Importance measures for performance shaping factors of human reliability analysis (original) (raw)
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Open Journal of Safety Science and Technology, 2016
Human error plays a pivotal rule in all aspects of engineering activities such as operation, maintenance, design, inspection and installation. Industries are faced up to various significant human errors and consequently irrecoverable loss each year, but still there is a lack of heeds to qualify as well as quantify such errors. This paper tries to estimate the probability of failure in lifting of light structures in sea by considering human errors. To do this, a strong qualifying tool such as Functional Resonance Analysis Method (FRAM) is applied to develop high risk accident scenario by considering non-linear socio-technical interaction in system. Afterwards, human error probability is calculated for each activity using the Success Likelihood Index Method (SLIM) based on resonance that is carried out in FRAM network. Then Event Tree (ET) is conducted to assess consequences. The present study is aimed to interpret the importance of attentions to qualitative methods in implementing quantitative risk analyses to consider human error in calculation. The final outcome depicts that considering human error in the process of risk assessment will result in more accuracy and reliability in final Risk Probability Number (RPN). The developed methodology has been applied to a case study of an offshore installation.
The SPAR-H human reliability analysis method
This paper presents a simple human reliability analysis (HRA) method for estimating the human error probabilities associated with operator and crew actions and decisions in response to initiating events at commercial US nuclear power plants. The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method was developed to support development of plantspecific probabilistic risk analysis (PRA) models for the US Nuclear Regulatory Commission (NRC) Office of Regulatory Research and recently has been used to help support the Reactor Oversight Process. SPAR-H addresses the need for the regulator to account for human errors when: (a) performing safety studies such as probabilistic risk analysis; (b) helping to risk-inform the inspection process; (c) reviewing special issues; and (d) helping to risk-inform regulation. The SPAR-H approach decomposes probability into contributions from diagnosis failures and action failures, accounts for the context associated with human failure events by using performance-shaping factors (PSFs), and dependency assignment to adjust a base-case Human Error Probability (HEP), guidance on how to assign the appropriate value of the PSF and offers an adjustment factor to reduce double counting of influencing factors shared by PSFs. This paper discusses development of the method and reviews the application of SPAR-H to a sample problem. Strengths and limitations of the method will also be discussed.
Human reliability assessment during offshore emergency conditions
Safety Science, 2013
This paper presents a quantitative approach to Human Reliability Analysis (HRA) during emergency conditions in an offshore environment. Due to the lack of human error data for emergency conditions most of the available HRA methodologies are based on expert judgment techniques. Expert judgment suffers from uncertainty and incompleteness due to partial ignorance, which is not considered in available techniques. Furthermore, traditional approaches suffer from unrealistic assumptions regarding the independence of the human factors and associated actions. The focus of this paper is to address the issue of handling uncertainty associated with expert judgments with evidence theory and to represent the dependency among the human factors and associated actions using a Bayesian Network (BN) approach. The Human Error Probability (HEP) during different phases of an emergency is then assessed using a Bayesian approach integrated with an evidence theory approach. To understand the applicability of the proposed approach, results are compared with an analytical approach: Success Likelihood Index Methodology (SLIM). The comparative study demonstrates that the proposed approach is effective in assessing human error likelihood. In addition to being simple, it possesses additional capability, such as updating as new information becomes available and representing complex interaction. Use of the proposed method would provide an effective mechanism of human reliability assessment in hazardous operations.
2009
Since the Reactor Safety Study in the early 1970's, human reliability analysis (HRA) has been evolving towards a better ability to account for the factors and conditions that can lead humans to take unsafe actions and thereby provide better estimates of the likelihood of human error for probabilistic risk assessments (PRAs). The purpose of this paper is to provide an overview of recent reviews of operational events and advances in the behavioral sciences that have impacted the evolution of HRA methods and contributed to improvements. The paper discusses the importance of human errors in complex human-technical systems, examines why humans contribute to accidents and unsafe conditions, and discusses how lessons learned over the years have changed the perspective and approach for modeling human behavior in PRAs of complicated domains such as nuclear power plants. It is argued that it has become increasingly more important to understand and model the more cognitive aspects of human performance and to address the broader range of factors that have been shown to influence human performance in complex domains. The paper concludes by addressing the current ability of HRA to adequately predict human failure events and their likelihood.
A Graphical Model Based on Performance Shaping Factors for Assessing Human Reliability
IEEE Transactions on Reliability, 2017
Human reliability assessment (HRA) is an aspect of risk analysis concerned with identifying, analyzing and quantifying the causes, contributions and occurrence of human failures. Applications of existing HRA methods are often domain-specific, and difficult to implement even for experts. Also, due to the lack of empirical data, managing uncertainty is important, if not essential. In view of such limitations, we propose a new and comprehensive HRA methodology acronymed 'PRELUDE' (Performance shaping factor based human REliability assessment using vaLUation-baseD systEms). It is a quantitative and qualitative HRA methodology, applied to railway operations. The qualitative part characterizes a safety critical situation using Performance Shaping Factors (PSFs). The PSFs are identified from domain specific human factors and PSF-based studies. The quantitative proposition is a framework of a graphical model (Valuation-based System) and belief functions theory. Appropriate representation and handling of all types of uncertainties, and combination of conflicting expert opinions is considered in this framework. To aid in the choice of appropriate combination method, combined expert data is discussed and compared using quantitative metrics. PRELUDE allows quantifying a human failure event given an operational context. Sensitivity analysis is used to establish a priority ranking among the PSFs. Finally, application on a railway accident scenario describes usage and applicability of our proposition.
A Model-Based Human Reliability Data Collection
In response to a Staff Requirements Memorandum (SRM) to the Advisory Committee on Reactor Safeguards (ACRS), the US Nuclear Regulatory Commission (NRC) has undertaken a research effort to create a consensus approach to human reliability analysis (HRA). This paper provides an overview of the approach being developed. The approach introduces the " crew response tree " (CRT) concept, which depicts the human failure events in a manner parallel to the PRA event tree process, provides a structure for capturing the " context " associated with the human failure events under analysis, and uses the Information Processing Model as a platform to identify potential failures. It incorporates behavioral science knowledge by providing the decompositions of human failures/failure mechanisms/failure factors built from a top-down and bottom-up approach, the latter reflecting those findings from scientific papers that document theories and data of interest. The structure provides a roadmap for incorporating the phenomena with which crews would be dealing, the plant characteristics (e.g., design, indications, procedures, training), and human performance capabilities (awareness, decision, action). In terms of quantification, the approach uses the typical PRA conditional probability expression, which is delineated to a level adequate for associating the probability of a human failure event with conditional probabilities of the associated contexts, failure mechanisms, and the underlying factors (e.g., performance shaping factors). Such mathematical formulation can be used to directly estimate HEPs using various data sources (e.g., expert estimations, anchor values, simulator or historical data), or can be modified to interface with existing quantification approaches. 1
A Model-Based Human Reliability Analysis Methodology
This paper provides an overview of a comprehensive framework, corresponding set of techniques, and guidelines for performing HRA analysis in various nuclear power plant risk-informed applications such as full-power PRA and event assessment. The work on the methodology has been supported through a number of projects and grants sponsored by the US NRC. The qualitative analysis part of the approach introduces the "crew response tree" (CRT), providing a structure for capturing the context associated with the human failure events (HFE), including EOO and EOC. It also uses a team-centered version of the IDA model and "macro cognitive" abstractions of crew behavior, as well as other relevant findings from cognitive psychology and operating experience, in order to identify potential causes of failures and influencing factors during procedure-driven and knowledge-supported crew-plant interactions. The result of the analysis is the set of identified HFEs and the likely sce...
Through the reactor oversight process (ROP), the U.S. Nuclear Regulatory Commission (NRC) monitors the performance of utilities licensed to operate nuclear power plants. The process is designed to assure public health and safety by providing reasonable assurance that licensees are meeting the cornerstones of safety and designated crosscutting elements. The reactor inspection program, together with performance indicators (PIs), and enforcement activities form the basis for the NRC's risk-informed, performance based regulatory framework.
A Human Reliability Approach Developed for Data Collection
In response to a Staff Requirements Memorandum (SRM) to the Advisory Committee on Reactor Safeguards (ACRS), the US Nuclear Regulatory Commission (NRC) has undertaken a research effort to create a consensus approach to human reliability analysis (HRA). This paper provides an overview of the approach being developed. The approach introduces the " crew response tree " (CRT) concept, which depicts the human failure events in a manner parallel to the PRA event tree process, provides a structure for capturing the " context " associated with the human failure events under analysis, and uses the Information Processing Model as a platform to identify potential failures. It incorporates behavioral science knowledge by providing the decompositions of human failures/failure mechanisms/failure factors built from a top-down and bottom-up approach, the latter reflecting those findings from scientific papers that document theories and data of interest. The structure provides a roadmap for incorporating the phenomena with which crews would be dealing, the plant characteristics (e.g., design, indications, procedures, training), and human performance capabilities (awareness, decision, action). In terms of quantification, the approach uses the typical PRA conditional probability expression, which is delineated to a level adequate for associating the probability of a human failure event with conditional probabilities of the associated contexts, failure mechanisms, and the underlying factors (e.g., performance shaping factors). Such mathematical formulation can be used to directly estimate HEPs using various data sources (e.g., expert estimations, anchor values, simulator or historical data), or can be modified to interface with existing quantification approaches. 1