Use of Markov Processes for Reliability Problems

Blin, Aurore; Carnino, A.; Georgin, J.P.; Signoret, J.P.

doi:10.1007/978-1-4613-3036-3_13

Cited by 5 publications

(3 citation statements)

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“…This slightly more complicated model from Blin et al [26] has four states. The system comprises two pumps, each of which is either in the working or in the failed state.…”

Section: Discussionmentioning

confidence: 99%

Salient Features of a Lecture Course on Reliability Theory

Csenki

2008

International Journal of Mechanical Engineering Education

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The module ‘oReliability Modelling and Analysis’ has been taught by the author at the University of Bradford in the School of Informatics over several years. In this paper, four aspects of the lecture course are described. After reviewing the general course structure, we examine within the field of maintenance modelling the method of marginal cost analysis. The application of this technique is deemed to be novel in the present (teaching) context and it is intended to make a basically mathematical subject amenable also to engineers and economists. Next, we outline the use of the computer algebra package Maxima in teaching ‘reliability’. This is exemplified with a classroom example, an exercise and a coursework assignment. Experience shows that the use of Maxima enriches the teaching of reliability to aspiring practitioners. Finally, some general principles are reviewed which help the learner to keep an eye on the reasonableness of purported results, using properties such as model structure, model symmetry, dimensional correctness, boundary conditions, specialization and generalization.

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“…This slightly more complicated model from Blin et al [26] has four states. The system comprises two pumps, each of which is either in the working or in the failed state.…”

Section: Discussionmentioning

confidence: 99%

Salient Features of a Lecture Course on Reliability Theory

Csenki

2008

International Journal of Mechanical Engineering Education

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“…We use this paper to present reliability analysis of boost converters connected to a solar panel using Markov modeling, which is a powerful technique used for reliability analysis of complex systems that undergo transitions among distinct states, and it is very useful in many practical situations such as smart microgrid energy management systems [11,12], solar farm generation [13], safety systems [14], and large systems [15][16][17][18]. Furthermore, the technique is widely employed to carry out reliability and availability evaluation of systems with fixed failure and repair rates [19].…”

Section: Introductionmentioning

confidence: 99%

“…The impact of parameters on the pertinent failure rates is outlined in Section 2. Section 3 describes the method used to evaluate the reliability of the boost converter, namely a method employing a continuous-time discrete-state Markov chain [12][13][14][15][16][17][18][19][20][21][22][23]. Section 4 discusses reliability analysis of the boost converter.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Boost Converters Connected to a Solar Panel Using a Markov Approach

Muktiadji

Rushdi

2021

JENRR

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In the past few decades, the energy shortage and global warming problems became a serious concern for humanity. To solve these problems, many countries have evolved renewable energy sources (RESs) such as solar, wind, hydro, tidal, geothermal, and biomass energy sources. Solar energy is usually harvested via a solar panel that is connected to a boost converter to supply the loads. The converter has a key role in the system, since it controls the voltage at the DC bus. If any accidental fault occurs in the converter, the solar panel cannot supply electricity to the loads. Therefore, reliability evaluation of the converter is usually warranted. In this study, reliability evaluation of boost converters connected to a solar panel is carried out using the Markov technique. This technique is widely employed to evaluate the reliability and availability of a system with fixed failure and repair rates. Using the Markov method, we found that the reliability of the typical specific converter considered is 0.9986 for and that its life expectancy or Mean-Time-To-Failure (MTTF) is .

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