Harold S. Blackman

Human Error Quantification Using Performance Shaping Factors in the SPAR-H Method:

This paper describes a cognitively based human reliability analysis (HRA) quantification technique for estimating the human error probabilities (HEPs) associated with operator and crew actions at nuclear power plants. The method described here, Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR- H) method, was developed to aid in characterizing and quantifying human performance at nuclear power plants. The intent was to develop a defensible method that would consider all factors that may influence performance. In the SPAR-H approach, calculation of HEP rates is especially straightforward, starting with pre-defined nominal error rates for cognitive vs. action-oriented tasks, and incorporating performance shaping factor multipliers upon those nominal error rates.

INTENT: a method for estimating human error probabilities for decisionbased errors

Abstract The development of a method, INTENT, for estimating probabilities associated with decisionbased errors is presented. These errors are not ordinarily incorporated into probabilistic risk assessments (PRAs) due to both the difficulty in postulating such errors and to the lack of a method for estimating their probabilities from existing data. By failing to include decisionbased errors in their analyses, most PRA practitioners seriously underestimate the true contribution of human actions to systems failure. This paper attempts to extend the identification of such errors and to quantify them. Two sources, Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR) and licensee event reports (LERs) were reviewed and two methods, HSYS and SNEAK, were used to identify a generic list of twenty potential errors which may be manifest as erroneous acts. Four categories of influence emerged from the data: consequence, attitudes, response set, and dependency. Corresponding human error probabilities (HEPs) for each error were generated by expert judgment methods. Lower and upper bounds for the HEPs for each error were determined by positing a situation reflecting optimized and degraded performance shaping factors, respectively. To allow analysts the opportunity to refine these extreme HEP values when evaluating a particular scenario of interest, normalization procedures were conducted and generic importance weights were computed for each of 11 performance shaping factors (PSFs) believed to affect the 20 decisionbased errors. It is believed by the authors that PSFs constitute a performance influence which, in some cases, such as in that for training, can serve to either augment or reduce the intellectual resources used by people to successfully accomplish tasks. These derived importance weights are used in conjunction with situation specific PSF ratings to compute a composite PSF score which, in turn, is mapped onto an HEP distribution. Distribution assumptions are presented and a function defining the relationship between composite PSF scores and HEPs is presented for use by the analyst.

The Use of Empirical Data Sources in HRA

Abstract This paper presents a review of available information related to human performance to support Human Reliability Analysis (HRA) performed for nuclear power plants (NPPs). A number of data sources are identified as potentially useful. These include NPP licensee event reports, augmented inspection team reports, operator requalification data, results from the literature in experimental psychology, and the Aviation Safety Reporting System. The paper discusses how utilizing such information improves our capability to model and quantify human performance. In particular, the paper discusses how information related to performance shaping factors can be extracted from empirical data to determine their size effect, their relative effects, as well as their interactions. The paper concludes that appropriate use of existing sources can help addressing some of the important issues we are currently facing in HRA.

Simplified Expert Elicitation Procedure for Risk Assessment of Operating Events

This report describes a simplified, tractable, and usable procedure within the US Nuclear Regulator Commission (NRC) for seeking expert opinion and judgment. The NRC has increased efforts to document the reliability and risk of nuclear power plants (NPPs) through Probabilistic Risk Assessment (PRA) and Human Reliability Analysis (HRA) models. The Significance Determination Process (SDP) and Accident Sequence Precursor (ASP) programs at the NRC utilize expert judgment on the probability of failure, human error, and the operability of equipment in cases where otherwise insufficient operational data exist to make meaningful estimates. In the past, the SDP and ASP programs informally sought the opinion of experts inside and outside the NRC. This document represents a formal, documented procedure to take the place of informal expert elicitation. The procedures outlined in this report follow existing formal expert elicitation methodologies, but are streamlined as appropriate to the degree of accuracy required and the schedule for producing SDP and ASP analyses.

Applying sneak analysis to the identification of human errors of commission

Abstract Sneak analysis was adapted for use in identifying human errors of commission. Flow diagrams were developed to guide the analyst through a series of questions aimed at locating sneak paths, sneak indications, sneak labels, and sneak timing. An illustration of the application of this methodology in a nuclear environment is given.

Simplified plant analysis risk (SPAR) human reliability analysis (HRA) methodology: Comparisons with other HRA methods

The 1994 Accident Sequence Precursor (ASP) human reliability analysis (HRA) methodology was developed for the U.S. Nuclear Regulatory Commission (USNRC) in 1994 by the Idaho National Engineering and Environmental Laboratory (INEEL). It was decided to revise that methodology for use by the Simplified Plant Analysis Risk (SPAR) program. The 1994 ASP HRA methodology was compared, by a team of analysts, on a point-by-point basis to a variety of other HRA methods and sources. This paper briefly discusses how the comparisons were made and how the 1994 ASP HRA methodology was revised to incorporate desirable aspects of other methods. The revised methodology was renamed the SPAR HRA methodology.

Is human reliability relevant to human factors

This paper presents a number of views from a panel discussion on the relationship between human reliability analysis (HRA) and human factors. HRA emerged concurrently with the field of human factors and now features a nearly fifty-year shared history. While built on human factors, HRA distinguished itself early on from human factors due to its emphasis on predicting human performance. While one of the major focus areas of human factors has been improving the design of novel systems to optimize human performance, HRA has largely focused on predicting human performance for as-built systems. Over time, as HRA became closely tied particularly to the nuclear energy industry, it increasingly became a field associated more with reliability engineering than human factors. Yet, the similarity to human factors has not abated, nor has the opportunity for the two fields to cooperate. Human factors research provides the empirical basis to support predicting human performance in HRA. Importantly, HRA continues to benefit human factors by providing: (1) a framework for modeling human performance, (2) an example of how a human factors discipline can be seamlessly integrated with an engineering field, and (3) insights on how predictive modeling may be used as a system design tool.

Development of a Behaviorally Based Human Reliability Analysis Method

Human reliability analysis (HRA) assesses the safety and risk significance of human tasks. This paper describes the development and testing of a behaviorally based human reliability analysis method. A general criticism of HRA methods is the inability to tie HRA methods back to first principles in human behavior. The method described here, developed for the accident sequence precursor (ASP) program of the U. S. Nuclear Regulatory Commission (NRC), begins by first describing an information processing model of human behavior, and then using it to define a comprehensive list of factors that influence human performance. These psychological factors are then distilled into the practical and operational factors more commonly identified in nuclear power plant operation. Appropriate adjustments for level of detail are then made to the factors and a further model developed to evaluate the effect of dependency between human actions. The application of the method to the ASP models for two nuclear power plants is discussed.

Modeling the influence of errors of commission on success probability

Abstract A new method for modeling the influence of errors of commission is presented. This method extends the modeling done in human reliability assessment (HRA) event trees to more accurately represent the population of potential human errors associated with errors of commission. The modified HRA event trees are called commission event trees (COMETs). The fundamental difference between COMETs and HRA event trees is that COMETs model errors of commission and deal with the problem of cascading errors often encountered when errors are either intentional or latent in nature. An illustration of the application of COMET is given for an error of intention in a nuclear control scenario.

Socio-Technical Organization and Cognition: A Socio-Organizational Approach to Risk Based Assessment and Design

The role of socio-technical factors (i.e., management and organizational factors) in events has recently been acknowledged in a growing number of studies. These factors influence systems performance in the workplace through their contribution to: worker cognition and sensitivity to safe practices, adherence to procedures, and the implementation of safety practices including design practices and procedures review. Modern day risk assessment approaches such as probabilistic risk assessment (PRA) methods are particularly challenged in considering socio-technical factors as part of the risk assessment process. The development of a socio-technical modeling framework to help interpret events will facilitate integration of these factors in PRA. This paper presents a preliminary socio-technical model, SOCRATES, applies it to the JCO criticality event, and demonstrates a process by which a number of errors are mapped to various underlying sociotechnical factors.