RBD Model-Based Approach for Reliability Assessment in Complex Systems

Catelani, Marcantonio; Ciani, Lorenzo; Venzi, Matteo

doi:10.1109/jsyst.2018.2840220

Cited by 29 publications

(8 citation statements)

References 18 publications

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“…An RBD decomposes a system into its independent components and shows the logical connections needed for the successful operation of the system [3][4][5]44,45]. The basic assumptions of the RBD methodology are the following ones:…”

Section: Reliability Block Diagramsmentioning

confidence: 99%

An Efficient Library for Reliability Block Diagram Evaluation

et al. 2021

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Reliability Block Diagrams (RBDs) are widely used in reliability engineering to model how the system reliability depends on the reliability of components or subsystems. In this paper, we present librbd, a C library providing a generic, efficient and open-source solution for time-dependent reliability evaluation of RBDs. The library has been developed as a part of a project for reliability evaluation of complex systems through a layered approach, combining different modeling formalisms and solution techniques at different system levels. The library achieves accuracy and efficiency comparable to, and mostly better than, those of other well-established tools, and it is well designed so that it can be easily used by other libraries and tools.

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Section: Reliability Block Diagramsmentioning

confidence: 99%

An Efficient Library for Reliability Block Diagram Evaluation

et al. 2021

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“…The dominance of the system reliability and availability can be done using sensitivity analysis. • RBD [80][81][82][83] : It is an inductive model used to describe success-oriented network in which a system is divided into blocks.…”

Section: Research Trendsmentioning

confidence: 99%

“…This method is capable to deal redundant, standby and parallel systems. The dominance of the system reliability and availability can be done using sensitivity analysis. RBD 80‐83 : It is an inductive model used to describe success‐oriented network in which a system is divided into blocks. The blocks are combined based on the success route of the system.…”

Section: Research Trendsmentioning

confidence: 99%

Reliability of safety‐critical systems: A state‐of‐the‐art review

Maurya

Kumar

2020

Quality & Reliability Eng

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Safety-critical systems are designed to prevent catastrophic consequences from failure, such as injury or death to humans and environmental damage. These must be carefully designed to ensure reliability requirements. The purpose of this paper is to identify the number of models in the reliability analysis of safety-critical systems. To achieve this goal, we conducted a systematic review of 40 shortlisted studies. The selected studies are classified based on various techniques used in safety-critical systems. This paper summarizes the literature review in the field of reliability of safety-critical systems. The limitations of the literature are exquisitely represented. The international safety norms and applications of safety-critical systems are discussed systematically in this paper. This paper emerges research trends, research challenges and insights of the researcher for future research direction in the area of safety-critical systems.

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“…Further, to provide a case study for validation purposes Industrial Gas Turbines (IGTs) are considered. IGTs are a true example of complex systems organized in many subsystems: compressor, combustors, pumps, fuel supply, ignition system, lubrication and drain modules, etc [21]. And as complex machines, IGTs can display a wide-ranging repertoire of anomalous behaviour (Sec.…”

Section: Introductionmentioning

confidence: 99%

Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

Martínez-García

Zhang

Suzuki

et al. 2021

IEEE Trans. Ind. Inf.

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The aim of this paper is to develop a methodology for measuring the degree of unpredictability in dynamical systems with memory, i.e., systems with responses dependent on a history of past states. The proposed model is generic, and can be employed in a variety of settings, although its applicability here is examined in the particular context of an industrial environment: gas turbine engines. The given approach consists in approximating the probability distribution of the outputs of a system with a deep recurrent neural network; such networks are capable of exploiting the memory in the system for enhanced forecasting capability. Once the probability distribution is retrieved, the entropy or missing information about the underlying process is computed, which is interpreted as the uncertainty with respect to the system's behaviour. Hence the model identifies how far the system dynamics are from its typical response, in order to evaluate the system reliability and to predict system faults and/or normal accidents. The validity of the model is verified with sensor data recorded from commissioning gas turbines, belonging to normal and faulty conditions.

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RBD Model-Based Approach for Reliability Assessment in Complex Systems

Cited by 29 publications

References 18 publications

An Efficient Library for Reliability Block Diagram Evaluation

An Efficient Library for Reliability Block Diagram Evaluation

Reliability of safety‐critical systems: A state‐of‐the‐art review

Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

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