Hybrid reliability model for nuclear reactor safety system

Verlinden, Steven; Deconinck, Geert; Coupé, B.

doi:10.1016/j.ress.2012.01.004

Cited by 32 publications

(10 citation statements)

References 11 publications

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“…Eryilmaz and Bozbulut studied reliability of nonrepairable multi‐state weighted k ‐out‐of‐ n : G system based on an algorithmic approach. Verlinden et al built a hybrid reliability model based on simulations for a nuclear reactor safety system under different environments. In addition, stochastic failure models that attempt to capture the impact of a randomly evolving environment have been examined extensively in the reliability field .…”

Section: Introductionmentioning

confidence: 99%

Aggregated Markov‐based reliability analysis of multi‐state systems under combined dynamic environments

Jia

Xing

Song

2020

Quality & Reliability Eng

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Reliability of a system may differ greatly when operating under different environments. However, the existing works have either neglected the environment factor in system reliability analysis or considered this factor for binary systems or systems subject to a single environment (parameter). In this paper, we make contributions by modeling a multi‐state system operating under hybrid dynamic environments affected by multiple environmental parameters. Different Markov chains with finite states are used to represent the random system behavior and dynamic environments, leading to an aggregated Markov process that models the overall system behavior. An effective approach based on state partitions and aggregations is suggested for assessing the system reliability indexes, including reliability, availability, multi‐point availability, and environment‐based reliability. A high‐pressure homogenizer system is analyzed to demonstrate the proposed model and show the comparison of the reliability of system under fixed and dynamic environment.

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Section: Introductionmentioning

confidence: 99%

Aggregated Markov‐based reliability analysis of multi‐state systems under combined dynamic environments

Jia

Xing

Song

2020

Quality & Reliability Eng

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“…Summers [8] narrated viewpoints on ISA TR84.0.02. Those quantitative techniques include reliability block diagram (RBD) [9], Markov Analysis (MA) [6,10,11], Fault Tree Analysis (FTA) [6,12], failure mode and effects analysis (FMEA) [13], simplified equations [6], hybrid techniques [14], Monte Carlo Simulation [15], Petri nets etc.…”

Section: Introductionmentioning

confidence: 99%

“…Oliveira [21] introduced a generalized equation for PFD G calculations to any KooN architecture by using simplified equation technique and the results obtained by the generalized equation are close to those in IEC 61508-6. Verlinden [14] presented a hybrid model combined a system level RBD and a Markov model to describe the dynamics of a safety shutdown system. Rouvroye and Brombacher [22], Rouvroye and Bliek [23], and Bukowski [24] compared these techniques and indicated that Markov and Enhanced Markov analysis techniques Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/ress can cover most aspects of the system's safety-related behavior except the uncertainty and sensitivity analysis, but Markov analysis is more complicated unless one masters background knowledge of Markov analysis which can be obtained in many books including Ref.…”

Section: Introductionmentioning

confidence: 99%

A novel method for SIL verification based on system degradation using reliability block diagram

Ding

Wang

Kang

et al. 2014

Reliability Engineering & System Safety

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“…Second, models based on Markov chains have extensive application scope. Markov chain models can be used to study different processes; they have proved in many managerial fields to analyze various problems from financial issues (Wu and Chuang, 2010) through human processes (Guerry, 2011;) to reliability issues (Verlinden et al, 2012). Third, Markov chains and processes are widely used for process improvement.…”

Section: Introductionmentioning

confidence: 99%

Application of Markov Chains for Modeling and Managing Industrial Electronic Repair Processes

Jónás

Kalló

Tóth

2014

Period. Polytech. Soc. Man. Sci.

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This paper presents a research of Markov chain based modeling possibilities of electronic repair processes provided by electronics manufacturing service (EMS)companies IntroductionDuring the last decades, due to the changing economic environment the improvement of business processes to enhance organizational performance has become an important issue resulting in a wide range of management initiatives (TQM, Six Sigma, Lean, BPR, etc.). For this reason, the analysis of processes and the enhancement of process quality has become an important research area in the academic field and a leading managerial issue for the practitioners. Resulting that companies are collecting huge amounts of data, perform complex data and statistical analysis and use sophisticated models; they are competing on analytics (Davenport, 2006;de Vries, 1999).Processes of manufacturing and service companies are described and analyzed with a wide range of tools. The most frequently used techniques include graphical tools (charts and diagrams), matrices, graphical, object-oriented and workfloworiented techniques and generic methodologies as simulation (Aguilar-Savén, 2004). In this paper, Markov chains are applied to analyze business processes. The benefits of the application are demonstrated through a specific business process of industrial electronic repair services.Markov chain based modeling provides process managers with a fast modeling tool. Rapid modeling techniques ensure fast results both for planning and for what-if analysis (Suri and Tomsicek, 1998) which is crucial for companies working in the highly competitive markets of the electronic industry. The requirement of time-based competition and the highly variable processes of Electronic Repair Services (ERPs) (demanding frequent managerial decisions) necessitate quick information for the current operation of any related processes.The proposed Markov chain based modeling results in important information for process improvement and capacity planning. The steps of electronic repair processes are stochastic in respect of their sequence and their length. Consequently, the paths (the sequence of the visited process steps) are also variable for the different items delivered for repair. With the help of the proposed model, the likelihood and the lead time of each possible path and its contribution to the total load of the process can be calculated. This can support the management to identify the

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Hybrid reliability model for nuclear reactor safety system

Cited by 32 publications

References 11 publications

Aggregated Markov‐based reliability analysis of multi‐state systems under combined dynamic environments

Aggregated Markov‐based reliability analysis of multi‐state systems under combined dynamic environments

A novel method for SIL verification based on system degradation using reliability block diagram

Application of Markov Chains for Modeling and Managing Industrial Electronic Repair Processes

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