Stochastic hybrid automaton model of a multi-state system with aging: Reliability assessment and design consequences

Chiacchio, Francesco; D’Urso, Diego; Manno, Gabriele; Compagno, Lucio

doi:10.1016/j.ress.2015.12.007

Cited by 33 publications

(28 citation statements)

References 27 publications

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“…Note that for the Hybrid Basic Events of the inverters, variable failure rates are updated during the simulation of an iteration in order to consider the variation of the aging variable. From lines (4)(5)(6)(7)(8)(9)(10)(11), the other variables required for the Monte Carlo simulation are initialized and line (12) sets the name of the Simulink model (which corresponds with the .slx file) that will be called at the beginning of the simulation (in our code it is named 'hybrid_pair_1'). With line (13) the variables initialized with the Matlab script are passed to the Simulink environment and line (14) starts the simulation.…”

Section: Simulation Of the Shyfta Modelmentioning

confidence: 99%

“…InitDP(); # initialize the parameters used in the Simulink deterministic block In particular, the script stops the Simulink simulation (line 2), updates the global variables and the estimator of the Matlab workspace (with the method UpdateGlobalVariables()) using the information generated in the Simulink environment (line 3) and verify if the accuracy required by the Monte Carlo setting is reached (line 4). If the variable 'completed' is not True (=1), lines (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) resets the simulation parameters to prepare for a next iteration. Before calling the built-in method of Simulink 'set_param' (line 17) to update the Simulink workspace for a new iteration, an important setting has to be performed (lines [8][9][10][11][12].…”

Section: Nexteventbe Scriptmentioning

confidence: 99%

“…The availability of a system is defined as the probability of a system to operate satisfactorily at a given point in time under stated operation conditions [9] and it can be computed for any type of industrial system comprised of different components through quantitative stochastic modelling methods The models typically adopted are shown in Table 2. They can be divided in three different groups: static, dynamic and hybrid-dynamic models [10,11]. Static or Boolean models are the simplest models as they can be solved directly with combinatorial logic.…”

Section: Introductionmentioning

confidence: 99%

“…This leads to more accurate reliability modelling that is able to account for environmental and operational changes of the working conditions. Moreover, recent works have shown its potential as a tool for the dimensioning of a system [11,23] and the understanding of other aspects of the life cycle of a system that characterizes the regime operations, like availability and maintenance. However, to the best of authors' knowledge, dynamic reliability has not been applied for the evaluation of a renewable power plant system.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model

et al. 2018

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Abstract:The contribution of renewable energies to the reduction of the impact of fossil fuels sources and especially energy supply in remote areas has occupied a role more and more important during last decades. The estimation of renewable power plants performances by means of deterministic models is usually limited by the innate variability of the energy resources. The accuracy of energy production forecasting results may be inadequate. An accurate feasibility analysis requires taking into account the randomness of the primary resource operations and the effect of component failures in the energy production process. This paper treats a novel approach to the estimation of energy production in a real photovoltaic power plant by means of dynamic reliability analysis based on Stochastic Hybrid Fault Tree Automaton (SHyFTA). The comparison between real data, deterministic model and SHyFTA model confirm how the latter better estimate energy production than deterministic model.

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Section: Simulation Of the Shyfta Modelmentioning

confidence: 99%

Section: Nexteventbe Scriptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model

et al. 2018

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“…There are a range of dynamic dependability models that address stochastic and temporal dependencies: BDMPs [20], Dynamic Fault Trees (DFT) [49], [50], Dynamic Bayesian Networks [51], [52], Dynamic Reliability Block Diagrams [53], StateEvent Fault Trees [54], Temporal Fault Trees [55], or hybrid DFT models [56] (see [3] for a more complete overview of dynamic dependability models).…”

Section: Relevant Workmentioning

confidence: 99%

Improved Dynamic Dependability Assessment Through Integration With Prognostics

et al. 2017

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This version is available at https://strathprints.strath.ac.uk/60422/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognosticsupdated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset interdependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations.

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Coherence region of the Priority‐AND gate: Analytical and numerical examples

Chiacchio

Aizpurua

D’Urso

et al. 2017

Quality & Reliability Eng

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In recent years, the need for a more accurate dependability modelling (encompassing reliability, availability, maintenance, and safety) has favoured the emergence of novel dynamic dependability techniques able to account for temporal and stochastic dependencies of a system. One of the most successful and widely used methods is Dynamic Fault Tree that, with the introduction of the dynamic gates, enables the analysis of dynamic failure logic systems such as fault-tolerant or reconfigurable systems. Among the dynamic gates, Priority-AND (PAND) is one of the most frequently used gates for the specification and analysis of event sequences. Despite the numerous modelling contributions addressing the resolution of the PAND gate, its failure logic and the consequences for the coherence behaviour of the system need to be examined to understand its effects for engineering decision-making scenarios including design optimization and sensitivity analysis. Accordingly, the aim of this short communication is to analyse the coherence region of the PAND gate so as to determine the coherence bounds and improve the efficacy of the dynamic dependability modelling process.

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Stochastic hybrid automaton model of a multi-state system with aging: Reliability assessment and design consequences

Cited by 33 publications

References 27 publications

Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model

Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model

Improved Dynamic Dependability Assessment Through Integration With Prognostics

Coherence region of the Priority‐AND gate: Analytical and numerical examples

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