Human reliability analysis: A critique and review for managers

Human reliability analysis (HRA) has drawn increasing attention from both academic and industry sectors to proactively enhance system safety in recent decades. HRA mainly focuses on identifying, quantifying, modeling, and preventing human error which is recognized as the most complicated and leading cause in major accidents occurring. However, HRA practitioners have often experienced several serious issues in modeling human behavior owing to rare quantitative data, great uncertainty, and considerable complexity of human behavior. In recent years, fuzzy set theory (FST) has been substantially employed to relax some important challenges in numerous domains from healthcare to nuclear power plants. However, few academic attempts have been made to demonstrate how, and to what extent, HRA has been improved through the FST perspective. Accordingly, this chapter reviews state-of-the-art scientific research to reveal the applications, importance, and contributions of FST onto HRA and its related concerns. It explains the abovementioned aspects by (a) predicting human error probability, (b) quantifying influence of performance shaping factors in human performance, (c) modeling intra-dependency among these factors, (d) incorporating human error into probabilistic safety and risk analysis, (e) modeling human behavior and finally, (f) characterizing the uncertainty analysis in HRA by incorporating fuzzy set theory. This chapter offers useful insights into main challenges, gaps, and demands in HRA from both academic and industrial perspectives considering the FST point of view.

The area of Human Reliability Analysis (HRA) has become increasingly important as both the nuclear industry and other high-technology industries have become conscious of the need for risk management and risk analysis. Techniques for performing and validating HRA should clearly be subjects of Human Factors Test and Evaluation attention. However, these issues have received less attention than they probably deserve from the Human Factors Society, despite the key contributions to the field of such HFS Test and Evaluation Group members as Dr. Alan Swain and Dr. David Meister. Issues that should be of interest to the HFS membership were recently raised in a white paper, recently published in “Reliability Engineering and System Safety” as an editorial. This editorial, titled “HRA — Where Shouldst Thou Turn?”, was written by Mr. Ed Dougherty, an HRA practitioner. The editorial takes to task (“pillories”, might not be too strong a term) almost every major technique and method for HRA currently in use, including his own. In addition, he castigates the US Nuclear Regulatory Commission attitudes and philosophies toward HRA use. Due to the controversial nature of the editorial, the journal editor, Dr. Apostolaikis, provided an opportunity for response to some fifteen practitioners/theorists of those mentioned. Some of these responders included such HFS members as Alan Swain, David Woods, Barry Kantowitz, Thomas Ryan, and Jens Rasmussen. However, these responders had no chance to review each others responses, nor has Mr. Dougherty had any opportunity to respond to the responses.

Human reliability in railway engineering: Literature review and bibliometric analysis of the last two decades

The traditional approach to the study of human factors in the maritime field involves the analysis of accidents without considering human factors reliability analysis. The main approach being use to analyze human errors are statistical approach and probability theory approach. Another suitable approach to the study of human factors in the maritime industry is the quasi-experimental field study where variations in performance (for example attention) can be observed as a function of natural variations in performance shaping factors. This paper analyzes result of modeling human error and human reliability emanating from the use of technology on board ship navigation in coastal water area by using qualitative and quantitative tools. Accident reports from marine department are used as empirical material for quantitative analysis. The literature on safety is based on common themes of accidents, the influence of human error resulting from technology usage design, accident report from MAIB and interventions information are use for qualitative assessment. Human reliability assessment involves analysis of accident in waterways, emanating from human-technology factors interface. This paper report an enhancement requirement of the methodological issues with previous research study, monitoring and deduce recommendations for technology modification of the human factors necessary to improve maritime safety performance. The result presented can contribute to rule making, and safety management leading for development of guideline and standards for human reliability risk management for ship navigating within inland and coastal waters.

Given the significant differences in the scope, approach, and underlying models of a relatively wide range of existing HRA methods, there has been a growing interest on the part of HRA method developers and users to empirically test the various methods. To this end, there is an ongoing international effort to begin this process by testing the application of HRA methods to nuclear power plant operating crew performance in the HAMMLAB simulators at the Halden Reactor Project in Norway. Initial efforts in designing and implementing these studies have identified a number of issues associated with structuring the studies in order to allow an adequate and appropriate test of the different methods. This paper focuses on issues associated with applying HRA methods in the context of an empirical study, particularly when a research simulator is used for data collection. Example issues include: determining the scope of the analysis when the methods themselves differ in the scope of the HRA processes they address, accounting for differences between the methods in how they use simulator exercises to support the analysis, addressing the impact of experimental controls on application of methods, and given the low probability of human failure events typically modelled in nuclear power plant probabilistic risk/safety assessments (PRAs/PSAs), the need for analysts to present their results in a somewhat different format than they usually do. These types of issues related to applying HRA methods in the context of empirical studies are discussed and resolutions are proposed.

In the civil aviation sector, human factors is the primary cause of many safety incidents. Aircraft flying, maintenance, and operations are the major tasks that are heavily dependent on professionals; thus, they are subject to human error probability. Human reliability analysis (HRA), which can evaluate human state and managing risk, has been developed over the years to identify, predict, and reduce human failures throughout aircraft operating procedures. Different generations of HRA tools have been developed to quantify the risks that are associated with safety accidents, including such as the Human Error Assessment and Reduction Technique, Technique for Human Error Prediction, Standardized Plant Analysis Risk Human Reliability Analysis, Cognitive Reliability and Error Analysis Methods, and Bayesian Network (BN). However, little is known about how these approaches are applied in aviation safety. This review aimed to systematically examine the current status of research on HRA in aviation accidents. A total of 13 studies were included and encompassed the studies of the first, second, and third generalizations of HRA alone or in combination with other methods (e.g., Improved Analytics Hierarchy Process, Functional Resonance Analysis Methods, Human Factor Analysis and Classification System, and Fault Tree Analysis). Results revealed that the third-generation HRA with BN was frequently used, showing great application potential for flight safety risk prevention and reduction. In the future, testing other third-generation HRA models driven by data in the field of airworthiness is necessary.

Analyses of human reliability during manned spaceflight are crucial because human error can easily arise in the extreme environment of space and may pose a great potential risk to the mission. Although various approaches exist for human reliability analysis (HRA), all these approaches are based on human behavior on the ground. Thus, to appropriately analyze human reliability during spaceflight, this paper proposes a space‐based HRA method of quantifying the human error probability (HEP) for space missions. Instead of ground‐based performance shaping factors (PSFs), this study addresses PSFs specific to the space environment, and a corresponding evaluation system is integrated into the proposed approach to fully consider space mission characteristics. A Bayesian network is constructed based on the cognitive reliability and error analysis method (CREAM) to model these space‐based PSFs and their dependencies. By incorporating the Bayesian network, the proposed approach transforms the HEP estimation procedure into a probabilistic calculation, thereby overcoming the shortcomings of traditional HRA methods in addressing the uncertainty of the complex space environment. More importantly, by acquiring more information, the HEP estimates can be dynamically updated by means of this probabilistic calculation. By studying 2 examples and evaluating the HEPs for an International Space Station ingress procedure, the feasibility and superiority of the developed approach are validated both mathematically and in a practical scenario.

Chapter 7 - Human reliability

This paper introduces a taxonomy and accompanying database designed to bridge previous efforts at human reliability analysis (HRA) and human factors design. Previous HRA database efforts have focused primarily on incident analyses related to the safe operation of nuclear power plants. Previous human factors design standards have largely omitted a thorough investigation of the root causes of human error and their prevention through optimal design. The taxonomy introduced in this paper redresses these shortcomings in three ways: (1) It targets a domain outside the traditional realm of nuclear power plant operations. Specifically, it focuses on information that would be informative to human performance in extreme environments. (2) The taxonomy includes specific fields for incorporating completed HRAs, incident reports, and psychological research using human subjects or simulation. (3) The information captured through the taxonomy is applicable to both HRA and system design. The information format incorporates quantitative information appropriate to HRA such as human error probabilities and qualitative information appropriate to system design such as overall “lessons learned.”

HRA studies in healthcare highlight that PSF (or Influencing Factors—IFs) taxonomies in HRA techniques have been developed and validated in industrial contexts, and as such are not fully applicable to healthcare contexts. In this paper, a modified version of Human Error Assessment and Reduction Technique (HEART), has been developed and tested through an application to the robotic surgical Radical Prostatectomy procedure. Personal and organizational factors were modeled and assessed through an IFs taxonomy validated in the surgical domain, and then systematically translated into the corresponding Error Producing Conditions (EPCs), typical of the HEART method. The results confirmed the importance of adapting HRA methods to the healthcare sector, and added detailed information on what are the most relevant factors that should be captured by an HRA method when applied to surgery. Additionally, the analysis revealed that team related factors have the highest influence on surgeons’ performance (i.e. increase of Human Unreliability Rate) in the context of different surgical tasks.

It is suggested that probabilistic risk assessment (PRA), incorporating human reliability analysis (HRA), provides for greater understanding and refined management of the human elements associated with nuclear plant operation and safety. HRA contributes to enhanced plant performance through better understanding of man-machine interface issues and human error modes. Thus, the HRA process can contribute to the justification of test and maintenance requirements for outage times and testing frequency. The results of HRA can be applied to improve operations, training, engineering, and safety. Combining more detailed human reliability models with probabilistic safety analyses can also help prioritize operational management issues. >

Extended SAFPH℞ (Systems Analysis for Formal Pharmaceutical Human Reliability): Two approaches based on extended CREAM and a comparative analysis

Complex engineered systems, such as nuclear reactors and chemical plants, have the potential for catastrophic failure with disastrous consequences. In recent years, human and management factors have been recognized as frequent root causes of major failures in such systems. However, classical probabilistic risk analysis (PRA) techniques do not account for the underlying causes of these errors because they focus on the physical system and do not explicitly address the link between components' performance and organizational factors. This paper describes a general approach for addressing the human and management causes of system failure, called the SAM (System-Action-Management) framework. Beginning with a quantitative risk model of the physical system, SAM expands the scope of analysis to incorporate first the decisions and actions of individuals that affect the physical system. SAM then links management factors (incentives, training, policies and procedures, selection criteria, etc.) to those decisions and actions. The focus of this paper is on four quantitative models of action that describe this last relationship. These models address the formation of intentions for action and their execution as a function of the organizational environment. Intention formation is described by three alternative models: a rational model, a bounded rationality model, and a rule-based model. The execution of intentions is then modeled separately. These four models are designed to assess the probabilities of individual actions from the perspective of management, thus reflecting the uncertainties inherent to human behavior. The SAM framework is illustrated for a hypothetical case of hazardous materials transportation. This framework can be used as a tool to increase the safety and reliability of complex technical systems by modifying the organization, rather than, or in addition to, re-designing the physical system.

The article presents an analysis of human reliability in the workplace with noise load. The noise load in the selected workplace is quantified within the article by measuring the standardized noise level of ISO 9612: 2009 Acoustics: Determination of occupational noise exposure, Technical method. The analysis of the reliability of the human factor at work, the aim of which is to identify possible human errors and their impact, is a systematic assessment of the factors influencing the manufacturing workers. The analysis includes identification of important sites affected by various errors caused by high noise load. The impact of noise on humans, especially in terms of the upper decibel scale or long-term exposure at the bottom of the decibel scale, leads to the organism loading and human failure during work. The probability of human error occurring in the selected workplace is estimated using the TESEO method.

A bibliometric analysis of human reliability research