Comparative study between a single unit system and a two-unit cold standby system with varying demand

Malhotra, Rashmi; Taneja, Gulshan

doi:10.1186/s40064-015-1484-7

Cited by 18 publications

(9 citation statements)

References 13 publications

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“…Yusuf et al (2021) analyzed the reliability and performance of series-parallel system using copula. Malhotra and Taneja (2015) developed and compared reliability models of varying demand cold standby systems. Malhotra et al (2021) analyzed the reliability of redundant cold standby two-unit system with preventive maintenance.…”

Section: Introductionmentioning

confidence: 99%

Reliability, Availability, Maintainability, and Dependability Analysis of Cold Standby Series-Parallel System

Danjuma¹,

Yusuf²,

Yusuf

2022

JCCE

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This paper dealt with the evaluation reliability, availability, maintainability, dependability, mean time between failures, and mean time to failure of series-parallel system. The system under investigation has four subsystems, namely subsystem A containing two units in cold standby, subsystem B and C possess one unit each, and subsystem D has two units in cold standby. Through the transition diagram of each subsystem and the Markov birth-death process, Chapman Kolmogorov forward equations are derived. Both failure and repair rates of units in each subsystem are assumed to follow exponential distribution. The objective is to derive the corresponding reliability models of dependability, availability, maintainability, and reliability and capture the effect of system parameters on reliability, availability, maintainability, and dependability and to determine the critical subsystems. On the basis of numerical results obtained, the system's performance has been evaluated. Moreover, the outcome of this study shows that the ideal system reliability can be achieved when the overall system failure rate is low and the supporting units are activated.

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Section: Introductionmentioning

confidence: 99%

Reliability, Availability, Maintainability, and Dependability Analysis of Cold Standby Series-Parallel System

Danjuma¹,

Yusuf²,

Yusuf

2022

JCCE

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“…Authors have incorporated inspection policy into their model and assumed correlated failure and repair time. Malhotra and Taneja (2013, 2014, 2015 studied the effect of varying demand on system reliability and profit. These studies are further based on the regenerative point technique.…”

Section: Introductionmentioning

confidence: 99%

Reliability Modeling of a Food Industrial System with two Types of Repair Persons wherein Demand is Season Dependent

Monika

Chopra

2022

Int. j. math. eng. manag. sci.

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The present study pertains to developing a reliability model concerning a food industrial system that runs throughout the month except for Sundays. There are two types of repair persons engaged with the system, one known as operator while another is fitter. The operator is responsible for minor repairs, while the fitter is responsible for major repairs. It is also noticed that the operator is the first person who attends the failed unit in case of a major failure. If the operator is found incapable of repairing the unit within some patience time, then the fitter is called. While gathering the actual month-wise data of the plant, two types of seasons, namely normal and festival seasons, have been recorded. The festival season is from July to November, while the normal season is from December to June, based on consumer demand. During the months of the normal season, the system is shut down for a few days as the demand gets accomplished before the month ends. However, during the festival season, the system has to be kept operational throughout the month to fulfill the high demand for the product. The semi-Markov process and regenerative point technique are used to assess the reliability measures, namely mean time to system failure (MTSF), system availability in both seasons, expected busy period of the repair persons, and expected downtime of the system. The overall profitability of the system is also demonstrated by Graphical interpretation.

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“…The Industrial system often runs their machines whenever there is the demand for their product in the market. Two different systems, namely single unit system and a two-unit cold standby system, with varying demand were analysed by Malhotra and Taneja (2015). Comparison in the profit of three redundant systems each supporting the external device was done by (Yusuf, 2014).…”

Section: Introductionmentioning

confidence: 99%

Application of Markov process/mathematical modelling in analysing communication system reliability

Kumar

2019

IJQRM

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Purpose The purpose of this paper is to analyse the main components of a wireless communication system, e.g. input transducer, transmitter, communication channel and receiver on the basis of their interconnection for evaluating the various reliability measures for the same. Design/methodology/approach Markov process and mathematical modelling is used to formulate a mathematical model of the considered system (on the basis of various failures/repairs). Findings Reliability of the wireless communication system with respect to its components failure is obtained and explained with the graphs. Also, critical components of the system are identified with the aid of sensitivity analysis. At last, mean time to failure with variation in various failures is obtained. Originality/value In the present paper, a mathematical model based on the working of the wireless communication system has been developed. Conclusions in this paper are good references for the improvement in the same.

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Comparative study between a single unit system and a two-unit cold standby system with varying demand

Cited by 18 publications

References 13 publications

Reliability, Availability, Maintainability, and Dependability Analysis of Cold Standby Series-Parallel System

Reliability, Availability, Maintainability, and Dependability Analysis of Cold Standby Series-Parallel System

Reliability Modeling of a Food Industrial System with two Types of Repair Persons wherein Demand is Season Dependent

Application of Markov process/mathematical modelling in analysing communication system reliability

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