Task human reliability analysis for a safe operation of autonomous ship

A probabilistic model of human error assessment for autonomous cargo ships focusing on human–autonomy collaboration

Human reliability analysis (HRA) offers a framework for the identification and evaluation of human errors in large‐scale industries like civil aviation. Dependence assessment plays a critical role in HRA, which is to evaluate the dependence degrees among human error events (HFEs). Dependence assessment necessitates expertise and knowledge from experts, however, due to the complexity of real‐world decision‐making, there inevitably encounters various uncertainties when experts assess the dependence between HFEs. Considering the above issues, this paper introduces a novel method using comparative linguistic expression and hybrid cloud model with the help of the technique for human error rate prediction (THERP) to address dependence assessment in HRA. This paper conducts the comparative linguistic expression to capture the multi‐presentation linguistic opinions from experts and develops a cloud transformation framework that utilizes the hybrid cloud models to represent and handle experts' opinions. Furthermore, two objective weight calculation approaches are proposed to determine the weights of the influential factors and experts without prior known weight information. The dependence degrees between HFEs can be obtained according to the cloud model and the THERP method. Finally, an empirical dependence assessment for HFEs in air traffic control (ATC) demonstrates the rationality and effectiveness of our proposed method. It can be concluded that our proposed method offers an applicable and effective way for dependence assessment in HRA.

An empirical study on the basic human error probabilities for NPP advanced main control room operation using soft control

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.

Chapter 5 - Human Reliability Analysis

Dependency definitions for quantitative human reliability analysis

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.

As part of an overall effort to upgrade and streamline methodologies for safety analyses of nonreactor nuclear facilities at the Savannah River Site (SRS), a human error data base has been developed and is presented in this report. The data base fulfills several needs of risk analysts supporting safety analysis report (SAR) development. First, it provides a single source for probabilities or rates for a wide variety of human errors associated with the SRS nonreactor nuclear facilities. Second, it provides a documented basis for human error probabilities or rates. And finally, it provides actual SRS-specific human error data to support many of the error probabilities or rates. Use of a single, documented reference source for human errors, supported by SRS-specific human error data, will improve the consistency and accuracy of human error modeling by SRS risk analysts. It is envisioned that SRS risk analysts will use this report as both a guide to identifying the types of human errors that may need to be included in risk models such as fault and event trees, and as a source for human error probabilities or rates. For each human error in this report, ffime different mean probabilities or rates are presented to cover a wide range of conditions and influencing factors. The ask analysts must decide which mean value is most appropriate for each particular application. If other types of human errors are needed for the risk models, the analyst must use other sources. Finally, if human enors are dominant in the quantified risk models (based on the values obtained fmm this report), then it may be appropriate to perform detailed human reliability analyses (HRAS) for the dominant events. This document does not provide guidance for such refined HRAS; in such cases experienced human reliability analysts should be involved.

Probabilistic safety assessment (PSA) uses a systematic approach to estimate the reliability and risk of a nuclear power plant (NPP). Over the past few years, severe accident management guidance (SAMG), which delineates the mitigation actions of core melt accidents of an NPP, has been developed to support operators and staff in the technical support center in dealing with those misfortunes. It can be expected that the implementation of SAMG will lower the containment failure frequency and reduce the amount of radionuclides released to the environment during the accident. The plant studied is the Maanshan NPP of Taiwan Power Company, which employs a Westinghouse-designed three-loop pressurized water reactor (PWR) with large dry containment.The containment system event trees and containment phenomenological event trees of the Level-2 PSA model are modified to incorporate the new mitigation actions specified in SAMG. The HCR (Human Cognitive Reliability) and THERP (Technique for Human Error Rate Prediction) models are used to quantify the human error probability (HEP) of all the actions in the Level-2 PSA model. The MAAP4 (Module Accident Analysis Program version 4) code is used to perform thermohydraulic calculations to determine the demand time required in the HEP analysis.The results show that the frequency of most of the source term categories is reduced except the one in which both the reactor pressure vessel and containment are intact. The containment failure frequency is reduced by 14.8% after the implementation of SAMG. The frequency of containment early failure is reduced by 16.2%. Most of the reduction in the containment early failure frequency comes from the reduction in the induced steam generator tube rupture (STGR). The frequency of induced SGTR was reduced from 2.3 × 10–7/reactor·yr to 1.0 × 10–8/reactor·yr.

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.

Human and organizational error data challenges in complex, large-scale systems

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.”

Quantification of performance shaping factors (PSFs)’ weightings for human reliability analysis (HRA) of low power and shutdown (LPSD) operations

As manufacturers and technologies become more complicated, manufacturing errors such as machine failure and human error have also been considered more over the past. Since machines and humans are not error-proof, managing the machines and human errors is a significant challenge in manufacturing systems. There are numerous methods for investigating human errors, fatigue, and reliability that categorized under Human Reliability Analysis (HRA) methods. HRA methods use some qualitative factors named Performance Shaping Factors (PSFs) to estimate Human Error Probability (HEP). Since the PSFs can be considered as the acceleration factors in Accelerated Life Test (ALT). We developed a method for Accelerated Human Fatigue Test (AHFT) to calculate human fatigue, according to fatigue rate and other effective factors. The proposed method reduces the time and cost of human fatigue calculation. AHFT first extracts the important factors affecting human fatigue using Principal Component Analysis (PCA) and then uses the accelerated test to calculate the effect of PSFs on human fatigue. The proposed method has been applied to a real case, and the provided results show that human fatigue can be calculated more effectively using the proposed method.

To support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method and Human Reliability data EXtraction (HuREX). GOMS-HRA was developed to provide cognition-based time and human error probability (HEP) information for dynamic HRA calculations within the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework, while HuREX is a comprehensive HRA data collection method developed by the Korea Atomic Energy Research Institute (KAERI). In this paper, we examine time distributions by using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which proposes an HRA data collection framework to complement full-scope simulator research and gather input data for dynamic HRA by using simplified simulators such as the Rancor Microworld simulator. This paper investigates whether the time required for GOMS-HRA and HuREX task primitives fits 13 statistical distributions. Additionally, we compare and discuss the time distributions obtained from both student operators and professional operators. The result was that this study identified several time distributions for five GOMS-HRA and four HuREX task primitives. In the future, the results of this study are expected to provide objective reference data on the elapsed time for task primitives and aid in realistically simulating scenarios within dynamic HRA.