Fast availability simulation

An approach for simulating models of highly dependable systems with general failure and repair time distribution is described. The approach combines importance sampling with event rescheduling in order to obtain variance reductions in such rare event simulations. The approach is general in nature and allows a variety of features commonly arising in dependability modeling to be simulated effectively. It is shown how the technique can be applied to systems with redundant components and/or periodic maintenance. For different failure time distributions, the effect of the maintenance period on the steady-state availability is explored. The amount of component redundancy needed to achieve a certain reliability level is determined. >

The failure and repair of modules in an N-modular redundant (NMR) system are governed by a failure time distribution and a repair time distribution, respectively. It is generally reasonable to assume that a module's failure time distribution is a simple exponential distribution. However, it is not reasonable to assume that the repair time distribution is also exponential. Reliability models with non-exponential repair have a higher computational complexity than a model of the same system with an exponential repair time distribution. This paper presents the results of a systematic study to determine whether non-exponential repair distributions produce significant differences in calculated NMR system unreliability, relative to an exponential repair distribution with the same mean time to repair (MTTR). Our approach is to embed Erlang repair distributions in generalized stochastic Petri net (GSPN) models of NMR systems and evaluate the unreliability. Our results show that, for a wide range of system parameters, the choice of a repair time distribution has minimal impact on the calculated unreliability. Rather, it is the MTTR that is the dominant parameter affecting unreliability.

A 2-out-of-3:F system with distinct components in the sense of having different failure and repair time distributions, is analysed in three different cases. The failure and repair time distributions are discrete, exponential, and general in cases 1, 2, and 3 respectively. Mean time to failure (MTTF) of the system in each case is obtained. The parameters are estimated using the method of maximum likelihood for Markov Processes.

Computation of bounds for transient measures of large rewarded Markov models using regenerative randomization

Cost-benefit analysis of a two-unit warm standby reliability system with two types of repair facilities

In recent years, many researches on economic production quantity (EPQ) models with machine breakdown and preventive maintenance have been developed, but few of them have developed integrated models for deteriorating items. In this study, we develop EPQ models for deteriorating items with preventive maintenance, random machine breakdown and immediate corrective action. Corrective and preventive maintenance times are assumed to be stochastic and the unfulfilled demands are lost sales. Two EPQ models of uniform distribution and exponential distribution of corrective and maintenance times are developed. An example and sensitivity analysis is given to illustrate the models. For the exponential distribution model, it is shown that the corrective time parameter is one of the most sensitive parameters to the optimal total cost.

Analysis of a two server-two unit cold standby system with correlated failures and repairs

System availability is an important measure of system effectiveness. This is particularly true of systems which are required to be “on station” more or less continuously, e.g., early warning systems, patrol craft and others. This paper presents a Generalized Sequential Probability Ratio Test (GSPRT) of the hypothesis where A = θ/(θ + ϕ) and θ, ϕ are means of the exponential distributions of failure and repair times respectively. The ASN and O. C. curves are discussed. In addition, termination is shown and examples are given. The availability, defined above, is a point availability and if logistics and administrative delays are excluded from restoration time, it is often called inherent availability; otherwise, it is called operational availability.

This paper considers the stochastic analysis of a complex system having three units. The first two units are designated as unit-1 and unit-2 while the third unit is called an auxiliary unit (unit-3) which enhances the range of transmission and efficiency of unit-1. Joint distributions of failure and repair times are bivariate exponential (B.V.E.) with different parameters. Regenerative point technique is used to obtain various reliability characteristics of the system under study. Behaviour of some characteristics has also been studied through graphs.

Cost-benefit analysis of a system with inspection, replacement and two types of repair

Reliability and M.T.T.F. evaluation of a two duplex-unit standby system with two types of repair

On a two-dissimilar-unit standby system with three modes and administrative delay in repair

Cost analysis of a two-dissimilar unit cold standby redundant system with administrative delay and no priority in repair

Analysis of a two unit standby system with preparation time and correlated failures and repairs

A warm standby redundant system with correlated failures and repairs