Incorporating organizational factors into probabilistic safety assessment of nuclear power plants through canonical probabilistic models

The ω-factor approach is a method that explicitly incorporates organizational factors into Probabilistic safety assessment of nuclear power plants. Bayesian networks (BNs) are the underlying formalism used in this approach. They have a structural part formed by a graph whose nodes represent organizational variables, and a parametric part that consists of conditional probabilities, each of them quantifying organizational influences between one variable and its parents in the graph. The aim of this paper is twofold. First, we discuss some important limitations of current procedures in the ω-factor approach for either assessing conditional probabilities from experts or estimating them from data. We illustrate the discussion with an example that uses data from Licensee Events Reports of nuclear power plants for the estimation task. Second, we introduce significant improvements in the way BNs for the ω-factor approach can be constructed, so that parameter acquisition becomes easier and more intuitive. The improvements are based on the use of noisy-OR gates as model of multicausal interaction between each BN node and its parents.     

PC-based probabilistic safety assessment in Japan

The role of PC-based programs is becoming important in the area of PSA. The PC-based program QUEST has been developed to perform level-1 PSA at PNC. This program is an effective tool to examine the effects of the change in the plant design and/or operational procedures. Also, as a part of the full scope PSA activity for the prototype liquid metal fast breeder reactor, a systems analysis code network, which involves some PC-based programs, was developed and has been utilized to perform level-1 PSA with less manpower and more consistency. Further, a living PSA tool is currently being developed for the purpose of maintenance of or improvement in operational safety.     

Analysis of truncation limit in probabilistic safety assessment

A truncation limit defines the boundaries of what is considered in the probabilistic safety assessment and what is neglected. The truncation limit that is the focus here is the truncation limit on the size of the minimal cut set contribution at which to cut off. A new method was developed, which defines truncation limit in probabilistic safety assessment. The method specifies truncation limits with more stringency than presenting existing documents dealing with truncation criteria in probabilistic safety assessment do. The results of this paper indicate that the truncation limits for more complex probabilistic safety assessments, which consist of larger number of basic events, should be more severe than presently recommended in existing documents if more accuracy is desired. The truncation limits defined by the new method reduce the relative errors of importance measures and produce more accurate results for probabilistic safety assessment applications. The reduced relative errors of importance measures can prevent situations, where the acceptability of change of equipment under investigation according to RG 1.174 would be shifted from region, where changes can be accepted, to region, where changes cannot be accepted, if the results would be calculated with smaller truncation limit.     

Marked point process framework for living probabilistic safety assessment and risk follow-up

We construct a model for living probabilistic safety assessment (PSA) by applying the general framework of marked point processes. The framework provides a theoretically rigorous approach for considering risk follow-up of posterior hazards. In risk follow-up, the hazard of core damage is evaluated synthetically at time points in the past, by using some observed events as logged history and combining it with re-evaluated potential hazards. There are several alternatives for doing this, of which we consider three here, calling them initiating event approach, hazard rate approach, and safety system approach. In addition, for a comparison, we consider a core damage hazard arising in risk monitoring. Each of these four definitions draws attention to a particular aspect in risk assessment, and this is reflected in the behaviour of the consequent risk importance measures. Several alternative measures are again considered. The concepts and definitions are illustrated by a numerical example.     

Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies

There will be simplifying assumptions and idealizations in the availability models of complex processes and phenomena. These simplifications and idealizations generate uncertainties which can be classified as aleatory (arising due to randomness) and/or epistemic (due to lack of knowledge). The problem of acknowledging and treating uncertainty is vital for practical usability of reliability analysis results. The distinction of uncertainties is useful for taking the reliability/risk informed decisions with confidence and also for effective management of uncertainty. In level-1 probabilistic safety assessment (PSA) of nuclear power plants (NPP), the current practice is carrying out epistemic uncertainty analysis on the basis of a simple Monte-Carlo simulation by sampling the epistemic variables in the model. However, the aleatory uncertainty is neglected and point estimates of aleatory variables, viz., time to failure and time to repair are considered. Treatment of both types of uncertainties would require a two-phase Monte-Carlo simulation, outer loop samples epistemic variables and inner loop samples aleatory variables. A methodology based on two-phase Monte-Carlo simulation is presented for distinguishing both the kinds of uncertainty in the context of availability/reliability evaluation in level-1 PSA studies of NPP.     

Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment

Traditional fault tree (FT) analysis is widely used for reliability and safety assessment of complex and critical engineering systems. The behavior of components of complex systems and their interactions such as sequence- and functional-dependent failures, spares and dynamic redundancy management, and priority of failure events cannot be adequately captured by traditional FTs. Dynamic fault tree (DFT) extend traditional FT by defining additional gates called dynamic gates to model these complex interactions. Markov models are used in solving dynamic gates. However, state space becomes too large for calculation with Markov models when the number of gate inputs increases. In addition, Markov model is applicable for only exponential failure and repair distributions. Modeling test and maintenance information on spare components is also very difficult. To address these difficulties, Monte Carlo simulation-based approach is used in this work to solve dynamic gates. The approach is first applied to a problem available in the literature which is having non-repairable components. The obtained results are in good agreement with those in literature. The approach is later applied to a simplified scheme of electrical power supply system of nuclear power plant (NPP), which is a complex repairable system having tested and maintained spares. The results obtained using this approach are in good agreement with those obtained using analytical approach. In addition to point estimates of reliability measures, failure time, and repair time distributions are also obtained from simulation. Finally a case study on reactor regulation system (RRS) of NPP is carried out to demonstrate the application of simulation-based DFT approach to large-scale problems.     

Incorporating human factors into the AAMT selection: a framework and process

Human factors such as employee morale and workers skills greatly influence the successful adoption of automated and advanced manufacturing technologies. For newly industrialised countries, the evaluation of these factors before technology selection is particularly paramount. Countries such as Brazil are in the critical early stages of technology adoption and low rates of secondary education and scarcity of technicians reinforce the importance of assessing human factors before the actual technology implementation. Although methods have been proposed to evaluate intangible aspects, the lack of a structured approach to identify and quantify human factors still constitutes a major hurdle. The paper describes a framework and process to assist managers in identifying and evaluating human factors in the selection. The approach was tested in eight companies in Brazil. The results indicated that the main advantages of the proposed approach are: (a) provide a comprehensive justification of technology adoption by identifying and quantifying intangible aspects; and (b) supply a practical process to be incorporated into the selection decision-making process.     

A framework to integrate software behavior into dynamic probabilistic risk assessment

Software plays an increasingly important role in modern safety-critical systems. Although, research has been done to integrate software into the classical probabilistic risk assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. Dynamic probabilistic risk assessment (DPRA) is considered to be the next generation of PRA techniques. DPRA is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This paper develops a methodology to integrate software contributions in the DPRA environment. The framework includes a software representation, and an approach to incorporate the software representation into the DPRA environment SimPRA. The software representation is based on multi-level objects and the paper also proposes a framework to simulate the multi-level objects in the simulation-based DPRA environment. This is a new methodology to address the state explosion problem in the DPRA environment. This study is the first systematic effort to integrate software risk contributions into DPRA environments.     

A min cut-set-wise truncation procedure for importance measures computation in probabilistic safety assessment

A truncation process aims to determine among the set of minimal cut-sets (MCS) produced by a probabilistic safety assessment (PSA) model which of them are significant. Several truncation processes have been proposed for the evaluation of the probability of core damage ensuring a fixed accuracy level. However, the evaluation of new risk indicators as importance measures requires to re-examine the truncation process in order to ensure that the produced estimates will be accurate enough. In this paper a new truncation process is developed permitting to estimate from a single set of MCS the importance measure of any basic event with the desired accuracy level. The main contribution of this new method is to propose an MCS-wise truncation criterion involving two thresholds: an absolute threshold in addition to a new relative threshold concerning the potential probability of the MCS of interest. The method has been tested on a complete level 1 PSA model of a 900 MWe NPP developed by “Electricité de France” (EDF) and the results presented in this paper indicate that to reach the same accuracy level the proposed method produces a set of MCS whose size is significantly reduced.     

Bridging humanitarian operations management and organisational theory

The aim of the editorial note is to introduce the scope of this special issue (SI). We explain our editorial approach and provide a brief summary of eight articles included in the SI following multiple rounds of reviews. Finally, we outline future research questions which stemmed out of the discussions of this SI.     

Uncertainty in probabilistic risk assessment

Dealing with uncertainty is an important and difficult aspect of analyses for complex systems. Such systems involve many uncertainties, and assessing probabilities to represent these uncertainties is itself a complex undertaking utilizing a variety of information sources. At a very basic level, uncertainty is uncertainty, and attempting to distinguish between ‘types of uncertainty’ is questionable. At a practical level, on the other hand, a close look at such distinctions suggests that they are driven by important modelling issues related to model structuring, probability assessment, information gathering, and sensitivity analysis. Anything that brings more attention to these issues should improve the state of the art. However, I would prefer to attack the issues directly instead of working indirectly through the notion of ‘types of uncertainty.’     

Human and organization factors: engineering operating safety into offshore structures

History indicates clearly that the safety of offshore structures is determined primarily by the humans and organizations responsible for these structures during their design, construction, operation, maintenance, and decommissioning. If the safety of offshore structures is to be preserved and improved, then attention of engineers should focus on to how to improve the reliability of the offshore structure ‘system,’ including the people that come into contact with the structure during its life-cycle. This article reviews and discusss concepts and engineering approaches that can be used in such efforts. Two specific human factor issues are addressed: (1) real-time management of safety during operations, and (2) development of a Safety Management Assessment System to help improve the safety of offshore structures.     

Different sets of reliability data and success criteria in a probabilistic safety assessment for a plant producing nitroglycol

The lack of plant-specific reliability data for probabilistic safety assessments usually makes it necessary to use generic reliability data. Justifiably different assessments of plant behaviour (success criteria) lead to different models of plant systems. Both affect the numerical results of a probabilistic safety assessment. It is shown how these results change, if different sets of reliability data and different choices of success criteria for the safety system are employed. Differences in results may influence decisions taken on their basis and become especially important if compliance with a safety goal has to be proved, e.g. a safety integrity level. For the purpose of demonstration an accident sequence from a probabilistic safety assessment of a plant producing nitroglycol is used. The analysis relies on plant-specific reliability data so that it provides a good yardstick for comparing it with results obtained using generic data. The superiority of plant-specific data, which should of course be acquired, cannot be doubted. Nevertheless, plant safety can be improved even if generic data are used. However, the assignment to a safety integrity level may be affected by differences in both data and success criteria.     

Method for assessing human resources management practices and organisational learning factors in a company under lean manufacturing implementation

The implementation of lean production systems is deemed essential for companies that wish to obtain high levels of competitiveness. There are several examples in the literature discussing the adoption of lean roadmaps; however, since such maps emphasise technical factors of lean change, the organisational learning (OL) process and human resources management (HRM) practices tend to be neglected. This article presents a method for assessing the impact of HRM practices and OL factors in a company under lean implementation. The method incorporates concepts of HRM and OL into lean implementation roadmaps, enabling a maturity analysis regarding dimensions of OL at different contextualisation levels, and allowing the proposition of improvements in HRM practices. The proposed method is illustrated in a case study from the automotive parts manufacturing sector.     

General principles of the use of safety factors in design and assessment

Any structure or component can be made to fail if it is subjected to loadings in excess of its strength. Structural integrity is achieved by ensuring that there is an adequate safety margin or reserve factor between strength and loading effects. The basic principles of ‘allowable stress’ and ‘limit state’ design methods to avoid failure in structural and pressure vessel components are summarised. The use of risk as a means of defining adequate safety is introduced where risk is defined as the product of probability of failure multiplied by consequences of failure. The concept of acceptable ‘target’ levels of risk is discussed. The use of structural reliability theory to determine estimates of probability of failure and the use of the reliability index β are described. The need to consider the effects of uncertainties in loading information, calculation of stresses, input data and material properties is emphasised. The way in which the effect of different levels of uncertainty can be dealt with by use of partial safety factors in limit state design is explained. The need to consider all potential modes of failure, including the unexpected, is emphasised and an outline given of safety factor treatments for crack tip dependent and time dependent modes. The relationship between safety factors appropriate for the design stage and for assessment of structural integrity at a later stage is considered. The effects of redundancy and system behaviour on appropriate levels of safety factors are discussed.     

The fit between environmental management systems and organisational learning orientation

With the significant increase in the adoption of environmental management systems (EMSs), it is important to evaluate whether EMSs can improve the business value for organisations that adopt them. However, less is known about whether or not EMSs lead to improved firm performance with only few studies evaluating the broad link between an organisation’s environmental strategies and its business performance. Furthermore, previous studies are inconsistent in their findings about the relationship between environmental strategies and firm performance. We attribute this inconsistency to the misfit between environmental strategies and organisational learning orientation which results in operational inefficiency within the firm. This study tries to find out the proper fit pattern between EMSs and organisational learning orientation by investigating their joint effect on firm performance. Based on survey data from 214 Chinese manufacturing companies, we found that the interactions between EMSs and four dimensions of organisational learning orientation have positive impacts on firm performance. Thus, the proper organisational learning orientation for efficiency and effective EMSs is characterised by high levels of commitment to learning, shared vision, open-mindedness and knowledge sharing.     

Human and management factors in probabilistic risk analysis: the SAM approach and observations from recent applications

Most severe industrial accidents have been shown to involve one or more human errors and these are generally rooted in management problems. The objective of this paper is to draw some conclusions from the experience that we have acquired from three different studies of this phenomenon: (1) the Piper Alpha accident including problems of operations management and fire risks on-board offshore platforms, (2) the management of the heat shield of the NASA space shuttle orbiter, and (3) the roots of patient risks in anaesthesia. This paper describes and illustrates the SAM approach (System-Action-Management) that was developed and used in these studies to link the probabilities of system failures to human and management factors. This SAM model includes: first, a probabilistic risk analysis of the physical system, second, an analysis of the decisions and actions that affect the probabilities of its basic events, and third, a study of the management factors that influence those decisions and actions. In the three initial studies, the analytical links (conditional probabilities) among these three submodels were coarsely quantified based on statistical data whenever available, or most often, on expert opinions. This paper describes some observations that were made across these three studies, for example, the importance of the informal reward system, the difficulties in the communication of uncertainties, the problems of managing resource constraints, and the safety implications of the short cuts that they often imply.