Due to the scarcity of Human Reliability Analysis (HRA) data, one of the key elements of any HRA analysis is use of engineering judgment. The Electric Power Research Institute (EPRI) HRA Calculator guides the user through the steps of any HRA analysis and allows the user to choose among analytical HRA methods. It applies Accident Sequence Evaluation Program (ASEP), Technique for Human Error Rate Prediction (THERP), the HCR/ORE Correlation, and the Caused Based Decision Tree Method (CBDTM). This program is intended to produce consistent results among different analysts provided that the initial information is similar. Even with this analytical approach, an HRA analyst must still render several judgments. The objective of this study was to evaluate the use of engineering judgment applied to the quantification of post-initiator actions using the HRA Calculator. The Comanche Peak Steam Electric Station (CPSES) Level 1 Probabilistic Risk Assessment (PRA) HRA was used as a database for examples and numerical comparison. Engineering judgments were evaluated in the following ways: 1) Survey of HRA experts. Two surveys were completed, and the participants provided a range of different perspectives on how they individually apply engineering judgment. 2) Numerical comparison among the three methods. 3) Review of CPSES HRA and identification of judgments and the effects on the overall results of the database. The results of this study identified thirteen areas in which an HRA analyst must interpret and render judgments on how to quantify a Human Error Probability (HEP) and recommendations are provided on how current industry practitioners render these same judgments. The areas are: identification and definition of actions to be modeled, identification and definition of actions to be modeled, definition of critical actions, definition of cognitive portion of the action, choice of methodology, stress level, rule-, skill- or knowledge-based designation, timing information, training, procedures, human interactions with hardware, recoveries and dependencies within an action, and review of final HEP.

No description available.

No description available.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Malgorzata Rys / Catheter associated urinary tract infections (CAUTIs) are a source of concern in the healthcare industry because they occur more frequently than other healthcare associated infections and the rates of CAUTI have not improved in recent years. The use of urinary catheters is common among patients; between 15 and 25 percent of all hospital patients will use a urinary catheter at some point during their hospitalization (CDC, 2016). The prevalence of urinary catheters in hospitalized patients and high CAUTI occurrence rates led to the application of human factors engineering to develop a tool to help hospitals reduce CAUTI rates. Human reliability analysis techniques are methods used by human factors engineers to quantify the probability of human error in a system. A human error during a catheter insertion has the opportunity to introduce bacteria into the patient’s system and cause a CAUTI; therefore, human reliability analysis techniques can be applied to catheter insertions to determine the likelihood of a human error. A comparison of three human reliability analysis techniques led to the selection of the Cognitive Reliability and Error Analysis Method (CREAM). To predict a patient’s probability of developing a CAUTI, the human error probability found from CREAM is incorporated with several health factors that affect the patient’s risk of developing CAUTI. These health factors include gender, duration, diabetes, and a patient’s use of antibiotics, and were incorporated with the probability of human error using fuzzy logic. Membership functions were developed for each of the health factors and the probability of human error, and the centroid defuzzification method is used to find a crisp value for the probability of a patient developing CAUTI. Hospitals that implement this tool can choose risk levels for CAUTI that places the patient into one of three zones: green, yellow, or red. The placement into the zones depends on the probability of developing a CAUTI. The tool also provides specific best practice interventions for each of the zones.

Grandes avanços têm sido alcançados com as técnicas de análise de segurança dos sistemas essenciais de navegação e performance das aeronaves resultando na diminuição das taxas de acidentes ao longo dos últimos anos. O Relatório de Segurança de 2017 da EASA (European Agency Safety Aviation) apresenta um relevante aumento do número de acidentes não fatais. Este resultado positivo leva ao aumento das evacuações de emergência. O Relatório de Segurança de 2016 da IATA (International Air Transport Association) mostra que em 35% dos acidentes com sobreviventes em Jatos e 55% dos acidentes com sobreviventes em turbo hélice ocorreram com evacuação de emergência. Diante deste cenário, a confiabilidade humana torna-se relevante na interface destes passageiros com o projeto de segurança da cabine durante o procedimento de evacuação de emergência. Para avaliar as características e a contribuição desta interface no sucesso do procedimento de evacuação, é proposta uma metodologia para a análise da interação humana com este sistema estabelecendo um diagrama causal genérico com o objetivo de estudar o mecanismo do erro humano nesta interface. A metodologia proposta utiliza a abordagem das Redes Bayesianas apoiada pela lógica Fuzzy para modelar os Fatores de Desempenho Humano e para verificar, através da diagnose e inferência causal, quais fatores mais influenciam o desempenho humano na execução das tarefas neste ambiente de emergência. Esta pesquisa apresenta uma aplicação da metodologia proposta para analisar as tarefas do ensaio de evacuação de emergência de uma aeronave, focando na quantificação do erro humano na interface com o projeto de segurança da cabine da aeronave. Os resultados da aplicação identificaram o fator situacional: cartão de segurança, marcas na asa e escorregadores, e os fatores individuais: conhecimento e habilidades: interpretação e percepção como aqueles que mais influenciaram no teste do procedimento de evacuação de emergência de uma aeronave. / Great advances have been achieved with the safety assessment techniques of essential aircraft navigation and performance systems due to decreasing of fatal accident rates in recent years. The EASA Annual Safety Report 2017 (European Agency Safety Aviation) presents a relevant increase of non-fatal accidents. This positive results leads to increasing of emergency evacuation. The IATA Safety Report 2016 (International Air Transport Association) presents that 35% of survival accidents with Jet and 55% of survival accidents with Turboprop occurred with emergency evacuation. In view of this scenario, human reliability becomes relevant in the interface of these passengers with the cabin safety design during emergency evacuation procedure. To evaluate this interface features, and the contribution of this interface in the success of evacuation procedure, it is proposed a method for analyzing the human interaction within the system, to establish a generic causal framework aiming at the study of the human error mechanism. The proposed methodology uses the Bayesian Networks approach supported by Fuzzy logic for modelling Human Performance Factors and for verifying, through diagnosis and causal inference, which factors most influence human performance in the execution of tasks in this emergency environment. This research presents an application of this approach to analyze the tasks of the emergency evacuation testing from an aircraft, focusing on the quantification of human error in the interface with aircraft cabin safety design. The results of application has identified the situational factor: safety card, marks on the wing and escape slides, and the individual factors: knowledge and abilities: interpretation and perception as one those most of influenced the emergency evacuation test procedure from an aircraft.

Outcomes from surgery are dependent upon technical performance, as demonstrated by the variability that exists in outcomes achieved by different surgeons following surgery for rectal cancer. It is possible to improve such outcomes by focused training and the adoption of specific surgical techniques, such as the total mesorectal excision (TME) training programme in Stockholm which reduced local recurrence rates of cancer by 50%. It is generally accepted that good surgical technique is the enactment of a series of positive surgical actions, and the avoidance of errors. However, the constituents of good surgical technique for rectal cancer have not yet been studied in sufficient detail to identify the specific associations between individual steps and their consequences. In this study the ergonomic principles of human reliability analysis (HRA) were applied to video recordings of rectal cancer surgery. A system of error definition and identification was developed, utilising a bespoke software solution designed for the project. Calculation of optimal camera angles and position was determined in a virtual operating theatre. Analysis of synchronised footage from multiple camera views was performed, through which over 6,000 errors were identified across 14 procedural tasks. The sequences of events contributing to these errors are reported, and a series of error reduction mechanisms formulated for rectal cancer surgery.

Safeguards and security (S&S) systems for nuclear facilities include material control and accounting (MC&A) and a physical protection system (PPS) to protect nuclear materials from theft, sabotage and other malevolent human acts. The PPS for a facility is evaluated using probabilistic analysis of adversary paths on the basis of detection, delay, and response timelines to determine timely detection. The path analysis methodology focuses on systematic, quantitative evaluation of the physical protection component for potential external threats, and often calculates the probability that the PPS is effective (PE) in defeating an adversary who uses that attack path. By monitoring and tracking critical materials, MC&A activities provide additional protection against inside adversaries, but have been difficult to characterize in ways that are compatible with the existing path analysis methods that are used to systematically evaluate the effectiveness of a site’s protection system. This research describes and demonstrates a new method to incorporate MC&A protection elements explicitly within the existing probabilistic path analysis methodology. MC&A activities, from monitoring to inventory measurements, provide many, often recurring opportunities to determine the status of critical items, including detection of missing materials. Human reliability analysis methods are applied to determine human error probabilities to characterize the detection capabilities of MC&A activities. An object-based state machine paradigm was developed to characterize the path elements and timing of an insider theft scenario as a race against MC&A activities that can move a facility from a normal state to a heightened alert state having additional detection opportunities. This paradigm is coupled with nuclear power plant probabilistic risk assessment techniques to incorporate the evaluation of MC&A activities in the existing path analysis methodology. Event sequence diagrams describe insider paths through the PPS and also incorporate MC&A activities as path elements. This work establishes a probabilistic basis for incorporating MC&A activities explicitly within the existing path analysis methodology to extend it to address insider threats. The analysis results for this new method provide an integrated effectiveness measure for a safeguards and security system that addresses threats from both outside and inside adversaries. / text

Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-06-20T22:54:11Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Marilia Abílio Ramos.pdf: 6997571 bytes, checksum: 1514e881a0919bde7d2b45038eed3a91 (MD5) / Made available in DSpace on 2018-06-20T22:54:11Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Marilia Abílio Ramos.pdf: 6997571 bytes, checksum: 1514e881a0919bde7d2b45038eed3a91 (MD5) Previous issue date: 2017-04-07 / ANP (Agência Nacional do Petróleo) / Petrobras / The oil industry has grown in recent decades in terms of quantity of facilities and process complexity. However, human and material losses still occur due to major accidents at the facility. The analysis of these accidents reveals that many involve human failures that, if prevented, could avoid such accidents. These failures, in turn, can be identified, modeled and quantified through Human Reliability Analysis (HRA), which forms a basis for prioritization and development of safeguards for preventing or reducing the frequency of accidents. The most advanced and reliable HRA methods have been developed and applied in nuclear power plant operations, while the petroleum industry has usually applied Quantitative Risk Analysis (QRA) focusing on process safety in terms of technical aspects of the operation and equipment. This thesis demonstrates that the use of HRA in oil refining and petrochemical operations allows the identification and analysis of factors that can influence the behavior of operators as well as the potential human errors that can contribute to the occurrence of an accident. Existing HRA methodologies, however, were mainly developed for the nuclear industry. Thus, they may not reflect the specificities of refining and petrochemical plants regarding the interaction of the operators with the plant, the failure modes of the operators and the factors that influence their actions. Thus, this thesis presents an HRA methodology developed specifically for use in this industry, HERO - Human Error in Refinery Operations HRA Methodology. The Phoenix HRA methodology was used as a basis, which has three layers i) a crew response tree (CRT), which models the interaction between the crew and the plant; ii) a human response model, modeled through fault trees, that identifies the possible crew failures modes (CFMs); and (iii) "contextual factors" known as performance influencing factors (PIFs), modeled through Bayesian networks. In addition to building on such a structure, HERO's development relied on interviews with HRA specialists, visitations to a refinery and its control room, and analysis of past oil refineries accidents - four accidents were analyzed in detail. The methodology developed maintains the three-layer structure and has a guideline flowchart for the construction of the CRT, in order to model the team-plant interactions in oil refining and petrochemical operations; it also features CFMs and PIFs developed specifically for this industry, with definitions that make them easily relatable by an analyst. Finally, the methodology was applied to three potential accidental scenarios of refinery operations. In one of these scenarios, it was combined with a QRA to illustrate how an HRA can be applied to a traditional QRA and to demonstrate the influence of PIFs and of human error probability on the final risk. The use of this methodology for HRA of refineries and petrochemical plants operations can enhance this industry safety and allow for solid riskbased decisions. / A indústria de petróleo teve grande crescimento nas últimas décadas em termos de quantidade de instalações e complexidade de processo. No entanto, perdas humanas e materiais ainda ocorrem devido a acidentes graves nas instalações. A análise desses acidentes revela que muitos envolvem falhas humanas que poderiam ser prevenidas de forma a evitar tais acidentes. Estas falhas, por sua vez, podem ser identificadas, modeladas e quantificadas através da Análise de Confiabilidade Humana (ACH), que forma uma base para priorização e desenvolvimento de salvaguardas na prevenção ou redução da frequência de acidentes. Os métodos de ACH mais avançados e confiáveis têm sido desenvolvidos e aplicados nas operações de controle de plantas nucleares; já a indústria de petróleo tem usualmente aplicado a Análise Quantitativa de Risco (AQR) com foco na segurança de processo em termos técnicos da operação e equipamentos. Esta tese demonstra que o uso da ACH em operações de refino e petroquímica possibilita a identificação e análise dos fatores que podem influenciar o comportamento do operador bem como as potenciais falhas humanas que podem contribuir para a ocorrência de um acidente. As metodologias de ACH existentes, no entanto, foram desenvolvidas para a indústria nuclear. Desta forma, elas não refletem as especificidades de refino e petroquímica no que se refere à interação dos operadores com a planta, aos modos de falha dos operadores e aos fatores que influenciam suas ações. Assim, esta tese apresenta uma metodologia de ACH desenvolvida especificamente para uso nessa indústria, a HERO - Human Error in Refinery Operations HRA Methodology. Como base, utilizou-se a Metodologia Phoenix, que possui três camadas i) uma árvore de resposta da equipe (crew response tree - CRT), que modela a interação da equipe com a planta; ii) um modelo de resposta humana, modelado através de árvores de falhas, que identifica os possíveis modos de falhas da equipe (crew failures modes - CFMs); e iii) os “fatores contextuais” conhecidos como fatores de desempenho ou performance influencing factors (PIFs), modelados através de redes Bayesianas. Além de basear-se em tal estrutura, o desenvolvimento da HERO apoiou-se em entrevistas com especialistas em ACH, visitas a uma refinaria e sua sala de controle e na análise de estudos de acidentes passados em refinarias – foram analisados em detalhe quatro acidentes. A metodologia desenvolvida mantém a estrutura de três camadas e possui um fluxograma-guia para construção da CRT, de forma a modelar as interações equipe-planta na operação de refino e petroquímicas; ela também apresenta CFMs e PIFs desenvolvidos especificamente para esta indústria, com definições que os tornam facilmente identificáveis por um analista. Por fim, a metodologia foi aplicada a três cenários acidentais de operações de refinaria. Em um destes cenários, ela foi conjugada a uma AQR de forma a ilustrar como uma ACH pode ser aplicada a uma tradicional AQR e para demonstrar a influência dos PIFs e da Probabilidade de Erro Humano no risco final. Espera-se que o uso da metodologia proposta nesta tese poderá aumentar a segurança em refinarias e petroquímicas e permitir sólidas decisões baseadas no risco.

A APS (Análise Probabilística de Segurança) de instalações industriais é assunto que evoluiu com a complexidade dos sistemas. A princípio, foram desenvolvidas ferramentas e técnicas com o propósito de analisar plantas já instaladas, possibilitando a identificação de fenômenos e mecanismos de falha desconhecidos até então. Com a evolução dos estudos dos acidentes, foram desenvolvidas técnicas aplicáveis às fases pré-operacionais com o propósito de diminuir os riscos na operação. Observa-se, portanto, um bom número de técnicas ideais para analisar projetos prontos ou em fase de conclusão. O mesmo não é observado para a fase de concepção. Apesar disto, cada vez mais especialistas na área de risco propõem que as considerações de segurança são mais eficazes quando ponderadas ao longo de toda a vida dos sistemas críticos. O estudo das APS realizadas no mundo em várias indústrias ajuda a entender o consenso sobre a contribuição potencial destas análises no desenvolvimento de novos sistemas. Para explorar este potencial, é essencial a elaboração de processos e modelos prospectivos que sejam simples, quantitativos, realistas, capazes de alimentar análises no estágio de projeto e que tragam resultados que possam ser interpretados pelos profissionais envolvidos no processo decisório. Estas considerações são extensíveis à ACH (Análise de Confiabilidade Humana), i.e., poucas são as ferramentas que ponderam aspectos operacionais, em especial o desempenho humano, na fase de projeto. A reconhecida contribuição do fator humano em acidentes envolvendo sistemas complexos - por vezes atribuída à falta de ferramentas adequadas para sua consideração na fase de projeto - evidencia ainda mais esta lacuna. Neste contexto, esta tese apresenta o desenvolvimento de uma metodologia e de uma técnica para a consideração precoce da confiabilidade humana na concepção de sistemas complexos, sendo que: 1) o desenvolvimento desta metodologia primou pela facilidade de entendimento de suas etapas e resultados, i.e., procurou-se a inteligibilidade para as pessoas envolvidas no projeto, sendo especialistas em ACH ou não, e; 2) a TECHR (Technique for Early Consideration of Human Reliability) foi concebida com o propósito de desenvolver um modelo prospectivo para o desempenho humano que possa ser explorado na fase de concepção de sistemas, e se baseia no aproveitamento da opinião de especialistas em relação a sistemas que operam ou operaram nos últimos anos para obter estimativas das probabilidades dos diversos tipos de erro humano que podem ocorrer durante a execução de uma ação específica. A metodologia proposta e a TECHR resultam em um procedimento simples e capaz de produzir modelos extremamente úteis na fase de projeto, representando uma contribuição original para o estado da arte da concepção de sistemas baseada em dados incertos. / PSA (Probabilistic Safety Assessment) is an industrial plant issue that has evolved with the complexity of systems. Initially, tools and techniques have been developed with the main purpose of analyzing operational plants, enabling the identification of phenomena and fault mechanisms hitherto not highlighted. With the evolution of accidents studies, some techniques applicable to pre-operational phases were developed in order to reduce the risks in operation. Therefore, a number of techniques adequate to analyze ready or near completion designs can be found. The same is not observed for the early design phase. Despite this, more and more experts in the risk assessment field suggest that safety considerations are most effective when assessed over the whole life of critical systems. Probabilistic safety analyses performed worldwide in various industries help us understand the consensus on the potential contribution of these analyses for developing new systems. To exploit this potential, it is essential to develop processes and prospective models that are simple, quantitative, realistic, able to feed analyses at the design stage and to bring results that can be interpreted by the professionals involved in the decision making process. These considerations are extended to HRA (Human Reliability Analysis), i.e., there are few tools that consider operational aspects, especially human performance, during the design phase. The recognized contribution of the human factor in accidents involving complex systems - sometimes attributed to the lack of suitable tools for its consideration in the design phase - further highlights this gap. In this context, this thesis presents a methodology and a technique developed for the early consideration of human reliability in complex systems design, and: 1) the development of this methodology has prioritized the easy understanding of its steps and results, i.e., its intelligibility for people involved in the system design has been sought, with expertise in HRA or not, and; 2) the technique for early consideration of human reliability (TECHR) was designed for developing a prospective human performance model that can be exploited in the system design phase, and is based on the use of expert opinion in relation to systems that operate or have operated in recent years to obtain estimates of the probabilities of the various types of human error that may occur during the performance of a specific action. The proposed methodology and technique result in a simple procedure capable of producing useful models for the design phase, representing an original contribution to the state of the art of systems conception under uncertainty.

A l'ère actuelle de la décentralisation des ressources et des compétences, le recours aux technologies est devenu de plus en plus fréquent donnant naissance à la collaboration électronique désignée aussi par eCollaboration. Malgré tous les avantages qu'elle apporte, l'implication des technologies dans le processus de la collaboration est loin de résoudre tous ses problèmes. Les travaux de recherche tournant autour de l'évaluation de l'eCollaboration se focalisent sur la détection, l'explication et la résolution de ces problèmes. Dans la littérature, nous avons noté que les travaux d'évaluation accordent un intérêt particulier et non justifié à la composante technologique de l'eCollaboration aux dépens de sa composante humaine. Cette constatation a motivé notre travail de thèse qui a pour objectif de proposer une approche générique d'évaluation des scénarios d'eCollaboration qui prend en compte leurs composantes technologiques et humaines. Face à la délicatesse du contexte de l'eCollaboration et afin d'atteindre l'objectif fixé, notre contribution a suivi une démarche constituée de trois étapes. La première étape a consisté en une analyse basée sur les scénarios qui, suite à l'élaboration de plusieurs abstractions, nous a permis de proposer un modèle conceptuel de l'eCollaboration, un schéma des interactions mises en œuvre et une classification des scénarios d'eCollaboration. La deuxième étape s'est intéressée au développement d'un simulateur de scénarios d'eCollaboration en se basant sur les résultats produits par l'étape précédente. Ce travail nous a permis d'identifier un critère générique de succès de l'eCollaboration à travers la génération de différentes instances de scénarios d'eCollaboration se déroulant dans des conditions variées et l'observation de différents types d'anomalies qui y apparaissent. La troisième étape a porté sur la proposition d'une approche générique d'évaluation basée sur le critère de succès dégagé précédemment et composée de deux niveaux : le premier consiste en une détection des anomalies et le deuxième se focalise sur l'explication de celles-ci en se basant sur une méthode d'analyse de la fiabilité humaine qui considère que toute anomalie détectée est provoquée par une inadéquation des conditions de déroulement qui englobent des causes aussi bien technologiques que humaines. / In the actual area of resource and skills decentralization, the use of technologies has become increasingly common under the name of electronic collaboration or eCollaboration. In spite of its interest, the involvement of technologies in the collaboration process has not solved all its problems. The research works on eCollaboration evaluation are focused on the detection, explanation and resolution of these problems.In the literature, we noted that evaluation works accord a special and unjustified attention to technological component of eCollaboration scenarios at the expense of their human component. This finding has motivated our thesis, aiming to propose a generic evaluation approach of eCollaboration scenarios that considers technological and human eCollaboration components. Given the delicacy of eCollaboration environments and to attain the objective of this work, our contribution follows a three step based process. The first step consists in a scenario based analysis that allowed, after carrying out several abstractions, to suggest a conceptual eCollaboration model, a scheme of the implemented interactions and a classification of eCollaboration scenarios. The second step is focused on the development of an eCollaboration simulator based on results of the previous step. This work allowed us to identify a generic criterion for eCollaboration success by generating different instances of eCollaboration scenarios progressing in varied conditions and observing different types of anomalies occuring on them. The third step was interested in the proposition of a generic evaluation approach based on the success criterion previously detected and composed by two levels : the first is interested in anomaly detection and the second is focused on the explanation of it, using a human reliability analysis method which considers that any detected anomaly is due to inappropriate progress conditions including both technological and human causes.