A Case Study of Aircraft Maintenance Repairs

and also by the Polytechnic Institute of Lisbon through the Projects for Research, Development, Innovation and Artistic Creation (IDI&CA), within the framework of the project ReEdIA—Risk Assessment and Management in Open Innovation, IPL/2021/ReEdIA/ISEL. Publisher Copyright: © 2022 by the authors.This paper proposes a qualitative model to overcome the limitations of conventional failure mode and effects analysis (FMEA), which does not consider risk mitigation capabilities when prioritizing risks. Failure to consider these capabilities can lead to unrealistic risk estimates, especially when the level of uncertainty is high. In the proposed model, the original applicability of conventional FMEA was retained along with the three conventional risk variables, namely, severity, occurrence, and detectability. In addition, a fourth variable was added to account for risk mitigation capabilities. A case study in the project selection of aircraft repairs was used to demonstrate the implementation of the model and its applicability. The results show that the inclusion of mitigation options leads to more realistic risk scenarios, suggesting that the original FMEA approach may lead to non-conservative risk estimates.publishersversionpublishe

Melhoria da priorização dos modos de falha no FMEA

Dissertação de natureza científica para obtenção do grau de Mestre em Engenharia MecânicaAtualmente, a indústria depende muito da manutenção e por isso o cuidado que têm em conseguir fazer uma melhor gestão e controlo dos seus ativos é de extrema importância. Para conseguir minimizar a frequência da manutenção nos equipamentos, sistemas ou mesmo em serviços, as empresas acabam por investir na fase inicial do projeto antes mesmo do produto existir, o que possibilita a otimização dos seus produtos tornando conseguindo obter uma maior fiabilidade. Na análise inicial do projeto quanto na fase em que um processo ou equipamento já esteja em funcionamento, é possível fazer uma análise de modo a conseguir ter um controlo maior da situação o que permite ter um maior controlo da manutenção e por isso um controlo do seu orçamento. Para fazer a análise, são necessárias técnicas como é o caso da Análise de Modos de Falha e Efeitos (FMEA) algo que já é amplamente usado na indústria, já que é uma ferramenta que ajuda a descobrir modos de falha e a conseguir priorizá-los. Este método, apesar de tudo, apresenta inúmeras limitações, desde o facto de que dentro de uma mesma equipa haver diferente opiniões, até na própria fórmula do Risk Priority Number , o que pode causar uma análise que não seja a mais correta. Neste trabalho, é proposto fazer uma melhoria do Risk Priority Number, criando um método que consiga ser capaz de superar as limitações que este apresenta, e para isso usar diferentes ferramentas como é o caso do fuzzy logicCurrently, the industry depends heavily on maintenance and therefore the care we take in being able to better manage and control its assets is extremely important. In order to minimize the frequency of maintenance on equipment, systems or even services, companies end up investing in the initial phase of the project before the product even exists, which makes it possible to optimize their products by achieving greater reliability. In the initial analysis of the project, or in the phase in which a process or piece of equipment is already in operation, it is possible to carry out an analysis in order to have greater control of the situation, which allows for greater control of maintenance and therefore control of its budget. In order to carry out the analysis, techniques are needed, such as Failure Modes and Effects Analysis (FMEA), something that is already widely used in industry, as it is a tool that helps to discover failure modes and prioritize them. However, this method has numerous limitations, from the fact that there are different opinions within the same team, to the Risk Priority Number formula itself, which can lead to an analysis that is not the most correct. This paper proposes an improvement to the Risk Priority Number, creating a method that is able to overcome its limitations, using different tools such as fuzzy logic.N/

A Methodological Framework for the Assessment of Level of Service of Stormwater Infrastructure

With climate change becoming the single most concern in the contemporary world, stormwater management has been receiving greater attention than before. Even though stormwater infrastructure plays a pivotal part in the municipality, there are no available standards or tools to assess its level of service (LOS). Stormwater infrastructure should manage the quantity and quality of stormwater by controlling flooding, pollutants and ensuring public safety. Currently, creating a proactive stormwater infrastructure management system is hindered by a lack of resources despite the importance of these resources for proper stormwater infrastructure management. In an advanced asset management system, LOS assessment is identified as a key component. LOS indicates the service provided by the stormwater infrastructure system to its stakeholders. The LOS assessment system must use a flexible framework that enables focusing on the component level up to the system level and integrates stakeholder and regional priorities. The objective of this research is to propose a methodological framework to assess the LOS of stormwater infrastructure. A Key Performance Indicator (KPI) based approach is used to indicate the current LOS. The KPIs identified under four performance parameters include stormwater quantity, quality, risk, and cost. Benchmarks are defined for each KPI based on industry standards, regional characteristics and/or stakeholder priorities. The proposed method develops the LOS assessment tool to support stakeholders in their asset management process. The KPI data for the study area, Ward 6, Windsor, Canada was obtained from municipal Geographic Information Systems (GIS) data and the Stormwater Management Model (SWMM). The programming language, R was used to read and write the SWMM files to aid in analysis. The LOS performance assessment is indicated at the stormwater system level, performance parameter level, and the KPI level. The proposed method is applied to the study area as a case study. The outcome identifies flooding prone areas, determines the level of pollutant concentrations and loadings in stormwater runoff and the cost vs benefits of low impact development (LID) in the study area. The LOS framework and tool gives an insight into the current LOS of the study area

Advances in Fluid Power Systems

The main purpose of this Special Issue of “Advances in Fluid Power Systems” was to present new scientific work in the field of fluid power systems for hydraulic and pneumatic control of machines and devices used in various industries. Advances in fluid power systems are leading to the creation of new smart devices that can replace tried-and-true solutions from the past. The development work of authors from various research centres has been published. This Special Issue focuses on recent advances and smart solutions for fluid power systems in a wide range of topics, including: • Fluid power for IoT and Industry 4.0: smart fluid power technology, wireless 5G connectivity in fluid power, smart components, and sensors.• Fluid power in the renewable energy sector: hydraulic drivetrains for wind power and for wave and marine current power, and hydraulic systems for solar power. • Hybrid fluid power: hybrid transmissions, energy recovery and accumulation, and energy efficiency of hybrid drives.• Industrial and mobile fluid power: industrial fluid power solutions, mobile fluid power solutions, eand nergy efficiency solutions for fluid power systems.• Environmental aspects of fluid power: hydraulic water control technology, noise and vibration of fluid power components, safety, reliability, fault analysis, and diagnosis of fluid power systems.• Fluid power and mechatronic systems: servo-drive control systems, fluid power drives in manipulators and robots, and fluid power in autonomous solutions

A new risk prioritization model for failure mode and effects analysis

Failure modes and effects analysis is a framework that has been widely used to improve reliability by prioritizing failures modes using the so‐called risk priority number. However, the risk priority number has some problems frequently pointed out in literature, namely its non‐injectivity, non‐surjectivity, and the impossibility to give weights to risk variables. Despite these disadvantages, the risk priority number continues to be widely used due to its higher simplicity when compared with other alternatives found in literature. In this paper, we propose a novel risk prioritization model to overcome the major drawbacks of the risk priority number. The model contains 2 functions, the risk isosurface function that prioritizes 3 risk variables considering their order of importance in a given risk scenario, and the risk prioritization index function which prioritizes 3 risk variables considering their weights. The novelty of the proposed model is its injectivity, surjectivity, and ease of use in failure modes prioritization. The performance of the proposed model was analyzed using some examples typically used to discuss the conventional risk priority number shortcomings. The model was applied to a case study and its performance correlated with other risk prioritization models. Results show that the failure modes prioritization reached with the proposed model agrees with the expectations made for the risk scenario.info:eu-repo/semantics/publishedVersio