Research Article  Open Access
Renbin Liu, Yong Wu, "A New Approach for Analyzing the Reliability of the Repair Facility in a Series System with Vacations", Journal of Applied Mathematics, vol. 2012, Article ID 182975, 16 pages, 2012. https://doi.org/10.1155/2012/182975
A New Approach for Analyzing the Reliability of the Repair Facility in a Series System with Vacations
Abstract
Based on the renewal process theory we develop a decomposition method to analyze the reliability of the repair facility in an nunit series system with vacations. Using this approach, we study the unavailability and the mean replacement number during of the repair facility. The method proposed in this work is novel and concise, which can make us see clearly the structures of the facility indices of a series system with an unreliable repair facility, two convolution relations. Special cases and numerical examples are given to show the validity of our method.
1. Introduction
It is well known that the repairable systems with an unreliable repair facility are frequently studied in the literature of reliability theory. Theoretically, they are in a class of more general repairable systems, and the usual repairable systems are their special cases [1].
Common approaches in the reliability analysis of repairable systems with a repair facility include the Markov renewal process method, the geometric process method, the supplementary variable method, and the strong stability method. The Markov renewal process method has important applications in reliability theory. By means of this method, the reliability indices of various systems were obtained in the previous literatures (see, e.g., [2, 3]). The geometric process proposed by Lam [4] is a special monotone process, which can model the working times and the repair times of a deteriorating system. Much research has been conducted by Jia and Wu [5, 6], Wu and ClementsCroome [7], Zhang and Wang [8], and others under the monotone process model. Moreover, with the help of the geometric process method, a deteriorating system with its repairman having multiple vacations was studied by Yuan and Xu [9]. They obtained the optimal replacement policy which could make the longrun expected cost per unit time minimize. The strong stability method studied by Rahmoune and Aissani [10] and other researchers is a more powerful tool. For example, Rahmoune and Aissani [10] applied this method to analyze the characteristics of the queue with multiple vacations where the rate of the vacations is sufficiently small. This method is also useful in analyzing complex repairable systems, such as, redundant repairable systems with unreliable facilities, with vacations, or with priority in use. The supplementary variable technique was first introduced by Cox [11], and it had been widely applied to complex repairable systems by Li et al. [12], Liu et al. [13, 14], and many others. Also, recently, Guo et al. [15] have derived the steadystate reliability indices of a series system by using the eigenfunction corresponding to eigenvalue 0 of the system operator.
However, these methods mentioned above usually become too complicated to be solved especially when dealing with some complex systems with many random variables following general distributions. In this paper, a new approach, called decomposition method here, is developed based on the renewal process theory. With this novel method, we analyze the unavailability and the mean replacement number during of the repair facility in an unit series system with an unreliable repair facility and vacations. To be precise, we obtain two convolution relations for the unavailability and the mean replacement number. The two convolution relations turn studying the unavailability and the mean replacement number during into studying the corresponding reliability indices in the classical oneunit system and the “generalized busy period of the repair facility” (see Definition 2.3). In addition, some new reliability results are also obtained.
To introduce our method, we consider an unit series system with a nonreliable repair facility and multiple adaptive vacations. We aim at obtaining two key reliability quantities of the repair facility, that are, the replacement probability (the unavailability of the repair facility) and the average number of replacements during . Note that the two indices have been investigated by Liu et al. [13, 14] with the help of the supplementary variable method. In this paper we study again the above two indices by means of a decomposition method, which is completely different from the methods used in [2–15]. Our main idea is as follows: with the definition of “generalized busy period of the repair facility,” we obtain the probability that the repair facility is during its generalized busy period at time ; based on this, using our decomposition method, we derive the unavailability and average replacement number of the repair facility, which lead to two convolution relations; finally, we present some special cases and numerical examples to show the validity of our method.
The rest of the paper is organized as follows. The next section gives the assumptions of the considered model and some preliminaries. In Section 3 we use a decomposition method to discuss two key reliability indices of the repair facility. In Sections 4 and 5 special cases and numerical examples are presented to validate the derived results. Conclusions are finally drawn in Section 6.
2. Assumptions and Preliminaries
We consider a series repairable system consisting of dissimilar units, a repairman, and an unreliable repair facility by making the following assumptions.(1)Whenever one unit fails, the system breaks down. It is assumed that units shut each other off, and each unit after repair is as good as new. As soon as the repair of the failed unit is completed, the system starts to operate immediately.(2)The repairman will take a random maximum number, denoted by , of vacations whenever there is no failed unit waiting for repair in the system. The probability mass function of is , . The random variable may represent the maximum number tasks or jobs available for repairman to work on if the system is operating. Denote the repairman’s th vacation time by , . Assume that , are independent and identically distributed random variables each with the probability distribution function and a mean vacation time . At each vacation completion instant, the repairman checks the system to see if there is a failed unit waiting and decides the action to take according to the state of the system. There are three cases: (a) if there is a failed unit waiting, the repairman will repair it immediately; (b) if there is no failed unit waiting and the total number of vacations is still less than , the repairman will take another vacation; (c) if there is no failed unit waiting and the total number of vacations is equal to , the repairman will stay idle and wait for the next failure.(3)The lifetime of unit follows an exponential distribution , , . The repair time of the failed unit obeys a general distribution function , with a mean repair time , .(4)It is assumed that the repair facility neither fails nor deteriorates in its idle periods and fails only during its “generalized busy periods” presented by this paper. The life span of the repair facility has an exponential distribution , , .(5)When the repair facility fails, it is replaced by a new and identical one immediately and the unit that is being repaired has to wait for repair. The replacement time of the repair facility has an arbitrary distribution with a mean replacement time . After replacement, the new repair facility starts its repair to the failed unit whose repair was stopped. The repair time for the failed unit is cumulative, that is, the repaired time of this failed unit is still valid.(6)Initially, the system with new units begins to operate, the new repair facility is during its idle period, and the repairman begins to take multiple adaptive vacations. All random variables are mutually independent.
Remark 2.1. The multiple adaptive vacation policy presented by this paper is introduced by Tian and Zhang [16], which is a generalization of single vacation, multiple vacations and variant vacation, and so forth. The analysis in this work shows that our method is valid in studying the reliability indices of the repair facility of complex vacation systems with an unreliable repair facility.
For ease of reference, we list some notations and state two definitions as shown at the end of the paper.
Definition 2.2. The “generalized repair time of unit ” , denotes the time interval from the time when the repair facility begins to repair the failed unit until the repair of the failed unit ends, which includes some possible replacement times due to the facility failures in the process of repairing the failed unit .
For , let , then (see [1])
The LaplaceStieltjes transform of is given by
where and are the LaplaceStieltjes transforms of and , respectively. The mean of the generalized repaired time of the failed unit is given by
Definition 2.3. The “generalized busy period of the repair facility” denotes the time interval from the time when the repair facility begins to repair a failed unit until the repair of the failed unit completes, where contains some possible replacement times due to the facility failures in the process of repairing the failed unit.
Remark 2.4. If the system’s operating time , then , .
3. Reliability Analysis of the Repair Facility
In this section, by means of a decomposition method we will discuss the probability that the repair facility is during its generalized busy period at time . On this basis, we study the unavailability and the replacement number during of the repair facility. Finally, we analyze the structures of the facility indices in our system.
3.1. The Probability That the Facility Is during Its Generalized Busy Period
For , let
Theorem 3.1. If , then the Laplace transform of is and, in steady state, the probability that the repair facility is during its generalized busy period is given by where
Proof. Let be the event that the repair facility is during its generalized busy period at time . Denote and be the first operating time and the first repair time of the system, respectively. By applying the total probability decomposition technique and noting that the time points that the system begins to operate are renewal ones, we have The first term of (3.5) can be decomposed as Similarly, we can decompose the second term of (3.5) as Taking the Laplace transforms of (3.5)–(3.7) and simplifying lead to Solving (3.8), we can get (3.2). Applying the wellknown Tauberian theorem [17], we have Putting (3.2) into the above equation and using L' Hospital's rule complete the proof of Theorem 3.1.
To study the unavailability and the replacement number during (0, ] of the repair facility, we first consider a classical oneunit replaceable system [18, 19]. When the unit fails, it is replaced by a new and identical one immediately. The lifetime of the unit has an exponential distribution (), and the replacement time of the failed unit obeys an arbitrary distribution with a mean replacement time . and are mutually independent. After replacement, the new unit begins to operate immediately. For , let and , .
Lemma 3.2 (see [18, 19]). If , then
3.2. The Unavailability of the Repair Facility
Now, we discuss the unavailability of the repair facility at time , that is, the probability that the repair facility is being replaced at time . For , let
Theorem 3.3. Let . If , then and the steadystate unavailability of the repair facility is given by where and are given by Lemma 3.2 and (3.2), respectively.
Proof. (i) In the light of the assumptions, the repair facility neither fails nor deteriorates in its idle period and it is available at the beginning time as well as the ending time of each generalized busy period, then the repair facility is replaced at time if and only if the time is during one generalized busy period of the repair facility, and the repair facility is replaced at time . Therefore, according to total probability decomposition and the renewal point technique, is decomposed as
where ; the repair facility is being replaced at time .
(ii) For ,
where is determined by Lemma 3.2.
In fact, in the generalized busy period the repair facility may be in one of two states: operation or replacement, and it is available at the beginning and ending times of each generalized busy period. According to the fact that the life span of the repair facility has an exponential distribution, and conditioning on the generalized busy period, we have the decomposition of as follows:
So (3.16) holds.
(iii) Substituting (3.16) into (3.15) and making the Laplace transform we get (3.13). Equation (3.14) is obtained by the Tauberian theorem, Lemma 3.2 and (3.3).
3.3. The Replacement Number of the Repair Facility
For , let
Theorem 3.4. Let . If , then and the steadystate replacement frequency of the repair facility is
Proof. For , let
then similar to (3.16) above, by conditioning on the generalized busy period we have
where is determined by Lemma 3.2.
Using total probability decomposition, we have the decomposition of as follows:
Based on the definition of and the renewal point technique, the sum of the first and second terms of (3.23) gives
Similarly, by the definitions of and , the sum of the third and fourth terms of (3.23) is decomposed as
Inserting (3.24), (3.25), and (3.22) in (3.23), taking the LaplaceStieltjes transform of (3.23) and simplifying lead to
Solving (3.26) and using (3.2) we get (3.19). By the Tauberian theorem, we have
Substituting (3.19) into the above equation and applying Lemma 3.2 and (3.3) give (3.20).
Remark 3.5. Taking the Laplace and LaplaceStieltjes inverse transforms of (3.13) and (3.19), respectively, we obtain two convolution relations as follows:
Clearly, (3.28) turn studying the unavailability and the replacement number during of the repair facility into studying the corresponding indices in the classical oneunit system and the probability that the repair facility is during its generalized busy period presented in this paper. It is important that the above results are not derived by the supplementary variable method in [13, 14].
Remark 3.6. It is seen from (3.3), (3.14), and (3.20), that the steadystate relation equations and also hold. The two steadystate relations are also new, which are not obtained in [13, 14].
Remark 3.7. By the decomposition method used in this paper, we can also analyze the reliability indices of the series system presented by this paper.
4. Special Cases
Case 1. If , that is, the repairman in the system takes multiple vacations, then (3.3), (3.14), and (3.20), can be simplified as
where .
In this case, the steadystate unavailability and steadystate replacement frequency are identical to those in [13], which are analyzed with the help of the supplementary variable method. Also, the steadystate probability is not obtained in [13].
Case 2. If , , that is, the repairman in the system takes a single vacation, then
where .
In this case, and are in accordance with the results obtained by Liu et al. [14], which are discussed by means of the supplementary variable method. In addition, is a new result.
Case 3. If , and , , then letting , we get the following indices of the repair facility of the series system without vacation:
where .
In this special case, and coincide with the results obtained in [13] or [14], which are derived using the supplementary variable method.
5. Numerical Examples
In this section, some numerical examples are presented to analyze the effects of various parameters on the derived steadystate results, including the probability that the repair facility is during its generalized busy period , the unreliability and the replacement frequency . For convenience, we consider an identicalcomponent series system with a nonreliable repair facility and multiple adaptive vacations. Let(1), , ;(2) follows a geometric distribution with probability generating function , ;(3) obeys an exponential distribution with a mean .
Numerical results are reported in Tables 1–3. Table 1 shows that the effects of varying mean repair time on the reliability indices of the facility for the set of parameters . We observe that the probability , the unreliability and the replacement frequency all increase monotonously as the value increases. The effects of varying failure rate on the reliability indices of the facility are shown in Table 2, where we set . As is to be expected, the probability , the unreliability , and the replacement frequency all decrease monotonously as the rate decreases. Table 3 reports the effects of varying mean replacement time on the reliability indices of the facility for the set of parameters . We note that when increases, both and increase, while decreases. The trends shown by the tables are as expected.



6. Conclusions
In this paper, based on the renewal process theory we develop a new approach—the decomposition method. According to the method, we analyze the unavailability and average replacement number during of the repair facility in a series vacation system with an unreliable repair facility. On this basis, we derive two convolution relations, which make us see clearly the structures of the facility indices in our system. Special cases and numerical examples show that our method is valid in studying the reliability indices of the repair facility of complex vacation systems with a nonreliable repair facility.
Notations
:  The LaplaceStieltjes transform of , that is, 
:  The Laplace transform of , that is, 
:  The fold convolution of with itself and , 
:  The mean of random variable 
:  
:  The real part of complex number 
:  The first operating time length of the system 
:  The first repair time length of the system 
:  The probability of event 
:  The probability generating function of . That is, . 
Acknowledgments
The authors would like to thank the anonymous referees and editor for their valuable comments and suggestions. This work is supported by the Youth Foundation of Chongqing University of Technology of China (no. 2010ZQ14).
References
 J. H. Cao and Y. H. Wu, “Reliability analysis of a multistate system with a replaceable repair facility,” Acta Mathematicae Applicatae Sinica, vol. 4, no. 2, pp. 113–121, 1988. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 K. Cheng, “Reliability analysis of a system in a randomly changing environment,” Acta Mathematicae Applicatae Sinica, vol. 2, no. 3, pp. 219–228, 1985. View at: Google Scholar
 J. H. Cao, “Availability and failure frequency of a Gnedenko system,” Annals of Operations Research, vol. 24, no. 1–4, pp. 55–68, 1990. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 Y. Lam, “A note on the optimal replacement problem,” Advances in Applied Probability, vol. 20, no. 2, pp. 479–482, 1988. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 J. Jia and S. Wu, “A replacement policy for a repairable system with its repairman having multiple vacations,” Computers and Industrial Engineering, vol. 57, no. 1, pp. 156–160, 2009. View at: Google Scholar
 J. Jia and S. Wu, “Optimizing replacement policy for a coldstandby system with waiting repair times,” Applied Mathematics and Computation, vol. 214, no. 1, pp. 133–141, 2009. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 S. Wu and D. ClementsCroome, “Optimal maintenance policies under different operational schedules,” IEEE Transaction on Reliability, vol. 54, no. 2, pp. 338–346, 2005. View at: Google Scholar
 Y. L. Zhang and G. J. Wang, “A deteriorating cold standby repairable system with priority in use,” European Journal of Operational Reserch, vol. 183, no. 1, pp. 278–295, 2007. View at: Google Scholar
 L. Yuan and J. Xu, “A deteriorating system with its repairman having multiple vacations,” Applied Mathematics and Computation, vol. 217, no. 10, pp. 4980–4989, 2011. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 F. Rahmoune and D. Aissani, “Strong stability of queues with multiple vacation of the server,” Stochastic Analysis and Applications, vol. 26, no. 3, pp. 665–678, 2008. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 D. R. Cox, “The analysis of nonMarkovian stochastic processes by the inclusion of supplementary variables,” Proceedings of the Cambridge Philosophical Society, vol. 51, pp. 433–441, 1955. View at: Google Scholar  Zentralblatt MATH
 W. Li, A. Alfa, and Y. Zhao, “Stochastic analysis of a repairable system with three units and two repair facilities,” Microelectronics and Reliability, vol. 38, no. 4, pp. 585–595, 1998. View at: Google Scholar
 R. B. Liu, Y. H. Tang, and C. Y. Luo, “A new kind of nunit series repairable system and its reliability analysis,” Mathematica Applicata, vol. 20, no. 1, pp. 164–170, 2007 (Chinese). View at: Google Scholar
 R. B. Liu, Y. H. Tang, and B. S. Cao, “A new model for the $N$unit series repairable system and its reliability analysis,” Chinese Journal of Engineering Mathematics, vol. 25, no. 3, pp. 421–428, 2008 (Chinese). View at: Google Scholar
 L. Guo, H. Xu, C. Gao, and G. Zhu, “Stability analysis of a new kind $n$unit series repairable system,” Applied Mathematical Modelling. Simulation and Computation for Engineering and Environmental Systems, vol. 35, no. 1, pp. 202–217, 2011. View at: Publisher Site  Google Scholar
 N. Tian and Z. G. Zhang, Vacation Queueing ModelsTheory and Applications, Springer, New York, NY, USA, 2006.
 J. L. Schiff, The Laplace Transform: Theory and Applications, Springer, New York, NY, USA, 1999.
 R. E. Barlow and F. Proschan, Mathematical Theory of Reliability, John Wiley & Sons, New York, NY, USA, 1965.
 J. H. Cao and K. Cheng, Introduction to Reliability Mathematics, Higher Education Press, Beijing, China, 1986.
Copyright
Copyright © 2012 Renbin Liu and Yong Wu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.