selection of risk assessment models for remediation decision of site contamination

The decision as to whether a contaminated site poses a threat to human health and should be cleaned up relies increasingly upon the use of risk assessment models. However, the more sophisticated risk assessment models become, the greater the concern with the uncertainty in, and thus the credibility of, risk assessment. In particular, when there are several equally plausible models, decision makers are confused by model uncertainty and perplexed as to which model should be chosen for making decisions objectively. When the correctness of different models is not easily judged after objective analysis has been conducted, the cost incurred during the processes of risk assessment has to be considered in order to make an efficient decision. In order to support an efficient and objective remediation decision, this study develops a methodology to cost the least required reduction of uncertainty and to use the cost measure in the selection of candidate models. The focus is on identifying the efforts involved in reducing the input uncertainty to the point at which the uncertainty would not hinder the decision in each equally plausible model. First, this methodology combines a nested Monte Carlo simulation, rank correlation coefficients, and explicit decision criteria to identify key uncertain inputs that would influence the decision in order to reduce input uncertainty. This methodology then calculates the cost of required reduction of input uncertainty in each model by convergence ratio, which measures the needed convergence level of each key input's spread. Finally, the most appropriate model can be selected based on the convergence ratio and cost. A case of a contaminated site is used to demonstrate the methodology.     

Forward and backward risk assessment throughout a system life cycle using dynamic Bayesian networks: A case in a petroleum refinery

In this paper, risk modeling was conducted based on the defined risk elements of a conceptual risk framework. This model allows for the estimation of a variety of risks, including human error probability, operational risk, financial risk, technological risk, commercial risk, health risk, and social and environmental risks. Bayesian network (BN) structure learning techniques were used to determine the relationships among the model variables. By solving a bi-objective optimization problem applying the genetic algorithm (GA) with the Pareto ranking approach, the network structure was learned. Then, risk modeling was performed for a petroleum refinery focusing on HydroDeSulfurization (HDS) technology throughout its life cycle. To extend the model horizontally and make it possible to evaluate the risk trend throughout the technology life cycle, we developed a dynamic Bayesian network (DBN) with three-time slices. A two-way forward and backward approach was used to analyze the model. The model validation was performed by applying the leave-one-out cross-validation method.     

Some considerations on the treatment of uncertainties in risk assessment for practical decision making

This paper discusses the challenges involved in the representation and treatment of uncertainties in risk assessment, taking the point of view of its use in support to decision making. Two main issues are addressed: (1) how to faithfully represent and express the knowledge available to best support the decision making and (2) how to best inform the decision maker. A general risk-uncertainty framework is presented which provides definitions and interpretations of the key concepts introduced. The framework covers probability theory as well as alternative representations of uncertainty, including interval probability, possibility and evidence theory.     

Development of crash-severity-index models for the measurement of work zone risk levels

Highway work zones interrupt regular traffic flows and create safety problems. Improving safety without sacrificing the main function of highways is a challenging task that traffic engineers and researchers have to confront. In this study, the concept of using crash severity index (CSI) for work zone safety evaluation was proposed and a set of CSI models were developed through the modeling of work zone crash severity outcomes. A CSI is a numerical value between zero and one that is estimated from given work zone variables. It is interpreted as the likelihood of having fatality/fatalities when a severe crash occurs in a given work zone. The CSI models were developed using a three-step approach. First, a wide range of crash variables were examined in a comprehensive manner and the significant risk factors that had impact on crash severity were selected. Second, the CSI models were developed using logistic regression technique by incorporating the selected risk factors. Finally, the developed models were validated using the recent crash data and their ability in assessing work zone risk levels were analyzed. Results of this study showed that CSI models can provide straightforward measurements of work zone risk levels.     

Daily intake and human risk assessment of organochlorine pesticides (OCPs) based on Cambodian market basket data

To assess organochlorine pesticide (OCP) contaminations and its possible adverse health impacts, different food samples were collected from three areas of Cambodia, one of the poorest countries in the world. The ∑OCP concentrations in Kampong Cham, Kratie and Kandal provinces ranged from 1.28 to 188 (median 3.11), 1.06 to 25.1 (5.59) and 2.20 to 103 (20.6) ng g⁻¹, respectively. The dichlorodiphenyltrichloroethanes (DDTs) were the predominant OCPs and accounted for 62.2% (median) among all foodstuffs. Congener profile analyses suggested that there were new input sources of DDTs and hexachlorocyclohexanes (HCHs) in Cambodia, particularly in Kandal province. The estimated daily intake of OCPs (330 ng kg⁻¹ day⁻¹) for residents in Kandal province ranked No. 1 among the 13 compared countries or regions. On the basis of 95th percentile concentrations, the carcinogenic hazard ratios (HRs) of most investigated individual OCPs in vegetable and fish in Cambodia exceeding unity. Particularly for α-HCH in vegetable, the 95th HR was as high as 186. The data revealed that there is a great cancer risk for the local residents with life time consumption of OCP contaminated vegetable and fish. To our knowledge, this the first study to evaluate the daily intakes of OCPs in Cambodia.     

Validation of finite-element models using full-field experimental data: Levelling finite-element analysis data through a digital image correlation engine

Full-field data from digital image correlation (DIC) provide rich information for finite-element analysis (FEA) validation. However, there are several inherent inconsistencies between FEA and DIC data that must be rectified before meaningful, quantitative comparisons can be made, including strain formulations, coordinate systems, data locations, strain calculation algorithms, spatial resolutions and data filtering. In this paper, we investigate two full-field validation approaches: (1) the direct interpolation approach, which addresses the first three inconsistencies by interpolating the quantity of interest from one mesh to the other, and (2) the proposed DIC-levelling approach, which addresses all six inconsistencies simultaneously by processing the FEA data through a stereo-DIC simulator to ‘level' the FEA data to the DIC data in a regularisation sense. Synthetic ‘experimental' DIC data were generated based on a reference FEA of an exemplar test specimen. The direct interpolation approach was applied, and significant strain errors were computed, even though there was no model form error, because the filtering effect of the DIC engine was neglected. In contrast, the levelling approach provided accurate validation results, with no strain error when no model form error was present. Next, model form error was purposefully introduced via a mismatch of boundary conditions. With the direct interpolation approach, the mismatch in boundary conditions was completely obfuscated, while with the levelling approach, it was clearly observed. Finally, the ‘experimental' DIC data were purposefully misaligned slightly from the FEA data. Both validation techniques suffered from the misalignment, thus motivating continued efforts to develop a robust alignment process. In summary, direct interpolation is insufficient, and the proposed levelling approach is required to ensure that the FEA and the DIC data have the same spatial resolution and data filtering. Only after the FEA data have been ‘levelled' to the DIC data can meaningful, quantitative error maps be computed.     

Perspectives on risk: review and discussion of the basis for establishing a unified and holistic approach

There exist many perspectives on risk, including safety engineering, social scientist perspectives, risk perception research and economic decision analysis. Traditionally, some of the different perspectives have been viewed to represent completely different frameworks, and the exchange of ideas and results has been difficult. Much of the existing discussions on risk perspectives have in our view lacked a sufficient level of precision on the fundamental ideas of risk assessments and management. For example, there is more than one line of thinking in risk analysis and assessment and mixing all approaches into one, gives a rather meaningless discussion. In this paper, we review some of the most common perspectives on risk. We show that it is possible to establish a common basis for the different perspectives, by looking at risk as the full spectrum of the dimensions (a) possible consequences and (b) associated uncertainties. Examples from the offshore oil and gas industry are included to illustrate ideas.