Multi-state failure phenomenon and analysis using semi-Markov model

This paper combines time varying failure rates and Markov chain analysis to obtain a hybrid reliability and availability analysis. However, combining these techniques can, depending on the size of the system, result in solutions of the Markov chain differential matrix equations that are intractable. This paper identifies solutions that are tractable, These form the analytical baseline for the reliability and availability analysis of systems with time varying failure rates. Tractable solutions were found for the 1-component 2-state and the 2-component 4-state configurations. Time varying failure rates were characterized by a general polynomial expression. Constant, linear, and Weibull failure rate functions are special cases of this polynomial. The general polynomial failure rate provides flexibility in modeling the time varying failure rates that occur in practice.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The purpose of this paper is to present a methodology for analyzing the system performance of an industrial system by utilizing uncertain data. Although there have been tremendous advances in the art and science of system evaluation, yet it is very difficult to assess their performance with a very high accuracy or precision. For handling of these uncertainties, fuzzy set theory has been used in the analysis while their corresponding membership functions are generated by solving a nonlinear optimization problem with particle swarm optimization. For finding the critical component of the system which affects the system performance mostly, a composite measure of reliability, availability and maintainability (RAM) named as the RAM-index has been introduced which influences the effects of failure and repair rate parameters on its performance. A time varying failure and repair rate parameters are used in the analysis instead of constant rate models. Finally, the computed results are finally compared with existing methodologies. The suggested framework has been illustrated with the help of a case.

Reliability, availability and maintainability analysis of hot standby database systems

A semi‐Markov model of a system operation process is proposed and its selected parameters are determined. A series–parallel multi‐state system is considered and its reliability and risk characteristics are found. Next, the joint model of the system operation process and the system multi‐state reliability and risk is constructed. Finally, the asymptotic approach to reliability, availability and risk evaluation of the multi‐state series–parallel system in its operation process is applied to the port grain transportation system. Copyright © 2006 John Wiley &amp; Sons, Ltd.

\n Nowadays, Dynamically Positioned (DP) units are responsible for most of offshore oil exploitation operations, including drilling and maintenance campaigns. Due to the larger congestion of the oil fields, keep the vessel position, despite the environmental forces, is a critical issue. This study aims to propose a methodology and apply it for a RAM analysis (Reliability, Availability &amp; Maintainability) of DP System of two different generations (DP class 2 and DP class 3) in order to obtain accurate information that allows subsequently define their restriction diagrams. The DP system, including various subsystems, DP classes and applications, are introduced and discussed. In addition, the necessary concept and techniques to analyze the Reliability, Availability and Maintainability of DP system are explained. In the reliability analysis, two interest events (free drift and controlled drift) are evaluated for different operation times (12, 9, 6 and 3 months); and, as expected, the DP3 system is more reliable than DP2 system in all scenarios. Regarding maintainability, the Monte Carlo Simulation exposed that in more than 90% of the free drift, the maintenance teams of the DP systems under study are able to recover the positioning of the units in the first 6 hours; while the recovery times lower than 3 hours, represent 43% of simulated free drifts. The asymptotic availability analysis of DP2 and DP3 systems shows availability greater than 0.9998 even for operational times greater than 15 years. The study concludes with an evaluation of the impact of the uncertainty in the failure rates of critical components of DP systems and the use of a non-parametric method for the design of probability density function of repair times. Finally, it is suggested that future studies take into consideration the impact of human factors in the DP system RAM analysis.\n

Reliability and availability analysis of high-altitude platform stations through semi-Markov modeling

Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package provides a variety of probabilistic, discrete-state models used to assess the reliability and performance of computer and communication systems. The models included are combinatorial reliability models (reliability block diagrams, fault trees and reliability graphs), directed, acyclic task precedence graphs, Markov and semi-Markov models (including Markov reward models), product-form queueing networks and generalized stochastic Petri nets. A practical approach to system modeling is followed; all of the examples described are solved and analyzed using the SHARPE tool. In structuring the book, the authors have been careful to provide the reader with a methodological approach to analytical modeling techniques. These techniques are not seen as alternatives but rather as an integral part of a single process of assessment which, by hierarchically combining results from different kinds of models, makes it possible to use state-space methods for those parts of a system that require them and non-state-space methods for the more well-behaved parts of the system. The SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) package is the `toolchest' that allows the authors to specify stochastic models easily and solve them quickly, adopting model hierarchies and very efficient solution techniques. All the models described in the book are specified and solved using the SHARPE language; its syntax is described and the source code of almost all the examples discussed is provided. Audience: Suitable for use in advanced level courses covering reliability and performance of computer and communications systems and by researchers and practicing engineers whose work involves modeling of system performance and reliability.

Components within a system may be related to one another in two primary ways: in either a serial or a parallel configuration. The other way associated system components is developed on the basis of series and parallel systems. Although some complex systems cannot generate from series and parallel system, the result of this paper is helpful to study more complex gradual system reliability problems. In this paper, a hypothesis is formed that the reliability of the components of the system is variable with time. Then, the reliability of the system is gradual, on condition that the components are combined by some related way. If the system reliability analysis is based on the static analysis, it may be different from the actual value of the reliability because the components of the system would afford stochastic load or generate degradation of performance. The method considering the random resistant degradation may make the result of reliability accurate. In this paper, gamma process is used to simulate the use of the components of the system, as deterioration is generally uncertain and non-decreasing, it can best be regarded as a gamma process. Combining the Edgeworth series method and forth moment technique, the method that calculate the reliability of series and parallel system is deduced. Then, the reliability of different systems and the failure rate of the systems are illustrated with time. The results show that the reliability of the systems will decline in different ways with time.

Reliability and safety analysis for systems of fusion device

ABSTRACTA framework for reliability and maintenance analysis of job shop manufacturing systems is proposed in this paper. An efficient preventive maintenance (PM) policy in terms of failure effects analysis (FEA) is proposed. Subsequently, reliability evaluation and component importance measure based on FEA are performed under the PM policy. A job shop manufacturing system is applied to validate the reliability evaluation and dynamic maintenance policy. Obtained results are compared with existed methods and the effectiveness is validated. Some vague understandings for issues such as network modelling, vulnerabilities identification, the evaluation criteria of repairable systems, as well as PM policy during manufacturing system reliability analysis are elaborated. This framework can help for reliability optimisation and rational maintenance resources allocation of job shop manufacturing systems.

Reliability analysis and initial requirements for FC systems and stacks

The problem of reliability and exploitation analysis of power systems used in intelligent transport systems (ITS) is extremely important from the point of view of ensuring the traffic safety. The use of advanced electronic devices in ITS systems requires their power supply from properly designed power systems. To ensure the continuity of operation of electronic devices, the basic and backup power supply are most often used. They should have specific values of reliability and performance indicators. Therefore, a reliability and exploitation model of the power system has been developed, taking into account renewable energy sources (RES), such as used in intelligent transport systems. This allowed to obtain dependencies enqabling to determine the probability of staying of the power system used in the intelligent transport system in the specified reliability and exploitation states.

This paper addresses the problem of mixed uncertainty in the reliability analysis of multistate systems under common cause failure conditions. Combining the cloud model theory, universal generation function (UGF) method, and common cause failure theory, the universal generation function method is extended based on a probabilistic cloud model, i.e., the cloud universal generation function (CUGF) analysis method. The cloud model represents the random and cognitive uncertainty of the state probability, i.e., mixed uncertainty. Next, through CUGF, according to the calculation rules of cloud operators, we provide steps to obtain the reliability of a multistate system under independent failure and common cause failure conditions and obtain cloud digital features for reliability. The accuracy and feasibility of the method are verified by a numerical example. This paper solves the problem of reliability analysis of multistate systems with mixed uncertainty in unit state probability information under common cause failure conditions. We integrate system multistate, information uncertainty, and common cause failure for reliability analysis to avoid large errors, more in line with a project’s actual situation. We propose new ideas and methods to process randomness and fuzzy information or data in multistate system reliability analysis.

In many practical cases, during a system failure or downtime, all non-failed components are kept idle to eliminate further damage to the system. This phenomenon is known as suspended animation (SA) because the aging process of the non-failed components is suspended. Suspended animation introduces dependencies among the component states. Therefore, we cannot calculate the system availability using the methods that are used to calculate the system reliability. In this paper, we provide a simple and efficient method to compute the availability indices of repairable systems subject to suspended animation. Using this method, we propose efficient algorithms for k-out-of-n systems. An important aspect of the proposed method is that it is not restricted to exponential failure and repair distributions. This method is also applicable for certain imperfect repair situations. Further, it can be applied to any system configuration with embedded hierarchical k-out-of-n subsystems subjected to suspended animation.

In consideration of some drawbacks of stochastic reliability method in theoretical and practical aspects, as well as the existence of insufficient and vague data in actual equipments design, the possbilistic reliability models of typically general systems are constructed in this paper. Characterizing the systems(components) failure behavior by possibility measures in place of probability measures, treating the systems (components) lifetime as fuzzy variables in the possibility space, and introducing possibilistic approaches into system reliability modeling and analysis, the possibilistic reliability theories are advanced based on possibilistic measurements. By expanding the universe of discourse, the derivation of system reliability models is simplified without loss of the nature of the problems to be solved, and proofs and calculations of possibilistic reliability of typical systems including series, parallel, series-parallel, parallel-series, and cold standby systems are much more straightforward than the traditional ones. The detailed application examples in system reliability analysis verify a possibilistic design framework on the general reliability models background, which provide some technical supports and guarantees for possibilistic reliability design and possibilistic reliability management in the case of limited statistical data.