Availability analysis of a standby complex system having imperfect switch-over device

Singh, Jagdev; Goel, Pardeep

doi:10.1016/0026-2714(94)00041-l

Cited by 13 publications

(6 citation statements)

References 4 publications

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“…Redundancy or standby cannot guarantee system availability. Switching may be imperfect [17], whereby there is a delay between a failure of one unit and the start-up of a standby unit. We consider perfect switching because switching times are typically small relative to unit lifetimes and because switching can be near-instantaneous when standby units are already operating under reduced load (warm standby).…”

Section: Introductionmentioning

confidence: 99%

Availability and Reliability Analysis of a k-Out-of-n Warm Standby System with Common-Cause Failure and Fuzzy Failure and Repair Rates

El-Ghamry

Muse²,

Aldallal

et al. 2022

Mathematical Problems in Engineering

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In the real life, there exists limited information or uncertainty in knowledge about failure and repair rates which follow one of the standard distributions as exponential distribution and Weibull distribution with some parameters. We suppose that these parameters are fuzzy which allow one to specify a system design for a “worst-case scenario.” In this paper, the fuzzy availability and the fuzzy reliability of a redundant repairable parallel k-out-of-n warm standby system with common-cause failure are evaluated. We assume that the failure time of each operating unit or warm standby unit follows Weibull distribution with two fuzzy parameters and the repair time of any failed unit follows exponential distribution with one fuzzy parameter. Each fuzzy parameter is represented by triangular membership function estimated from statistical data taken from random samples of each unit. Also, we give a numerical example for a fuzzy repairable parallel 3-out-of-5 warm standby system with two active and three warm standby units to get analytically and represent graphically the fuzzy availability and reliability function of this fuzzy system.

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Section: Introductionmentioning

confidence: 99%

Availability and Reliability Analysis of a k-Out-of-n Warm Standby System with Common-Cause Failure and Fuzzy Failure and Repair Rates

El-Ghamry

Muse²,

Aldallal

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…To solve reliability problems, one needs to identify assumptions/ constraints of dependences or interdependences of units/ component of the system [1]- [2]. To increase the reliability of the system using standby unit with or without waiting time to repair using the supplementary variable technique have been investigated [3]- [5]. A lot of studies dealing with the reliability and profit of systems have been conducted and analyzed the system behavior, reliability and profit function with respect to time.…”

Section: Introductionmentioning

confidence: 99%

Estimate Reliability Parameters in Bio_Fuel Plant Using Neural Network Architecture

Gupta¹,

Ekata²,

Batra³

2019

IJITEE

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The world’s ever increasing demand for energy and abating global warming, suitable renewable sources of energy are highly in demand. The wastes from industries such as plant’s biomass could meet the energy requirements. In this paper authors analyze bio fuel plant system which produces ethanol fuel. This system is divided into various subsystems considering multiple phases in the production of ethanol. The structure of this system consists of interconnected networks of components on very large dimensional scales escalates the complexity of systems that can increase the degradation of system's functioning. In view of this, one of the computational intelligence approach, neural network (NN), is useful in predicting various reliability parameters. To improve the accuracy and consistency of parameters, Feed Forward Back Propagation Neural Network (FFBPNN) is used. All types of failures and repairs follow exponential distributions. System state probabilities and other parameters are developed for the proposed model using neural network approach. Failures and repairs are treated as neural weights. Neural network's learning mechanism can modify the weights due to which these parameters yield optimal values. Numerical examples are included to demonstrate the results. The iterations are repeated till the convergence in the error tends up to 0.0001 precision using MATLAB code. The reliability and cost analysis of the system can help operational managers in taking the decision to implement it in the real time systems

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“…(Kaushik and Singh 1994) presented the reliability estimation of thermal power plant by using the laplace transform technique. (Singh and Goel 1995) developed a model of a series system composed of four subsystems having one standby sub unit with an imperfect switching. (Kumar, Mehta and Kumar 1997) presented the steady state analysis and maintenance order of the sub unit of fertilizer industry.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Neural Network for Availability Analysis

Garg

Sharma

2019

Math. J. Interdiscip. Sci.

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The appliance of radial basis neural network is demostrated in this paper. The method applies failure and repair rate signals to learn the hidden relationship presented into the input pattern. Statistics of availability of several years is considered and collected from the management of concern plant. This data is considered to train and calidate the radial basis neural network (RBNN). Subsequently validated RBNN is used to estimate the availability of concern plant. The main objective of using neural network approach is that it’s not require assumption, nor explicit coding of the problem and also not require the complete knowledge of interdependencies, only requirement is raw data of system functioning.

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Availability analysis of a standby complex system having imperfect switch-over device

Cited by 13 publications

References 4 publications

Availability and Reliability Analysis of a k-Out-of-n Warm Standby System with Common-Cause Failure and Fuzzy Failure and Repair Rates

Availability and Reliability Analysis of a k-Out-of-n Warm Standby System with Common-Cause Failure and Fuzzy Failure and Repair Rates

Estimate Reliability Parameters in Bio_Fuel Plant Using Neural Network Architecture

Radial Basis Neural Network for Availability Analysis

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