Fuzzy reliability analysis using cellular automata for network systems

He, Li; Zhang, Xiaodong

doi:10.1016/j.ins.2016.01.102

Cited by 19 publications

(13 citation statements)

References 50 publications

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“…For large-scale network system, decomposing algorithm makes it simpler subsystems from scratch. In this paper, we use DB-CA algorithm based on cellular automaton (CA) to decompose network in [2]. In network G, let and be neighborhood of node , with each node mapping to a cell whose neighborhood is represented by two sets of nodes connected to it by its input arcs and output arcs, respectively.…”

Section: Network Topology Decomposing Model Based On Cellular Automatamentioning

confidence: 99%

“…During the past ten years, a significant amount of research has been conducted to address reliability evaluation. Network reliability can be estimated using Bayesian approach [1], Monte Carlo simulation [2,3], genetic algorithm [4], fault-tree analysis [5], etc. Obviously, all those methods apply numerical reliability or boundary value to indicate the reliability of network systems.…”

Section: Introductionmentioning

confidence: 99%

“…The elements appearing in set ( ) can be expressed as *1 * 2 ⋅ ⋅ ⋅ * , * ∈ {1, , }, = 1, 2, ⋅ ⋅ ⋅ , . Let * 1 = 1 * = * .Theorem 2 (see[2]). For any , ∈ , there exists item , such that both and contain the same items, and the items in and are complementary; then and are disjoint.…”

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confidence: 99%

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Measuring Component Importance for Network System Using Cellular Automata

Cao

Shang

2019

Complexity

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This paper concentrates on the component importance measure of a network whose arc failure rates are not deterministic and imprecise ones. Conventionally, a computing method of component importance and a measure method of reliability stability are proposed. Three metrics are analyzed first: Birnbaum measurement, component importance, and component risk growth factor. Based on them, the latter can measure the impact of the component importance on the reliability stability of a system. Examples in some typical structures illustrate how to calculate component importance and reliability stability, including uncertain random series, parallel, parallel-series, series-parallel, and bridge systems. The comprehensive numerical experiments demonstrate that both of these methods can efficiently and accurately evaluate the impact of an arc failure on the reliability of a network system.

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Section: Network Topology Decomposing Model Based On Cellular Automatamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring Component Importance for Network System Using Cellular Automata

Cao

Shang

2019

Complexity

Self Cite

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“…Therefore, uncertainty processing is necessary for factors with incomplete information and ambiguity. Zadeh et al, He and Zhang, Abbasi-Ghalehtaki et al, Tencer et al, Rajak et al, and Jafelice et al proposed the concept of fuzzy set in 1965, which provided a method to solve fuzzy problems [33][34][35][36][37][38]. Since then, many scholars have extensively studied the combination of fuzzy sets and GIS (geographic information system), which provides a quantitative description method for handling complex systems [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Yang

Weiling

et al. 2018

Complexity

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The spatial evolution of land use in Jinzhou area was simulated using fuzzy cellular automata to determine all factors influencing urban land use change. For this purpose, land use data of multiple sources and remote sensing images from 2003 to 2015 were analyzed. The following results were obtained: (1) real land use data and simulation data for 2015 were tested using four indices. Under the 5 m × 5 m scale, the model shows good performance for simulation of areas with various land use types. (2) Among simulations under four scenarios, the effect of traffic guidance on the development of construction land was more distinct under the economic development mode, which clearly showed the phenomenon of “agglomeration” along major traffic lines. (3) Jinshitan Street is adjacent to the sea, and land use changes are significant under the 4th scenario, and thus related departments should pay more attention. (4) Shortcomings of conventional cellular automata model in processing complex systems could be mitigated through the integration of fuzzy sets.

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“…Typical algorithms to compute correspondingly terminal reliability are minimal cuts method, 34 state enumeration method, 35 sum of disjoint products method, 36 factorization method, 37 and cellular automata. 38 In fact, the nodes must be analyzed differently from other nodes that constitute networks, since these nodes represent components of the EMS and have their own individually topological attributes. Terminal reliability does not consider functional properties of components, which are quite significant in sustaining normal operations of networks.…”

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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Fuzzy reliability analysis using cellular automata for network systems

Cited by 19 publications

References 50 publications

Measuring Component Importance for Network System Using Cellular Automata

Measuring Component Importance for Network System Using Cellular Automata

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Reliability assessment of complex electromechanical systems: A network perspective

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