Multi-state Markov Model for Reliability Analysis of a Combined Cycle Gas Turbine Power Plant

Lisnianski, Anatoly; Laredo, David; Haim, Hanoch Ben

doi:10.1109/smrlo.2016.31

Cited by 14 publications

(6 citation statements)

References 5 publications

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“…Every component C i is affected by D i independent degradation processes, which are individually modeled as multi-state Markov processes, 36,37 with S d i states each, d = 1,…, D i .…”

Section: Problem Statementmentioning

confidence: 99%

A Markov decision process framework for optimal operation of monitored multi-state systems

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Marelli

Baraldi

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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We develop a decision support framework based on Markov decision processes to maximize the profit from the operation of a multi-state system. This framework enables a comprehensive management of the multi-state system, which considers the maintenance decisions together with those on the multi-state system operation setting, that is, its loading condition and configuration. The decisions are informed by a condition monitoring system, which estimates the health state of the multi-state system components. The approach is shown with reference to a mechanical system made up of components affected by fatigue.

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“…Every component C i is affected by D i independent degradation processes, which are individually modeled as multi-state Markov processes, 36,37 with S d i states each, d = 1,…, D i .…”

Section: Problem Statementmentioning

confidence: 99%

A Markov decision process framework for optimal operation of monitored multi-state systems

Compare

Marelli

Baraldi

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…Instead of traditional reliability evaluation, the fuzzy reliability approach is also considered by the researcher for power plant reliability analysis 22 . Reliability investigation of combined cycle gas turbine has been done by developing a Markov model 23 . Numerical analysis for the reliability of HRSG unit during start‐up phase has been done and validated through experimental study 24 .…”

Section: Introductionmentioning

confidence: 99%

“…22 Reliability investigation of combined cycle gas turbine has been done by developing a Markov model. 23 Numerical analysis for the reliability of HRSG unit during start-up phase has been done and validated through experimental study. 24 A performance optimization study has been carried out for combined gas-steam power plants by considering various objective functions.…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling and availability optimization of condenser used in steam turbine power plants using GA and PSO

Saini

Gupta

Shankar

et al. 2022

Quality & Reliability Eng

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The prominent objective of the present study is to optimize the availability of condenser units in steam turbine power plants. For this purpose, a novel stochastic model is proposed by considering a condenser as a system consisting of seven subsystems. All the time‐dependent random variables associated with failure rates followed exponential distribution while repair rates are arbitrarily distributed. Furthermore, to attain the optimum value of system availability nature‐inspired algorithm has been applied. Predicting the future phenomena is very difficult and most of the existing algorithms have some limitations like stuck at local minima, and slow convergence rate, and so forth. So, in this study proposed model has been optimized using a genetic algorithm (GA) and particle swarm optimization (PSO) The expressions for steady‐state availability of the system have been assessed using Chapman‐Kolmogorov differential‐difference equations developed by applying the Markov birth‐death process. The supplementary variable technique has been adopted to solve differential‐difference equations For a comparative analysis, the steady‐state values, GA, and PSO results are compared. It is revealed that the GA gives the maximum value of availability as 0.9886 corresponding to crossover probability 0.65, population size 50, evaluation 300, and mutation probability 0.5. While using PSO optimum availability value is 0.998152 at 27 iterations, population 55, inertia weight 1, damping ratio 0.85, personal best 1.5, and global best 2. So, PSO results outperform the steady‐state and GA results. It is revealed that mixed bed filter and ejector are the most sensitive components of the condenser those influences most the system effectiveness.

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“…Lisnianski, A.; Laredo, D.; and Haim, H.B. [19] performed a reliability analysis of a combined cycle gas turbine power plant by the means of a multi-state Markov model. Sensitivity analysis for reliability data uncertainty was also provided.…”

Section: Introductionmentioning

confidence: 99%

“…1−B(x) -conditional PDF relative to the time spent on completion of the repair [19]. λ i -parameter of the exponential distribution of the uptime of components, i = 1, 2.…”

Section: Introductionmentioning

confidence: 99%

Mathematical and Simulation Model for Reliability Analysis of a Heterogeneous Redundant Data Transmission System

Houankpo

Kozyrev

2021

Mathematics

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In the actual study, we carried out a reliability analysis of a repairable redundant data transmission system with the use of the elaborated mathematical and simulation model of a closed heterogeneous cold standby system. The system consists of one repair unit and two different data sources with an exponential cumulative distribution function (CDF) of their uptime and a general independent CDF of their repair time. We consider five special cases of the general independent CDF; including Gamma, Weibull-Gnedenko, Exponential, Lognormal and Pareto. We study the system-level reliability, defined as the steady-state probability (SSP) of failure-free system operation. The proposed analytical methodology made it possible to assess the reliability of the whole system in the event of failure of its components. Specific analytic expressions and asymptotic valuations are obtained for the steady-state probabilities of the system and the SSP of failure-free system operation. A simulation model of the system in cases where it is not workable to obtain expressions for the steady-state probabilities of the system in an explicit analytical form was considered, in particular for constructing the empirical system reliability function. The issue of sensitivity analysis of reliability characteristics of the considered system to the types of repair time distributions was also studied. The simulation modeling was done with the R statistics package.

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Multi-state Markov Model for Reliability Analysis of a Combined Cycle Gas Turbine Power Plant

Cited by 14 publications

References 5 publications

A Markov decision process framework for optimal operation of monitored multi-state systems

A Markov decision process framework for optimal operation of monitored multi-state systems

Stochastic modeling and availability optimization of condenser used in steam turbine power plants using GA and PSO

Mathematical and Simulation Model for Reliability Analysis of a Heterogeneous Redundant Data Transmission System

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