Dynamic assessment of multi-state systems using phase-type modeling

Eryılmaz, Serkan

doi:10.1016/j.ress.2015.03.037

Cited by 22 publications

(15 citation statements)

References 19 publications

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“…Zhang (2010aZhang ( ,2010b have investigated some ordering properties of the residual lives and the inactivity times of coherent systems. Eryilmaz (2015) demonstrated, among other things, the utility of phase type modeling in the computation of MRL of some multi-state systems. Tavangar and Bairamov (2012) studied the residual lifetimes of coherent systems in the situation where there exists a stochastic dependence among the components of the system.…”

Section: Introductionmentioning

confidence: 98%

On conditional residual lifetime and conditional inactivity time of k-out-of-n systems

Tavangar

Bairamov

2015

Reliability Engineering & System Safety

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Section: Introductionmentioning

confidence: 98%

On conditional residual lifetime and conditional inactivity time of k-out-of-n systems

Tavangar

Bairamov

2015

Reliability Engineering & System Safety

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“…The assumptions the assumptions of above approaches may not conform to most real-life systems. 11 Dynamic models: Markov model 12,13 ; dynamic fault tree [14][15][16] ; Petri net 17,18 ; Bayesian network 6,19,20 ; multivalued decision diagram 21,22 ; universal generating functions [23][24][25] ; and stochastic process approach 26,27 The problem of combinatorial explosion is intractable. Some of them have been criticized for the high computational complexity.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Reliability assessment of complex electromechanical systems: A network perspective

Lin

Wang

Jia

et al. 2018

Quality & Reliability Eng

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The network theory is widely applied to improve the reliability of a complex electromechanical system. In this application, system reliability assessment with network theory has been paid a great deal of attention. Because of instrument malfunctions, staff omissions, imperfect inspection strategies, and complex structures, field failure data are often subject to interval censoring, making the holistic reliability assessment becomes a difficult task. Most traditional methods assume reliability of critical components or partial reliability as system reliability, which may cause a large bias in system reliability estimation. This paper proposes a novel method to evaluate and predict the system reliability of a complex electromechanical system subject to the insufficient fault data problem from a network perspective. First, the system modeling based on network theory is developed to describe the topology of a holistic system. Second, interval-valued intuitionistic hesitant fuzzy number is proposed in order to solve insufficient data for single component. Then, a new measurecomprehensive reliability-that can reflect the reliability of nodes in combination with functional properties and topological properties, which are formulated by failure data and network model, respectively, is constructed for system reliability assessment. Subsequently, an improved system reliability model based on percolation theory is given in terms of comprehensive reliability of nodes. Finally, to verify the effectiveness of the proposed method, a simulation and a real case study for traction system are implemented.

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“…Li et al built a safe and reliable application system and adopted security methods such as all‐sided, multi‐tier, and multi‐technical to ensure that there is no danger. Considerable research has also been performed on multistate reliability models for SCSs, such as Abou, Levitin et al, Liu et al, Moghaddass and Zuo, Eryilmaz, Peng et al, Mo et al, Faghih‐Roohi et al, and Naderpour et al…”

Section: Introductionmentioning

confidence: 99%

Copula‐based reliability and safety analysis of safety‐critical systems with dependent failures

Jia

Xing

2018

Quality & Reliability Eng

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A safety-critical system (SCS) is a system whose failure could result in a certain serious consequence, such as loss of life and significant damage to property or environment. Examples of SCSs abound in real-world applications, such as medical instruments, emergency shutdown systems, and fire/gas detection systems. An SCS can assume 1 of 3 states: working, safe failure, and dangerous (or unsafe) failure. To analyze reliability and safety of SCSs accurately, we build multi-state models of an SCS and its constituent units. The dependent relationships (nonlinear correlation) of different parts within a safety-critical unit as well as across the units are modeled using the Copula method. Formulas computing reliability and safety indexes of a safety-critical unit and of safety-critical series or parallel systems are derived. Numerical examples are provided to demonstrate the application of the proposed methodology.

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Dynamic assessment of multi-state systems using phase-type modeling

Cited by 22 publications

References 19 publications

On conditional residual lifetime and conditional inactivity time of k-out-of-n systems

On conditional residual lifetime and conditional inactivity time of k-out-of-n systems

Reliability assessment of complex electromechanical systems: A network perspective

Copula‐based reliability and safety analysis of safety‐critical systems with dependent failures

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