On the way to assess errors of commission

Sträter, Oliver; Dang, Vinh N.; Kaufer, B.; Daniels, Ardela

doi:10.1016/j.ress.2003.09.004

Cited by 18 publications

(10 citation statements)

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“…First generation HRA methods are demonstrated with experience and use, not able to provide sufficient prevention and adequately perform its duties [10]. The criticism of base to the adequacy of the traditional methods is that these approaches have a tendency to be descriptive of events in which only the formal aspects of external behaviour are observed and studied in terms of errors, without considering reasons and mechanisms that made them level of cognition.…”

Section: First Generation Hra Methodsmentioning

confidence: 99%

“…These methods ignore the cognitive processes that underlie human performance and, in fact, possess a cognitive model without adequate human and psychological realism. They are often criticised for not having considered the impact of factors such as environment, organisational factors, and other relevant PSFs; errors of commission; and for not using proper methods of judging experts [4,10,25]. Swain remarked that "all of the above HRA inadequacies often lead to HRA analysts assessing deliberately higher estimates of HEPs and greater uncertainty bounds, to compensate, at least in part, for these problems" [4].…”

Section: First Generation Hra Methodsmentioning

confidence: 99%

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An Overview of Human Reliability Analysis Techniques in Manufacturing Operations

Pasquale¹,

Iannone²,

Miranda³

et al. 2013

Operations Management

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In recent years, there has been a decrease in accidents due to technical failures through technological developments of redundancy and protection, which have made systems more reliable. However, it is not possible to talk about system reliability without addressing the failure rate of all its components; among these components, "man" – because his rate of error changes the rate of failure of components with which he interacts. It is clear that the contribution of the human factor in the dynamics of accidents – both statistically and in terms of severity of consequences – is high [2]. Although valid values are difficult to obtain, estimates agree that errors committed by man are responsible for 60–90% of the accidents; the remainder of accidents are attributable to technical deficiencies [2,3,4]. The incidents are, of course, the most obvious human errors in industrial systems, but minor faults can seriously reduce the operations performances, in terms of productivity and efficiency. In fact, human error has a direct impact on productivity because errors affect the rates of rejection of the product, thereby increasing the cost of production and possibly reduce subsequent sales. Therefore, there is need to assess human reliability to reduce the likely causes of errors [1]. The starting point of this work was to study the framework of today’s methods of human reliability analysis (HRA): those quantitative of the first generation (as THERP and HCR), those qualitative of second (as CREAM and SPAR-H), and new dynamic HRA methods and recent improvements of individual phases of HRA approaches. These methods have, in fact, the purpose of assessing the likelihood of human error – in industrial systems, for a given operation, in a certain interval of time and in a particular context – on the basis of models that describe, in a more or less simplistic way, the complex mechanism that lies behind the single human action that is potentially subject to error [1]. The concern in safety and reliability analyses is whether an operator is likely to make an incorrect action and which type of action is most likely [5]. The goals defined by Swain and Guttmann (1983) in discussing the THERP approach, one of the first HRA methods developed, are still valid: The objective of a human reliability analysis is ‘to evaluate the operator’s contribution to system reliability’ and, more precisely, ‘to predict human error rates and to evaluate the degradation to human–machine systems likely to be caused by human errors in association with equipment functioning, operational procedures and practices, and other system and human characteristics which influence the system behavior’ [7]. The different HRA methods analysed allowed us to identify guidelines for determining the likelihood of human error and the assessment of contextual factors. The first step is to identify a probability of human error for the operation to be performed, while the second consists of the evaluation through appropriate multipliers, the impact of environmental, and the behavio...

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Section: First Generation Hra Methodsmentioning

confidence: 99%

Section: First Generation Hra Methodsmentioning

confidence: 99%

An Overview of Human Reliability Analysis Techniques in Manufacturing Operations

Pasquale¹,

Iannone²,

Miranda³

et al. 2013

Operations Management

View full text Add to dashboard Cite

show abstract

“…First-generation HRA methods ignore the cognitive processes that underlie human behaviour, in fact, they have a cognitive model without realism and they are psychologically inadequate. They are often criticized for not considering some factors' impact such as environment, organizational factors and other relevant PSFs, and inadequate treatment of commission errors and expert judgment [14,18,19]. Hollnagel [18] noted that "all inadequacies of previous HRA methods often lead analysts to perform an HEP evaluation deliberately high and with greater uncertainty limits to compensate, at least in part, these problems" [18].…”

Section: Human Reliability Assessment Reviewmentioning

confidence: 99%

Fuzzy Human Reliability Analysis: Applications and Contributions Review

Baziuk

Rivera

Leod

2016

Advances in Fuzzy Systems

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The applications and contributions of fuzzy set theory to human reliability analysis (HRA) are reassessed. The main contribution of fuzzy mathematics relies on its ability to represent vague information. Many HRA authors have made contributions developing new models, introducing fuzzy quantification methodologies. Conversely, others have drawn on fuzzy techniques or methodologies for quantifying already existing models. Fuzzy contributions improve HRA in five main aspects: (1) uncertainty treatment, (2) expert judgment data treatment, (3) fuzzy fault trees, (4) performance shaping factors, and (5) human behaviour model. Finally, recent fuzzy applications and new trends in fuzzy HRA are herein discussed.

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“…For example, since the objective of method development is to ensure the correctness and completeness of a method in terms of correct representation of the complexity of the situation and human behavior to be assessed, it requires many types of data at varying levels of information detail. In contrast, for the review of an HRA or PRA, data are needed to generate repeatable results for comparisons between different plants or different countries; therefore, data needed for HRA or PRA reviews tend to be more scenario-or task-specific compared to method development [10,22]. In addition, HRA data needs also vary with HRA methods.…”

Section: Data Needsmentioning

confidence: 99%

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

Liao

Groth

Marie

2015

Reliability Engineering & System Safety

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This article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. These challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.

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On the way to assess errors of commission

Cited by 18 publications

References 1 publication

An Overview of Human Reliability Analysis Techniques in Manufacturing Operations

An Overview of Human Reliability Analysis Techniques in Manufacturing Operations

Fuzzy Human Reliability Analysis: Applications and Contributions Review

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

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