Reliability analysis of a linear consecutive 2-out-of-3 system in the presence of supporting device and repairable service station

This paper studies the reliability characteristics of a linear consecutive 2-out-of-3 cold standby repairable system operating with the help of a repairable external supporting device with preventive maintenance. A repairable service station is set aside to repair any failed unit. The system is analyzed using first order linear differential equation to develop the explicit expression for steady-state availability, busy period, profit function and mean time to system failure (MTSF). Based on assumed numerical values given to system parameters, graphical illustrations are given to highlight important results. Comparisons are performed to highlight the impact of preventive maintenance and found that the 2-out-of-3 cold standby system with preventive maintenance, supporting device and a repairable service station is better

Estimation of Reliability Parameters of a Complex Repairable System

In this paper estimation of reliability parameters of a complex repairable system is considered and semi-markov process is used in analyzing various reliability parameters such as Transition Probabilities, Mean sojourn times, MTSF, Availability and Busy period of repairman in repairing the failed units. In the past, Arora et-al[2] has done reliability analysis of two unit standby redundant system with constrained repair time. Gupta et-al [6] has worked on a compound redundant system involving human failure. Rander et-al [2] has evaluated the cost analysis of two dissimilar cold standby systems with preventive maintenance and replacement of standby units. A pioneer work in this field was done by Gopalan [1] and Osaki [3] by performing analysis of warm standby system and parallel system with bivariate exponential life respectively. Earlier, Pathak et al [7&8] studied reliability parameters of a main unit with its supporting units and also compared the results with two different distributions. In this paper, Chapman-Kolmogorov equations are used to develop recursive relations. Also the involvement of preventive maintenance in the model increases the reliability of the functioning units. In the end a particular case is also taken for discussion

On cost-effective reuse of components in the design of complex reconfigurable systems

Design strategies that benefit from the reuse of system components can reduce costs while maintaining or increasing dependability—we use the term dependability to tie together reliability and availability. D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) is a methodology that supports the design of complex systems with a focus on reconfiguration and component reuse. D3H2 systematizes the identification of heterogeneous redundancies and optimizes the design of fault detection and reconfiguration mechanisms, by enabling the analysis of design alternatives with respect to dependability and cost. In this paper, we extend D3H2 for application to repairable systems. The method is extended with analysis capabilities allowing dependability assessment of complex reconfigurable systems. Analysed scenarios include time-dependencies between failure events and the corresponding reconfiguration actions. We demonstrate how D3H2 can support decisions about fault detection and reconfiguration that seek to improve dependability while reducing costs via application to a realistic railway case study

Evaluation of reliability parameters of a system having three independent components with repair facility

Barlow & Prochan [1] were first to study a complex system taking the component failure and repair times as Independent of each other. In recent years, many papers on reliability such as Li  [2] used multi-state weighted k- out- of- n systems to analyze repairable systems with arbitrary failure time distributions. Exponential distribution plays an important role in the study of system with repair. In order to predict and  estimate or optimize the probability of survival and the mean life, it is essential to take exponential distribution.  Earlier, Goel et al[8 ] have done similar reliability analysis taking units in three different modes. Rander et-al [6] has evaluated the cost analysis of two dissimilar cold standby systems with preventive maintenance and replacement of standby units. A pioneer work in this field was done by Gopalan [3] and Osaki [5] by performing analysis of warm standby system and parallel system with bivariate exponential life respectively. Earlier, Pathak et al [10 & 11] studied reliability parameters of a main unit with its supporting units and also compared the results with two different distributions. We define semi-up mode as the case when the one particular unit is not able to operate due to error in other units which makes these units non-operative. In this paper an attempt has been made by authors by incorporating the concept of semi-up mode and tried to obtain the reliability parameters of working system taking three independent components.&nbsp

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

THE OPTIMAL SYSTEM FOR SERIES SYSTEMS WITH WARM STANDBY COMPONENTS AND A REPAIRABLE SERVICE STATION

This paper deals with the reliability and availability characteristics of three different series system configurations with warm standby components and a repairable service station. The failure time of the primary and warm standby are assumed to be exponentially distributed with parameters and respectively. The repair time distribution of each server is also exponentially distributed with parameter . The breakdown time and the repair time of the service station are also assumed exponentially distributed with parameters and respectively. We derive the reliability dependent on time, availability dependent on time, the mean time to failure, , and the steady-state availability for three configurations and perform comparisons. Comparisons are made for specific values of distribution parameters and of the cost of the components. The three configurations are ranked based on: , and where is either or