The DYLAM Approach for the Reliability Analysis of Dynamic Systems

Cojazzi, G.; Cacciabue, Pietro Carlo

doi:10.1007/978-3-662-03041-7_2

Cited by 9 publications

(14 citation statements)

References 10 publications

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“…In this context, DET is used as the logical modeling technique to derive, by means of simulation, the scenarios that can arise in the life evolution of a dynamic system as the result of a sequence of successes and failures of different components and functions. (8,29) Much software for DET analyses, such as DYLAM, (10) ADS, (11) and MCDET, (14) is available and, in principle, all the possible (accident) scenarios could be extracted, especially by recurring to massive parallel computing (30) or to backtracking techniques. (8) However, not all the possible different time sequences within a given scenario can be explored, by reason of the extremely high computational cost needed for simulating all of them.…”

Section: Problem Definitionmentioning

confidence: 99%

“…In this line of thought, works on dynamic event trees (DETs) have highlighted that the end states reached by a system as a result of an accident scenario do not depend only on the order of occurrence of the events in the sequence, but also on the exact time at which these events occur and on their magnitude . However, the introduction of the time and magnitude dimensions into the analysis makes the size of the system state‐space theoretically infinite and, thus, impossible to be explored completely.…”

Section: Introductionmentioning

confidence: 99%

“…(4)(5)(6) In this context, the combination of event trees (ETs) (representing the logic of the system) and mathematical models of the system dynamics (describing the dynamics of the physical phenomena involved) has been largely advocated as the way for determining the end states (ESs) that can be reached by the system and deriving the corresponding causality relations among the events. (7)(8)(9) In this line of thought, works on dynamic event trees (DETs) (10)(11)(12)(13)(14)(15) have highlighted that the end states reached by a system as a result of an accident scenario do not depend only on the order of occurrence of the events in the sequence, but also on the exact time at which these events occur and on their magnitude. (8,9,(16)(17)(18) However, the introduction of the time and magnitude dimensions into the analysis makes the size of the system state-space theoretically infinite and, thus, impossible to be explored completely.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

2016

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The end states reached by an engineered system during an accident scenario depend not only on the sequences of the events composing the scenario, but also on their timing and magnitudes. Including these additional features within an overarching framework can render the analysis infeasible in practical cases, due to the high dimension of the system state-space and the computational effort correspondingly needed to explore the possible system evolutions in search of the interesting (and very rare) ones of failure. To tackle this hurdle, in this article we introduce a framework for efficiently probing the space of event sequences of a dynamic system by means of a guided Monte Carlo simulation. Such framework is semi-automatic and allows embedding the analyst's prior knowledge about the system and his/her objectives of analysis. Specifically, the framework allows adaptively and intelligently allocating the simulation efforts preferably on those sequences leading to outcomes of interest for the objectives of the analysis, e.g., typically those that are more safety-critical (and/or rare). The emerging diversification in the filling of the state-space by the preference-guided exploration allows also the retrieval of critical system features, which can be useful to analysts and designers for taking appropriate means of prevention and mitigation of dangerous and/or unexpected consequences. A dynamic system for gas transmission is considered as a case study to demonstrate the application of the method.

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Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

2016

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“…It is necessary to mention that the term scheduler has been used in other simulation-based probabilistic risk assessment frameworks, such as DYLAM (Cojazzi, 1996) and ADS (Hsueh and Mosleh, 1996) as well. However, the role of the scheduler is quite different in our framework.…”

Section: Schedulermentioning

confidence: 99%

Hierarchical planning and multi-level scheduling for simulation-based probabilistic risk assessment

Nejad¹,

Zhu²,

Mosleh³

2007

2007 Winter Simulation Conference

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Simulation of dynamic complex systems-specifically, those comprised of large numbers of components with stochastic behaviors-for the purpose of probabilistic risk assessment faces challenges in every aspect of the problem. Scenario generation confronts many impediments, one being the problem of handling the large number of scenarios without compromising completeness. Probability estimation and consequence determination processes must also be performed under real world constraints on time and resources. In the approach outlined in this paper, hierarchical planning is utilized to generate a relatively small but complete group of risk scenarios to represent the unsafe behaviors of the system. Multi-level scheduling makes the probability estimation and consequence determination processes more efficient and affordable. The scenario generation and scheduling processes both benefit from an updating process that takes place after a number of simulation runs by fine-tuning the scheduler's level adjustment parameters and refining the planner's high level system model.

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“…In [22], the authors have used the same system to present continuous cell-to-cell mapping Markovian approach (CCCMT) still to simulate the process. The simulation of the holdup tank example has been and is still widely studied in the literature (not exhaustive) [21,3,9,20,12,6]. Here we go one step further and not only propose to simulate the tank process but also we optimize it.…”

Section: Introductionmentioning

confidence: 99%

Predictive maintenance for the heated hold-up tank

Saporta

2013

Reliability Engineering & System Safety

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We present a numerical method to compute an optimal maintenance date for the test case of the heated hold-up tank. The system consists of a tank containing a fluid whose level is controlled by three components: two inlet pumps and one outlet valve. A thermal power source heats up the fluid. The failure rates of the components depends on the temperature, the position of the three components monitors the liquid level in the tank and the liquid level determines the temperature. Therefore, this system can be modeled by a hybrid process where the discrete (components) and continuous (level, temperature) parts interact in a closed loop. We model the system by a piecewise deterministic Markov process, propose and implement a numerical method to compute the optimal maintenance date to repair the components before the total failure of the system.

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The DYLAM Approach for the Reliability Analysis of Dynamic Systems

Cited by 9 publications

References 10 publications

An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

Hierarchical planning and multi-level scheduling for simulation-based probabilistic risk assessment

Predictive maintenance for the heated hold-up tank

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