Lifecycle risk assessment of a technological system using dynamic Bayesian networks

Investigations of technological systems accidents reveal that technical, human, organizational, as well as environmental factors influence the occurrence of accidents. Despite these facts, most traditional risk assessment techniques focus on technical aspects of systems and have some limitations of incorporating efficient links between risk models and human and organizational factors. This paper presents a method for risk analysis of technological systems. Application of the presented framework makes it possible to analyze the influence of technical, human, organizational, and environmental risk factors on system safety. It encompasses system lifecycle from design to operational phase to give a comprehensive picture of system risks. The developed framework comprises the following main steps: (1) development of a conceptual risk analysis framework, (2) identifying risk influencing factors in different levels of technical, human, organizational, and environmental factors providing the possibility of analyzing interactions in a multi‐level system, (3) modeling system risk using dynamic Bayesian network (DBN), (4) assignment of probabilities and risk quantification in node probability tables (NPTs) based on industry records and experts extracted knowledge, (5) implementation of the model for wind turbines risk analysis combining use of V‐model, risk factors, and DBN in order to analyze the risk, and (6) analyzing different scenarios and the interactions in different levels. Finally, the various steps of the framework, the research objective fulfillment, and case study results are presented and discussed.     

Handling Uncertainties in Fault Tree Analysis by a Hybrid Probabilistic–Possibilistic Framework

Fault tree analysis is a method largely used in probabilistic risk assessment. Uncertainties should be properly handled in fault tree analyses to support a robust decision making. While many sources of uncertainties are considered, dependence uncertainties are not much explored. Such uncertainties can be labeled as ‘epistemic’ because of the way dependence is modeled. In practice, despite probability theory, alternative mathematical structures, including possibility theory and fuzzy set theory, for the representation of epistemic uncertainty can be used. In this article, a fuzzy β factor is considered to represent the failure dependence uncertainties among basic events. The relationship between β factor and system failure probability is analyzed to support the use of a hybrid probabilistic–possibilistic approach. As a result, a complete hybrid probabilistic–possibilistic framework is constructed. A case study of a high integrity pressure protection system is discussed. The results show that the proposed method provides decision makers a more accurate understanding of the system under analysis when failure dependencies are involved. Copyright © 2015 John Wiley & Sons, Ltd.     

A new model to optimize the human reliability based on CREAM and group decision making

The Cognitive Reliability and Error Analysis Method (CREAM) represents one of the second-generation approaches to human reliability assessment, taking into account the influence of environmental conditions on human error probability (HEP). In the context of CREAM, the Common Performance Conditions (CPCs) influence error probabilities. Since not all CPCs have equal impacts, this study employs the Bayesian Best Worst Method (BWM), a novel approach in group decision-making, to assign weights to these factors. Subsequently, two techniques based on basic CREAM are proposed. The current control mode is determined in the first technique according to the experts' opinions. Then the probability of human error is calculated based on the amount of control. It is possible to provide solutions for improving control mode, based on obtained results. Therefore, in this study, the second method has been used to make suggestions to enhance human reliability. For this purpose, in the second technique, an optimization problem is formulated to select the best applicable programs for managers to enhance human reliability. The proposed bi-objective model tries to increase the reliability of human resources by reducing human error and costs. The proposed bi-objective model seeks to bolster the reliability of human resources by concurrently minimizing HEP and associated costs. The efficiency of the presented methods is verified through a case study in the control room of the cement factory. The results of the first technique reveal an opportunistic control mode with a corresponding HEP of 0.0198. On the other hand, the outcomes of our proposed model underscore the greater impact of improving CPC levels in reducing the probability of human error. Ultimately, the practical programs derived from our mathematical model provide decision-makers with valuable insights to reduce the probability of human error to a mere 0.000172 through the transition from opportunistic to strategic control.     

Introducing a heuristic approach to enhance the reliability of system safety assessment

Probabilistic risk assessment techniques are the important tools which can considerably improve the safety performance of the studied system and reduce the risk to an acceptable level. Typically, decision‐making process is an important part of risk assessment methods that accordingly bring the ambiguity inside. Decision makers as experts commonly express their subjective opinions about the occurrence of the root events in order to obtain the probability of the undesired event. Subsequently, the critical root events are identified, and possible intervention is performed to reduce the probability of the critical events. However, the serious point is the viability of the obtained probabilities and priority ranking of the critical events. In this study, a heuristic optimization model of linear mathematical programming using triangular intuitionistic fuzzy number (TrIFN) is proposed to obtain the feasible, optimum, and reliable results compared with available methods. The Spearman correlation is performed to examine the reliability and behavior of the proposed model. In order to show the effectiveness of the proposed approach, it is applied on a real case study. The application of the model confirms its robustness to prioritize critical root events over the conventional one.     

Assessment of technical and environmental performances of wheat‐based foams in thermal packaging applications

This paper presents an assessment of the technical and environmental performance of a wheat‐based foam (WBF) and bio‐composite for shipping chilled products. The thermal conductivity of the WBF was found to be higher than that of polyurethane foams commonly used in high‐value insulation packaging, but close to that of low‐density (expanded polystyrene) EPS foams and significantly lower than that of polyethylene (PE) foams, which are typically used in thermal packaging of foods. The insulation performance of a simple cool box constructed from both the WBF and EPS sandwich panels without the use of any refrigerant was studied experimentally. The comparison demonstrated that the performance of the WBF cool box was comparable to that of the EPS counterpart. Two industrial case studies were conducted on WBF cool boxes with refrigerants in comparison with PE or EPS counterparts. The WBF cool boxes had comparable thermal performance to the EPS and PE counterparts on the basis of identical foam thickness. The performance of the WBF cool boxes was also simulated with finite element (FE) modelling. Good agreement was achieved between experimental data and the FE prediction. The model was then used to assist cool box design. WBF cool boxes made from renewable raw materials are inherently biodegradable and may be used as an alternative to those based on polymer foams in thermal packaging applications. Life‐cycle assessment (LCA) was used to investigate environmental profiles of cool boxes made with WBF, EPS and PE foams. The WBF cool boxes offer substantially lower global warming and abiotic depletion potentials than equivalent cool boxes made from petrochemical foams. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

Calibration of Expert Judgments Applied to Uncertainty Assessment

Abstract:

This article contributes a methodology for eliciting expert judgment in support of decision analysis associated with the conceptual design of advanced engineering systems. To provide a basis for decision making in the presence of model and input parameter uncertainties, experts in several disciplines can be utilized to provide model parameter estimates to facilitate analyses. The judgment elicitation methodology was developed to cover a multitude of system disciplines using multiple experts. To address consistency in expert assessments, the methodology includes expert assessment calibration means. A sample application of the resultant expert judgment methodology is discussed. An engineering manager can use the methodology described in this article to assess viability of potential courses of action in high-risk or advanced state-of-the-art technology systems development ventures.     

An approach for assessing human decision reliability

This paper presents a method to study human reliability in decision situations related to nuclear power plant disturbances. Decisions often play a significant role in handling of emergency situations. The method may be applied to probabilistic safety assessments (PSAs) in cases where decision making is an important dimension of an accident sequence. Such situations are frequent e.g. in accident management. In this paper, a modelling approach for decision reliability studies is first proposed. Then, a case study with two decision situations with relatively different characteristics is presented. Qualitative and quantitative findings of the study are discussed. In very simple decision cases with time pressure, time reliability correlation proved out to be a feasible reliability modelling method. In all other decision situations, more advanced probabilistic decision models have to be used. Finally, decision probability assessment by using simulator run results and expert judgement is presented.     

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.     

Justification for the selection of manufacturing technologies: a fuzzy-decision-tree-based approach

In this paper, a developed model for the justification of alternative manufacturing technologies is presented. The approach, based on fuzzy decision trees, provides a methodology capable of identifying patterns within a technology case repository to support the evaluation of manufacturing systems. Experts are highly influential individuals in the decision process; they provide support and guidance when selecting investments. The experience-oriented task is founded on previous cases or an experts’ experience, and therefore difficult to express in a rational form. The concept is based on a number of characteristics of the case-based reasoning, rule induction and expert system theory. Structured around the fuzzy-decision-tree data-mining technique, the framework provides the ability of using regulated case information to act as structured experience for assisting in the decision process. Fuzzy induction extracts formal rules from a set of experience data, and the expert system philosophy computes the experience base of human expertise for problem-solving. A test case indicates the stability of the classification algorithm and verifies the applicability within the domain.     

计及人因可靠性的变电站自动化系统失效风险评估

目前对于变电站自动化系统的可靠性研究鲜有涉及人为因素的定量影响,而许多电网事故是由人为失误引起的,因此提出了一种计及人因可靠性的变电站自动化系统失效风险评估方法。首先根据工作人员所处场景采用层次分析法-成功似然指数法评估人为失效概率以此修正设备故障概率,其次基于IEC-61850的功能分解对自动化系统各种功能的失效风险进行评估,最终融合为系统失效风险,以此描述当前系统可靠性,为调度部门决策提供指导。算例结果表明,人为因素对于系统失效风险有着较大的影响,计及人因可靠性可更为客观地描述系统风险,为调度操作人员提供风险预警。     

Development of a Fuzzy Rule‐based Decision‐making System for Evaluating the Lifetime of a Rubber Fender

If a rubber fender, whose purpose is to absorb the shock energy that is generated when a ship is berthing, fails because of deterioration and vulcanization, the ship and the pier structure may incur serious damage. In this paper, we introduce a fuzzy rule‐based decision‐making system to predict the maintenance timing of rubber fenders by diagnosing their faults and evaluating their lifetimes on the basis of the knowledge and experience of experts. First, we built a case library containing information concerning fender lifetimes and points in these lifetimes when faults occurred. This was gathered on the basis of the results of testing the lifetime of rubber materials under accelerated conditions and a knowledge base of the physical properties of fenders in order to evaluate the deterioration in a rubber fender. Moreover, after representing the key factors affecting the deterioration of rubber fenders as a fuzzy cognitive map that reflects the knowledge and experience of domain experts, we drew inferences regarding the heuristic factors of the lifetime of rubber, the conceptual centrality of which is relatively high according to the cause‐and‐effect relationship between key factors. Finally, we propose a diagnostic process for the performance deterioration of rubber fenders based on the various factors that affect the lifetime of rubber and its multiple physical properties in the form of a decision‐making tree. We then applied it to rule‐based and case‐based reasoning. Copyright © 2014 John Wiley & Sons, Ltd.     

飞行机组人为差错风险分析方法研究

提出了一个新的飞行机组人为差错风险评估方法F HECA,识别和分析航空公司的机组人为差错风险。该方法在对机组人为差错类型进行总结和分类的基础上,选取人为差错严重度作为评价指标,将该指标中的3个变量人为差错概率、人为差错后果严重度、人为差错影响概率与灰色综合评价法结合,定量评估机组人为差错的严重度,实现机组人为差错风险评估。通过实例分析验证此方法可用于分析机组人为差错风险,为飞行训练和飞行操作手册内容的改进提供技术支持,是民航人为因素研究中可借鉴的一种方法。     

Reliability Analysis of a conveyor system using hybrid data

The quantification of a fault tree is difficult without an exact probability value for all of the basic events of the tree. To overcome this difficulty in this paper, we propose a methodology which employs ‘hybrid data’ as a tool to analyse the fault tree. The proposed methodology estimates the failure probability of basic events using the statistical analysis of field recorded failures. Under these circumstances, where there is an absence of past failure records, the method follows a fuzzy set based theoretical evaluation based on the subjective judgement of experts for the failure interval. The proposed methodology has been applied to a conveyor system. The results of the analysis reveal the effectiveness of the proposed methodology and the instrumental role played by the experience of experts in providing reliability oriented information. Copyright © 2006 John Wiley & Sons, Ltd.     

A hierarchical fuzzy TOPSIS approach to assess improvement areas when implementing green supply chain initiatives

Implementing green supply chain management (GSCM) initiatives can generate more business opportunities for firms. It also requires changes in firms’ operational capabilities and resources that may have an adverse effect on firms’ operations performance. In order to achieve sustainable economic and environmental performance, it is essential for companies to evaluate different green initiatives and assess improvement areas when implementing green initiatives. This study proposes a fuzzy hierarchical TOPSIS approach to support such an assessment. It enables decision makers to better understand the complete evaluation process and provide a more accurate, effective and systematic decision support tool. An illustrative case is presented to help researchers and practitioners understand the importance of developing an appropriate organisation strategy in implementing green practices.     

Using AHP in determining the prior distributions on gas pipeline failures in a robust Bayesian approach

The paper proposes a robust Bayesian approach to support the replacement policy of low-pressure cast-iron pipelines used in metropolitan gas distribution networks by the assessment of their probability of failure. In this respect, after the identification of the factors leading to failure, the main problem is the historical data on failures, which is generally limited and incomplete, and often collected for other purposes. Consequently, effective evaluation of the probability of failure must be based on the integration of historical data and knowledge of company experts. The Analytic Hierarchy Process has been used as elicitation method of expert opinion to determine the a priori distribution of gas pipeline failures. A real world case study is presented in which the company expertise has been elicited by an ad hoc questionnaire and combined with the historical data by means of Bayesian inference. The robustness of the proposed methodology has also been tested.     

Selective critique of risk assessments with recommendations for improving methodology and practise

Risk assessments are often criticised for defending activities that could harm the environment and human health. The risk assessments produce numbers which are used to prove that the risk associated with the activity is acceptable. In this way, risk assessments seem to be a tool generally serving business. Government agencies have based their regulations on the use of risk assessment and the prevailing practise is supported by the regulations. In this paper, we look more closely into this critique. Are risk assessments being misused or are risk assessments simply not a suitable tool for guiding decision-making in the face of risks and uncertainties? Is the use of risk assessments not servicing public interests? We argue that risk assessments may provide useful decision support but the quality of the risk assessments and the associated risk assessment processes need to be improved. In this paper, three main improvement areas (success factors) are identified and discussed: (1) the scientific basis of the risk assessments needs to be strengthened, (2) the risk assessments need to provide a much broader risk picture than what is typically the case today. Separate uncertainty analyses should be carried out, extending the traditional probabilistic-based analyses and (3) the cautionary and precautionary principles need to be seen as rational risk management approaches, and their application would, to a large extent, be based on risk and uncertainty assessments.     

A Review of Cognitive Models in Human Reliability Analysis

Human error behavior is determined by both environmental and human factors. In particular, psychological and spiritual factors have a decisive impact on human errors. The human cognitive model not only makes a sound exposition of the generation process and mechanism of human erroneous actions but also improves the accuracy and credibility of human reliability analysis (HRA). Therefore, it helps effectively avoid and prevent human errors in industrial fields. This paper highlights the significant role that the cognitive model has played in HRA. Then, based on an analysis of the nature of human behavior and the classifications of common human errors, several typical cognitive models are summarized in the areas of ergonomics, behavioral science, and cognitive engineering, including a cognitive model related to process, an information‐processing model, a decision‐making and problem‐solving process model, and a cognitive simulation model based on computer technology. Then, cognitive models and the corresponding HRA methods that are applied in the fields of reliability engineering, safety engineering, and risk assessment are reviewed. Finally, some directions and challenges are proposed for the future research of cognitive models applied in HRA methods based on the discussion of current cognitive models used in HRA methods. Copyright © 2016 John Wiley & Sons, Ltd.     

Transportation,handling, and microbial growth performance of molded fiber and expanded polystyrene apple trays

Washington State apples are shipped all over the United States for retail sales. Adequate packaging is needed to minimize apple damage during distribution. Two common materials used for apple trays are expanded polystyrene (EPS) and molded fiber (MF). A recent industry change from MF to EPS trays provoked an interest in exploring the performance difference between the two. Distribution environment hazards include shock, vibration, and compression forces. Prior work has focused only on comparing vibration or shock performance of EPS and MF trays used in protecting apples. In this study, a variety of typical hazards encountered between case pack and retail preparation are considered on a larger sampling scale. The shock, vibration, and interlayer dynamic compressive forces are simulated as well as free‐fall drops possible with manual handling of the cases. A novel device and test method is developed to simulate the forces because of human hands removing trays of apples from the case at retail. Further, a microbial analysis of worst case apple condensation is performed to compare the tray materials effect on apple microbial populations. It is concluded through physical and microbial testing that there is no statistical difference in the physical protection or the growth of microbial populations between EPS and MF apple trays. Most significant tray damage is observed in the EPS trays because of free‐fall drop testing. Apple tray material selection can be shifted to other factors such as cost or consumer preference.