Dynamic failure rate model of an electric motor comparing the Military Standard and Svenska Kullagerfabriken (SKF) methods

Abstract Electric motors are industrial systems' components widely diffused enabling all productive processes and safety equipment. They are affected by aging effect with a contribution based on the environmental condition on which they work. In order to design efficient maintenance plans, the behaviour of their main components, such as bearings and winding, has to be predicted. Therefore, a model-based methodology is applied aiming at codifying the failure rate of an electric engine, taking into account the thermal aging and relevant environment boundary conditions in which bearings and winding operate. The winding failure mode is coded by means of the Military standard technique while the bearings one is simulated comparing the Military Standard and the Svenska Kullagerfabriken (SKF) techniques. While the former predicts more conservative behaviours, the latter, taking into account lubrication conditions, dynamic loads and a better knowledge of materials quality, enables to capture the evolution of the operative conditions. The proposed reliability model can capture both the deterministic and stochastic behaviour of the electric motor: it belongs to the field of hybrid automaton application; the model is coded by means of the emerging software framework called SHYFTOO. The proposed model and the Monte Carlo simulation process that performs its evolution can support the development of a new class of electric motors: a cyber-physical oriented electric motor

A novel approach based on stochastic hybrid fault tree to compare alternative flare gas recovery systems

Flaring has always been an inseparable part of oil production and exploration. Previously, waste gas collected from different parts of facilities was released for safety or operational reasons and combusted on top of a flare stack since there was not the possibility to treat or use this type of gas. Concerns about global warming led to several initiatives for reducing flaring or even eliminating combustion. Treating flare gas was made possible by the introduction of flare gas recovery systems that have become increasingly obligatory. Most solutions add a flare gas recovery system to an existing flare system. In a typical scenario, after analyzing the existing facility and collecting the necessary data, alternative designs are proposed and criteria are determined to make a choice between the proposed alternatives. In this paper two designs of a gas control system are proposed, and reliability was chosen as the deciding factor. Using repairable dynamic fault trees, the failure models of the two designs have been implemented. Afterwards, a novel hybrid technique, the Stochastic Hybrid Fault Tree Automaton, is used to model the working conditions in which the system operates, with the aim to achieve a more realistic assessment and evaluate the disaster likelihood associated to these failures. It is shown that the latter enables a richer analysis where the effects of failure can be better assessed. This is important for correct choice between design alternatives because, as shown in the case study, the results of the two analyses can lead to contrasting conclusions of the solution to adopt. Further investigations have been carried out focusing on the safety sub-systems and on the basic events in each design. The Importance Measure analysis revealed that some of the components were responsible for most of the critical failures, thus locating some areas of possible design improvement

On the use of dynamic reliability for an accurate modelling of renewable power plants

Renewable energies are a key element of the modern sustainable development. They play a key role in contributing to the reduction of the impact of fossil sources and to the energy supply in remote areas where the electrical grid cannot be reached. Due to the intermittent nature of the primary renewable resource, the feasibility assessment, the performance evaluation and the lifecycle management of a renewable power plant are very complex activities. In order to achieve a more accurate system modelling, improve the productivity prediction and better plan the lifecycle management activities, the modelling of a renewable plant may consider not only the physical process of energy transformation, but also the stochastic variability of the primary resource and the degradation mechanisms that affect the aging of the plant components resulting, eventually, in the failure of the system. This paper presents a modelling approach which integrates both the deterministic and the stochastic nature of renewable power plants using a novel methodology inspired from reliability engineering: the Stochastic Hybrid Fault Tree Automaton. The main steps for the design of a renewable power plant are discussed and implemented to estimate the energy production of a real photovoltaic power plant by means of a Monte Carlo simulation process. The proposed approach, modelling the failure behavior of the system, helps also with the evaluation of other key performance indicators like the power plant and the service availability

Modelling and Resolution of Dynamic Reliability Problems by the Coupling of Simulink and the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO) Library

Dependability assessment is one of the most important activities for the analysis of complex systems. Classical analysis techniques of safety, risk, and dependability, like Fault Tree Analysis or Reliability Block Diagrams, are easy to implement, but they estimate inaccurate dependability results due to their simplified hypotheses that assume the components’ malfunctions to be independent from each other and from the system working conditions. Recent contributions within the umbrella of Dynamic Probabilistic Risk Assessment have shown the potential to improve the accuracy of classical dependability analysis methods. Among them, Stochastic Hybrid Fault Tree Automaton (SHyFTA) is a promising methodology because it can combine a Dynamic Fault Tree model with the physics-based deterministic model of a system process, and it can generate dependability metrics along with performance indicators of the physical variables. This paper presents the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO), a Matlab® software library for the modelling and the resolution of a SHyFTA model. One of the novel features discussed in this contribution is the ease of coupling with a Matlab® Simulink model that facilitates the design of complex system dynamics. To demonstrate the utilization of this software library and the augmented capability of generating further dependability indicators, three di erent case studies are discussed and solved with a thorough description for the implementation of the corresponding SHyFTA models

A Novel Approach Based on Stochastic Hybrid Fault Tree to Compare Alternative Flare Gas Recovery Systems

Flaring has always been an inseparable part of oil production and exploration. Previously, waste gas collected from different parts of facilities was released for safety or operational reasons and combusted on top of a flare stack since there was not the possibility to treat or use this type of gas. Concerns about global warming led to several initiatives for reducing flaring or even eliminating combustion. Treating flare gas was made possible by the introduction of flare gas recovery systems that have become increasingly obligatory. Most solutions add a flare gas recovery system to an existing flare system. In a typical scenario, after analyzing the existing facility and collecting the necessary data, alternative designs are proposed and criteria are determined to make a choice between the proposed alternatives. In this paper two designs of a gas control system are proposed, and reliability was chosen as the deciding factor. Using repairable dynamic fault trees, the failure models of the two designs have been implemented. Afterwards, a novel hybrid technique, the Stochastic Hybrid Fault Tree Automaton, is used to model the working conditions in which the system operates, with the aim to achieve a more realistic assessment and evaluate the disaster likelihood associated to these failures. It is shown that the latter enables a richer analysis where the effects of failure can be better assessed. This is important for correct choice between design alternatives because, as shown in the case study, the results of the two analyses can lead to contrasting conclusions of the solution to adopt. Further investigations have been carried out focusing on the safety sub-systems and on the basic events in each design. The Importance Measure analysis revealed that some of the components were responsible for most of the critical failures, thus locating some areas of possible design improvement

An overview of fault tree analysis and its application in model based dependability analysis

YesFault Tree Analysis (FTA) is a well-established and well-understood technique, widely used for dependability evaluation of a wide range of systems. Although many extensions of fault trees have been proposed, they suffer from a variety of shortcomings. In particular, even where software tool support exists, these analyses require a lot of manual effort. Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based dependability analysis (MBDA). Different tools and techniques have been developed as part of MBDA to automate the generation of dependability analysis artefacts such as fault trees. Firstly, this paper reviews the standard fault tree with its limitations. Secondly, different extensions of standard fault trees are reviewed. Thirdly, this paper reviews a number of prominent MBDA techniques where fault trees are used as a means for system dependability analysis and provides an insight into their working mechanism, applicability, strengths and challenges. Finally, the future outlook for MBDA is outlined, which includes the prospect of developing expert and intelligent systems for dependability analysis of complex open systems under the conditions of uncertainty

Fuel Cell Renewable Hybrid Power Systems

Climate change is becoming visible today, and so this book—through including innovative solutions and experimental research as well as state-of-the-art studies in challenging areas related to sustainable energy development based on hybrid energy systems that combine renewable energy systems with fuel cells—represents a useful resource for researchers in these fields. In this context, hydrogen fuel cell technology is one of the alternative solutions for the development of future clean energy systems. As this book presents the latest solutions, readers working in research areas related to the above are invited to read it

Quantitative risk assessment using Monte Carlo and dynamic process simulation

Currently, the concern about the industrial risk is a key issue to implement any technology process or to improve the industry competitiveness. In this sense, the risk concept may be considered as the main tool to anticipate behaviors that can lead to further problems. Considering the process industry, different risk analysis techniques are employed to identify hazardous events, to estimate their frequencies and severities, and to characterize the risk, being such tools the best ones to improve the industrial safety. Knowing that, the present Thesis discusses these risk topics to propose four main contributions: (i) new procedure to identify hazardous events; (ii) new procedures to quantify frequency; (iii) new risk definition and representation; and (iv) a method to integrate the proposed procedures to manage a complete risk assessment management. The idea behind the contributions is to use computational tools in new techniques with improved results about the operational risk, helping its obtainment and understanding. Thus, based on a new risk definition that allow better relation between the developed analysis, process simulations are employed to identify hazardous events and Monte Carlo simulations are employed to estimate frequency and to generate a new risk representation characterized by a severity x time x frequency surface. Despite all contributions has its particularity and importance for the risk analyses development, as final contribution, the presented Thesis apply all developed techniques in a case study, proposing an innovative risk assessment procedure.Atualmente, a preocupação com o risco industrial é um ponto chave para implantação de uma nova tecnologia ou para um melhor posicionamento competitivo. Neste sentido, a ideia de risco pode ser considerada como o principal recurso para antever situações que podem gerar problemas futuros. Considerando a indústria de processos, diferentes técnicas de análise de riscos são utilizadas para identificar eventos perigosos, estimar suas frequências e severidades e caracterizar o risco, sendo essas as principais ferramentas para o aumento da segurança industrial. Sabendo disso, a presente tese aborda tais tópicos e propõe quatro contribuições principais: (i) novo procedimento para identificação de eventos perigosos; (ii) novos procedimentos para quantificação de frequência; (iii) nova definição e representação de risco e (iv) um método para integrar os procedimentos propostos em uma avaliação quantitativa de risco completa. A ideia por trás destas contribuições é utilizar procedimentos computacionais que geram resultados mais acurados sobre o risco de uma operação, ajudando em seu entendimento e na obtenção de seu valor. Assim, baseado em um novo conceito de risco que melhor relaciona as análises desenvolvidas, simulações de processos são utilizadas para identificação de eventos perigosos e simulações de Monte Carlo são utilizadas para estimativa de frequência e gerar uma nova representação de risco caracterizada por uma superfície com eixos frequência x severidade x tempo. Apesar de cada contribuição ter sua particularidade e importância para o desenvolvimento das técnicas de análise de risco, como contribuição final, a presente tese aplica todas as técnicas desenvolvidas em um estudo de caso, apresentando assim uma avaliação de risco inovadora