Reliability and Sensitivity Analysis of the k-out-of-n:G Warm Standby Parallel Repairable System with Replacement at Common-Cause Failure using Markov Mode

2016 ◽  
Vol 5 (3) ◽  
pp. 521-533
Author(s):  
Medhat Ahmed El-Damcese ◽  
Naglaa Hassan El-Sodany
Author(s):  
ZHIJIE PAN ◽  
YASUO NONAKA

In quantitative analysis of common cause failures (CCFs), importance measures for ranking the importance of the CCF events will be significant to limit the CCF analysis framework, find out efficient defence strategies against CCF, and make sensitivity analysis for them. In this paper, three importance measures are defined for different types of common cause failure events: Structure Importance, Probability Importance, and β-Importance. Simplified algorithms for calculating these importance measures are developed.


2019 ◽  
Vol 37 (3) ◽  
pp. 1043-1071
Author(s):  
Chandra Shekhar ◽  
Amit Kumar ◽  
Shreekant Varshney ◽  
Sherif I. Ammar

Purpose The internet of things and just-in-time are the embryonic model of innovation for the state-of-the-art design of the service system. This paper aims to develop a fault-tolerant machining system with active and standby redundancy. The availability of the fault-tolerant redundant repairable system is a key concern in the successful deployment of the service system. Design/methodology/approach In this paper, the authors cogitate a fault-tolerant redundant repairable system of finite working units along with warm standby unit provisioning. Working unit and standby unit are susceptible to random failures, which interrupt the quality-of-service. The system is also prone to common cause failure, which tends its catastrophe. The instantaneous repair of failed unit guarantees the increase in the availability of the unit/system. The time-to-repair by the single service facility for the failed unit follows the arbitrary distribution. For increasing the practicability of the studied model, the authors have also incorporated real-time machining practices such as imperfect coverage of the failure of units, switching failure of standby unit, common cause failure, reboot delay, switch over delay, etc. Findings For deriving the explicit expression for steady-state probabilities of the system, the authors use a supplementary variable technique for which the only required input is the Laplace–Stieltjes transform (LST) of the repair time distribution. Research limitations/implications For complex and multi-parameters distribution of repair time, derivation of performance measures is not possible. The authors prefer numerical simulation because of its importance in the application for real-time uses. Practical implications The stepwise recursive procedure, illustrative examples, and numerical results have been presented for the diverse category of repair time distribution: exponential (M), n-stage Erlang (Ern), deterministic (D), uniform (U(a,b)), n-stage generalized Erlang (GE[n]) and hyperexponential (HE[n]). Social implications Concluding remarks and future scopes have also been included. The studied fault-tolerant redundant repairable system is suitable for reliability analysis of a computer system, communication system, manufacturing system, software reliability, service system, etc. Originality/value As per the survey in literature, no previous published paper is presented with so wide range of repair time distribution in the machine repair problem. This paper is valuable for system design for reliability analysis of the fault-tolerant redundant repairable.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Jinlei Qin ◽  
Zheng Li

With the increasing complexity of industrial products and systems, some intermediate states, other than the traditional two states, are often encountered during reliability assessments. A system with more than two states is called a multistate system (MSS) which has already become a general phenomenon in the components and/or systems. Moreover, common cause failure (CCF) often plays a very important role in the assessment of system reliability. A method is proposed to assess the reliability and sensitivity of an MSS with CCF. Some components are not only in a failure state that can cause failure itself, but also in a state that can cause the failure of other components with a certain probability. The components that are affected by one type of CCF make up some sets which can overlap on some components. Using the technology of a universal generating function (UGF), the CCF of a component can be incorporated in the expression of its UGF. Consequently, indices of reliability can be calculated based on the UGF expression of an MSS. Sensitivity analysis can help engineers to judge which type of CCF should be eliminated first under various resource limitations. Examples illustrate and validate this method.


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