Failure Mode and Effect Analysis a Tool for Reliability Evaluation: Review

The purpose of safety designing is generally not on cost, but rather on saving life and nature, and consequently bargains just with specific risky system failure modes. High reliability levels are the consequence of good designing, scrupulousness and dependably never the aftereffect of re-dynamic failure management. Failure mode and effect analysis (FMEA) is a helpful technique analyzing engineering system reliability. The study focused on the use of FMEA technique to analyze the reliability of engineering equipment or components in selected areas such as: Wind Turbine component, Manufacturing Industries, Medical field and in evaluating the performances of Robots in different fields. The study showed the importance of FMEA as used widely in analyzing engineering equipment with regards to reliability

A monte carlo evolution of the functional resonance analysis method (FRAM) to assess performance variability in complex systems

Modern trends of socio-technical systems analysis suggest the development of an integrated view on technological, human and organizational system components. The Air Traffic Management (ATM) system can be taken as an example of one of the most critical socio-technical system, deserving particular attention in managing operational risks and safety. In the ATM system environment, the traditional techniques of risk and safety assessment may become ineffective as they miss in identifying the interactions and couplings between the various functional aspects of the system itself: going over the technical analysis, it is necessary to consider the influences between human factors and organizational structure both in everyday work and in abnormal situations. One of the newly introduced methods for understanding these relations is the Functional Resonance Analysis Method (FRAM) which aims to define the couplings among functions in a dynamic way. This paper evolves the traditional FRAM, proposing an innovative semi-quantitative framework based on Monte Carlo simulation. Highlighting critical functions and critical links between functions, this contribution aims to facilitate the safety analysis, taking account of the system response to different operating conditions and different risk state. The paper presents a walk-through section with a general application to an ATM process

Resilience engineering for sociotechnical safety management

Modern societies call for a reconsideration of risk and safety, in light of the increasing complexity of human-made systems. Technological artefacts, and the respective role of humans, as well as the organizational contexts in which they operate, dramatically changed in the last decades with an even more severe transformation expected in the future. Rooted in human factors, ergonomics, cognitive engineering, systems thinking and complexity theory, the discipline of resilience engineering proposes innovative approaches for safety challenges imposed by the dynamic, uncertain, and intertwined nature of modern sociotechnical systems. Resilience engineering aims to provide support means for ensuring that systems can sustain required operations under both expected and unexpected conditions. This chapter aims to provide a summary of the scientific field of resilience engineering, as well as a description of two methods common in the field, the resilience analysis grid and the functional resonance analysis method. Following two examples, the chapter proposes a multidisciplinary research agenda for the field

FRAM for systemic accident analysis: a matrix representation of functional resonance

Due to the inherent complexity of nowadays Air Traffic Management (ATM) system, standard methods looking at an event as a linear sequence of failures might become inappropriate. For this purpose, adopting a systemic perspective, the Functional Resonance Analysis Method (FRAM) originally developed by Hollnagel, helps identifying non-linear combinations of events and interrelationships. This paper aims to enhance the strength of FRAM-based accident analyses, discussing the Resilience Analysis Matrix (RAM), a user-friendly tool that supports the analyst during the analysis, in order to reduce the complexity of representation of FRAM. The RAM offers a two dimensional representation which highlights systematically connections among couplings, and thus even highly connected group of couplings. As an illustrative case study, this paper develops a systemic accident analysis for the runway incursion happened in February 1991 at LAX airport, involving SkyWest Flight 5569 and USAir Flight 1493. FRAM confirms itself a powerful method to characterize the variability of the operational scenario, identifying the dynamic couplings with a critical role during the event and helping discussing the systemic effects of variability at different level of analysis

Operationalising FRAM in Healthcare: A critical reflection on practice

Resilience Engineering principles are becoming increasingly popular in healthcare to improve patient safety. FRAM is the best-known Resilience Engineering method with several examples of its application in healthcare available. However, the guidance on how to apply FRAM leaves gaps, and this can be a potential barrier to its adoption and potentially lead to misuse and disappointing results. The article provides a self-reflective analysis of FRAM use cases to provide further methodological guidance for successful application of FRAM to improve patient safety. Five FRAM use cases in a range of healthcare settings are described in a structured way including critical reflection by the original authors of those studies. Individual reflections are synthesised through group discussion to identify lessons for the operationalisation of FRAM in healthcare. Four themes are developed: (1) core characteristics of a FRAM study, (2) flexibility regarding the underlying epistemological paradigm, (3) diversity with respect to the development of interventions, and (4) model complexity. FRAM is a systems analysis method that offers considerable flexibility to accommodate different epistemological positions, ranging from realism to phenomenology. We refer to these as computational FRAM and reflexive FRAM, respectively. Prac-titioners need to be clear about their analysis aims and their analysis position. Further guidance is needed to support practitioners to tell a convincing and meaningful "system story" through the lens of FRAM

Development and piloting of a software tool to facilitate proactive hazard and risk analysis of Health Information Technology

Health Information Technology is now widely promoted as a means for improving patient safety. The technology could also, under certain conditions, pose hazards to patient safety. However, current definitions of hazards are generic and hard to interpret, particularly for large Health Information Technology in complex socio-technical settings, that is, involving interacting clinical, organisational and technological factors. In this article, we develop a new conceptualisation for the notion of hazards and implement this conceptualisation in a tool-supported methodology called the Safety Modelling, Assurance and Reporting Toolset (SMART). Safety Modelling, Assurance and Reporting Toolset aims to support clinicians and engineers in performing hazard identification and risk analysis and producing a safety case for Health Information Technology. Through a pilot study, we used and examined Safety Modelling, Assurance and Reporting Toolset for developing a safety case for electronic prescribing in three acute hospitals. Our results demonstrate the ability of Safety Modelling, Assurance and Reporting Toolset to ensure that the safety evidence is generated based on explicit traceability between the clinical models and Health Information Technology functionality. They also highlight challenges concerning identifying hazards in a consistent way, with clear impact on patient safety in order to facilitate clinically meaningful risk analysis

Impact of the Functional Resonance Analysis Method (FRAM) in safety management at healthcare organisations

Patient safety events are likely to be one of the ten leading causes of death and disability in the world (World Health Organization, 2020). To manage safety, healthcare organisations have traditionally focused on identifying failures, performing analysis of events, and developing strategies to reduce the failures. Several thought leaders have argued that the traditional method is not adequate to manage safety in a complex environment. Their argument is that safety management should not solely focus on what went wrong, it should also include efforts which enable things to go right more often. If healthcare organisations want to broaden their approach towards managing safety, suitable methods must be investigated. The Functional Resonance Analysis Method (FRAM) was developed by Hollnagel in 2004 and has been applied in high-risk industries such as railway, aviation, maritime and healthcare. FRAM investigates the interaction of the different functions within a complex, underspecified system, and improves the understanding of normal work and its variability (Hollnagel, 2012). This systematic review will assess the application of FRAM in healthcare settings to develop a rich understanding of the application of FRAM in healthcare as a complementary method to safety management. Firstly, understanding how FRAM was implemented within healthcare organisations and secondly understanding how healthcare organisations have perceived the value-add of FRAM in terms of safety management. The results are expected to provide healthcare organisations with guidance on applying the FRAM and demonstrate the value it potentially adds to safety management. In the studies reviewed, FRAM was applied in a wide variety of settings and in different contexts. Thematic value-added aspects were identified and discussed. Shortcomings and prerequisites for the application of FRAM was also highlighted. This dissertation wishes to motivate healthcare organisations to investigate and apply alternative methods such as FRAM to enhance their ability to manage safety in a complex environment

Variabilidade de desempenho em sistemas intratáveis como metodologia de análise de risco no desenvolvimento de produtos médicos

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, Engenharia Eletrônica, 2021.É possível perceber que cada vez mais são desenvolvidos sistemas de alta complexidade, principalmente na esfera médica, que é uma área delicada por se tratar da saúde de indi- víduos. É interessante desenvolver uma metodologia que foca na identificação das falhas potenciais do sistema complexo considerando todos os aspectos de um sistema sociotécni- cos, que integram tecnologias e sociedade. Dessa forma, o estudo busca desenvolver uma metodologia de analise de risco no desenvolvimento de produtos médicos que foca em verificar se as saídas do sistema são aceitáveis para as condições determinadas, utilizando a variabilidade de desempenho de sistemas intratáveis e verificando quando ela é alta o suficiente a ponto de produzir resultados indesejados. São explicados os conceitos de Segurança I e II para introduzir a Engenharia de Resiliência, que fornece a ferramenta ne- cessárias para a descrição de aprimoramento de resposta de sistemas intratáveis: o Método de Análise de Ressonância Funcional (FRAM). Esse método tem como propósito a análise de atividades diárias e a verificação de como elas podem variar, definindo as funções de um sistema e seus acoplamentos de forma dinâmica. O método Análise de Modos de Falha e Efeito (FMEA) não consegue descrever tão bem sistemas complexos, porém ele é incluso na análise como fonte de variabilidade para mapear as condições de cenário das funções do sistema. Para quantificar e otimizar o método, é inserido uma abordagem probabilística chamada de simulação de Monte Carlo, visando destacar as funções e acoplamentos críti- cos levando em considerações a resposta do sistema aos cenários em que está inserido. Esse trabalho realizou um estudo de caso com a aplicação da metodologia desenvolvida em um protótipo de produto médico para monitorar e atenuar sintomas de pacientes acometidos com a doença de Parkinson. Foram levantados os requisitos para reunir as informações detalhadas sobre o protótipo, e com base neles foram identificados 34 acoplamentos no sistema. Com a escolha do cenário e a identificação das fontes de variabilidade, foram utilizadas 10.000 iterações para aplicação da simulação de Monte Carlo. Como resultado, os acoplamentos mais críticos foram identificados possibilitando a formação dos caminhos críticos. Foi observado que a simulação analisa apenas acoplamentos isolados ao invés de verificar o efeito dos acoplamentos conjuntos. Também foi percebido que algumas funções humanas que deveriam ter sido destacadas foram sobrepostas pela aplicação do FMEA focando na tecnologia. Porém na investigação final da metodologia, as funções humanas que deveriam ser destacadas e os acoplamentos conjunto foram identificadas e foram pro- postas formas de monitorar as variabilidades, e amortecer as necessárias, melhorando a segurança e eficácia do produto.It is possible to notice that more and more highly complex systems are being developed, es- pecially in the medical sphere, which is a delicate area because it deals with people health. It’s interesting to develop a methodology that focuses on identifying the potential failures of complex system considering all aspects of a sociotechnical system, which integrate tech- nologies and society. Thus, the study seeks to develop a risk analysis methodology in the development of medical products that focuses on verifying whether the system outputs are acceptable for the given conditions, using the performance variability of intractable systems and verifying when it’s high enough to the point of producing unwanted results. The concepts of Safety I and II are explained to introduce Resilience Engineering, which provides the necessary tool for describing the response improvement of intractable sys- tems: the Functional Resonance Analysis Method (FRAM). This method aims to analyze daily activities and verify how they can vary, defining the functions of a system and its couplings in a dynamic way. The Failure Mode and Effect Analysis (FMEA) method can- not describe complex systems, but it’s included in the analysis as a source of variability to map the scenario conditions of system functions. To quantify and optimize the method, a probabilistic approach called Monte Carlo simulation is introduced, aiming to highlight the critical functions and couplings taking into account the system’s response to the sce- narios in which it’s inserted. A case study was carried out using this method in a prototype of a medical product to monitor and alleviate symptoms of pacients with Parkinson’s Dis- ease. The requirements to gather detailed information about the prototype were raised, and based on them, 34 couplings in the system were identified. After choosing the sce- nario and identifying the sources of variability, 10.000 iterations were used to apply the Monte Carlo simulation. As a result, the most critical couplings were identified enabling the formation of critical paths. It was observed that the simulation analyzes only isolated couplings instead of verifying the effect of joint couplings. It was also noticed that some human functions that should have been highlighted were overridden by the application of FMEA focusing on technology. However, in the final investigation of the methodology, the human functions that should be highlighted and the joint couplings were identified and means were proposed to monitor the variability, and dampen the necessary ones, improving the safety and efficacy of the product