Ibrahim Yusuf, Nafisatu M. Usman, Abdulkareem Lado Ismail „„„ . .T „

RT&A, No 3 (69)

RELIABILITY ESTIMATION OF A SERIAL SYSTEM_Volume 17, September 2022

Reliability Estimation of a Serial System Subject to General and Gumbel-Hougaard Family Copula Repair Policies

Ibrahim Yusuf

Department of Mathematical Sciences, Bayero University Kano, Nigeria iyusuf.mth@buk.edu.ng

Nafisatu Muhammad Usman

Department of Arts and Humanity, School of General Studies, Kano State Polytechnic, Kano, Nigeria mamankhairat2015@gmail.com

Abdulkareem Lado Ismail

Department of Mathematics, Kano State College of Education, Kano, Nigeria

ladgetso@gmail.com

Abstract

Abstract: The dependability analysis of a hybrid series-parallel system with five subsystems A, B, C, D, and E is the subject of this research. Subsystem A has two active parallel units, whereas subsystem B has two out of four active units. Both units have a failure and repair time that is exponential. There are two states in the system under consideration: partial failure and complete failure. To assess the system's dependability, the system's first-order partial differential equations are constructed from the system transition diagram, resolved using the supplementary variables technique, and the reliability models are Laplace transformed. Failure times are assumed to follow an exponential distribution, whereas repair times are expected to follow a general distribution and a Gumbel-Hougaard family copula distribution. Reliability measurements of testing system effectiveness are derived and investigated, including reliability, availability, MTTF, sensitivity MTTF, and cost function. Tables and graphs show some of the most relevant findings.

Keywords: Reliability, estimation, availability, MTTF, Series system, Gumbel-Hougaard family copula

1. Introduction

Series-parallel systems are made up of multiple subsystems that are connected in a series. Each subsystem is made up of units or components that are connected in a parallel fashion. When all of the subsystems of a series-parallel system are operational, the system operates. Partially or completely failing such systems is possible. When any of the parallel units or components of the subsystem failed, it resulted in a partial failure of the system. This failure will not stop the system from working; rather, depending on the configuration of the components, the system will continue to operate at full or decreased capacity (see Ram and Manglik [29] and Ram et al. [30]). When any of the subsystems fails, the system's operation comes to a halt, resulting in total failure.

The majority of industrial and manufacturing systems are set up in a series-parallel fashion. Because of their widespread use in industrial and manufacturing settings, determining the reliability, availability, and profitability of series-parallel systems as industrial and manufacturing systems has become a more pressing concern. A good example can be found in a computer network (see Yusuf et al. [42], Rawal et al. [31] and Rawal et al. [32]). Feeding, crushing, refining, steam generation, evaporation, crystallization, fertilizer plant, sugar plant crystallization unit, and piston manufacturing factory are all instances of these systems.

Some studies consider reliability block diagrams to identify the role of cascading failures on the reliability of series-parallel systems (see Xie et al [35]) or consider optimal component grouping in series-parallel and parallel-series systems composed of k subsystems to improve reliability and availability of series-parallel systems (see Xie et. al.[36]). The reliability of such a system can be improved by determining the best time for performing PM and also finding the number of spare parts and facilities in single-item replacement and parallel systems to minimize the expected average cost per unit time (see Fallahnezhad and Najafian [7]), or by performing maintenance to the system components (see Chauhan and Malik [5]), or by performing maintenance to the system components (see Fallahnezhad and Najafian [7]). (see Khatab et al.[18]).

The importance of ensuring the survival of industrial and manufacturing systems, as well as their accompanying economies, through dependability, availability, and profit optimization has become critical to their expansion. The percentage of time that the system is available to users is known as system availability. When the system's dependability and availability are improved (increased), the accompanying income is improved as well. Crystallization system of a sugar plant, uncaser system of a brewing plant, thermal plant, two-wheeler automobile, cattle feed, and ice cream making unit of a milk plant are examples of such industrial and manufacturing systems (see Aggarwal et al. [2], Garg et al. [11] and Kumar and Mudgil [21]).

Studies on reliability and availability modeling, as well as performance evaluation of industrial and manufacturing systems, have been conducted, as indicated above. To determine the reliability and availability models of such systems, the governing differential difference equations are obtained and solved with Runge-Kutta fourth-order and using genetic algorithm method to analyze the system's reliability (see Aggarwal et al. [3], Garg et al. [12], Kadiyan et al. [17]) and making decisions using the developed model (see Aggarwal et al. [3], Garg et al (see Gupta and Tewari [14] and Fadi and Sibai [6])

Maintaining a high level of system reliability, availability, manufacturing output, and revenue generating requires proper maintenance planning. As a result, it's critical that the equipment is always available. Several approaches for studying behavioral studies (see Arvind et al. [4]) and maintenance planning and problem identification (see Khanduja et al. [16] and Xu et al. [37]) have been abandoned. Reliability, availability, mean time to failure, mean time between failures, and mean time to repair are all indicators of such performance.

The degree of identification of the most critical subsystem that results in low reliability, availability, and profit between the subsystems is critical in analyzing the performance of the system (see Kumar et al. [23] and Kumar and Lata [24]) / subsystems through reliability, availability, and generated profit, as well as the degree of identification of the most critical subsystem that results in low reliability, availability, and profit between the subsystems (see Freiheit et al. [8]). The ideal profit level, at which the profit is highest, can be identified using this mathematical approach (see Kumar and Tewari [22]).

Reliability, availability, and revenue enhancement literature Consider the options by categorizing the configurations' dependability and choosing the optimum structure that maximizes system reliability (see Peng et al. [28]). Particle swarm optimization (see Garg and Sharma [13]) and the concept of inherent availability (see Kaur et al. [19]) are two further ways for increasing reliability.

Few studies consider the application of redundancy application problem as a means of a maximizing the system reliability, availability and profit (see Mohammed et al. [27] and Zhu et al. [41]), identification and elimination of the most critical component with low reliability (see Yusuf et al. [39] and Yusuf [40]), by considering general repair as a way regaining the system to its former position before complete failure (see Yusuf et al. [38]), by employing human operator to avoid catastrophic breakdown (see Gahlot et al.[9],Gulati et al. [10],Lado and Singh [25], Lado et al. [26] and Singh and Ayagi [33]) or through the study of optimization allocation problem for a repairable series-parallel system having failure dependencies among the units of the system so as to reduce the number of repair teams are available for each subsystem (see Hu et al. [15]).

Existing literatures above either ignores the importance of repair policies on reliability, availability, mean time to failure and profit on both industrial growth, employment, increase in volume of business, etc. Most literatures laid emphasis of availability and performance evaluation of the systems alone without paying much attention to the impact of copula and general repair policies on reliability, availability, mean time to failure and generated revenue.

More sophisticated models of repairable series parallel systems should be developed to assist in reducing risk of a complete breakdown, operating costs, prolonging the overall reliability, availability, mean time to failure as well as generated revenue (profit). For this reason, this paper considered a series-parallel system consisting of five subsystem A, B, C, D and E. The performance of the system is studied using the supplementary variable technique and Laplace transforms. The various measures of reliability such as availability, reliability, mean time to system failure (MTTF), sensitivity for MTTF and cost analysis have been computed for various values of failure and repair rates.

The paper is organized as follows: Section 2 captures the description of the system, assumption and notations used for the study. Section 3 deals with the formulation and solution of mathematical model. Section 4 focuses on the analytical part of the study in which some particular cases are taken for discussion. The paper is concluded in section 5.

2. Notations, Assumptions, and Description of the System

2.1. Notations

t: Time variable on a time scale.

s: Laplace transform variable for all expressions

b / b2/ b3/ / b5: Failure rate of subsystem 1/ subsystem 2/ subsystem 3/ subsystem 4

and / subsystem 5 respectively. f (y) / f (z): Repair rate of subsystem 2 / subsystem 3.

(x) / (y) / (z) / (m) / (n): Repair rate for complete failed states of subsystem 1 /

subsystem 2/ subsystem 3/ subsystem 4 and / subsystem 5 respectively. Pi(t): The probability that the system is in Si state at instants for i =0 to 11

P(s): Laplace transformation of state transition probability p(t)

Pi (x, t): The probability that a system is in state Si for ¿=1..., the system under repair and elapse repair time is (x, t) with repair variable x and time variable t

Pi (y, t): The probability that a system is in state Si for ¿=1..., the system under repair

and elapse repair time is (y, t) with repair variable y and time variable t Pi (z, t): The probability that a system is in state Si for ¿=1..., the system under repair

and elapse repair time is (z, t) with repair variable z and time variable t Pi (m, t): The probability that a system is in state Si for ¿=1., the system under repair

and elapse repair time is (m, t) with repair variable m and time variable t Pi (n, t): The probability that a system is in state Si for ¿=1., the system under repair

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and elapse repair time is (n, t) with repair variable n and time variable t Ep(t): Expected profit during the time interval [0, t)

Ki, K2: Revenue and service cost per unit time, respectively.

;U0(x): The expression of joint probability according to Gumbel-Hougaard family

copula definition

1 <d<¥ . Where

and

2.2 Assumptions

(ux (x), W2 (x)) = exp xq + {log f ( x )q}

M =f(x )

(1)

(2) (3)

i. Firstly, it is assumed that all subsystems are in perfect operational state.

ii. Failure of any unit leads to insufficient performance of the system.

iii. Secondly, subsystem 1, subsystem 4, subsystem 5, one unit from subsystem 2 and at least three units from subsystem3 are compulsory for the system to operate.

iv. Thirdly, the system will not operate if any of the subsystems completely fail.

v. If a unit of the system failed, it can be tackled when it is in operation or failed state.

vi. All failure rates are constant and assumed to follow exponential distribution.

vii. General distribution is employed to repair partially failed states while Gumbel-Hougaard family copula distribution takes care of complete failed state.

viii. The repaired unit of the system is assumed to operate like new and no harm shows in the repair process.

ix. However, as soon as the failed unit gets repaired, it is ready to take the load for successful perform of the system.

2.3 Descriptions of the System

This system consists of five different subsystems. Subsystem 1/ Subsystem 4/ Subsystem 5: One unit whose failure led to complete failure of the system. Subsystem 2: Consists of two homogeneous units working under 1-out-of-2 policy in parallel configuration. If one unit fails, the system is partially operative and failed unit is assigned for repair. The failure of the second unit will automatically result in complete failure of the system. Subsystem 3: Consisting of four homogeneous units that work under 3-out-of-4 policy in parallel configuration. System experience partial failure if one unit fails and failed unit is assigned for repair system is operative while complete failure occurred if additional unit fails. General repair method is assigned for partially failed state and completely failed state is carried out by copula repair method.

4

Figure 1: System reliability block diagram mrf*)

Figure 2: Transition diagram of the System

Ibrahim Yusuf, Nafisatu M. Usman, Abdulkareem Lado Ismail „„„ . .T „

RT&A, No 3 (69)

RELIABILITY ESTIMATION OF A SERIAL SYSTEM_Volume 17, September 2022

States Description

So: Good working state, all subsystems are adequate.

51 / S4 / S5: Completely failed states as such the system stopped working.

52 / S11: One unit is operating and the second is on standby mode, system work in

full capacity.

53 / S6: Three units are operating while the remaining unit is on standby mode, system work

in full capacity.

S7 / S9: One unit from subsystem 3 failed and is assigned for repair, system work partially. S8: Complete failed state due to the failure of second unit from subsystem 2. S10: Complete failed state due to the failure of second unit from subsystem 3.

3. Reliability Model Formulation

The resulting sets of partial differential equations are obtained through the transition diagram of the

Mathematical model, by observing at probability of deliberations and connection of impacts.

) ¥ ¥ ¥

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— + 2bi + 2b + 4b + b + b ) Po (t)= Jf(y) P2 (y, t)dy + Jf(z) P3 (z, t)dz + J^ (*) Pi (*, tytx-

0 0 0 0

¥ ¥ ¥ ¥

j U0 (y) P8 (y, t )dy + j U0 (z) P10 (zt )dz + j U0 (m) P4 (mt )dm + j U0 (n) P5 (n, t d (4)

J+|(x) ] mx' )=0

(5)

0 0 1

-+—+b + b+4b+b+b? +f(y) jP2 (y, t ) = 0 (6) + £ + b + 2b + 3b + b + b + f(z(z,t) = 0 (7)

f + -t ^ (m)^ ^ (m, ') =0

(8)

'00 I

ft + On + U0 (n) I ^ (' ) = 0

(9)

| + |- + 3b3 + f( Z) 1 p6 ( z, t ) = 0

(10)

[it+ iz+2b 2) ) P7 (z, t ) = 0

J* + ^ ^ ( *)^ * (y, ' ) = 0

(11) (12)

0

0

vi + ^ + ^ Z ) Ö ^ ( Z' ' )= 0 Jt + £ + Mo ( z ) J Pio ( z, t ) = 0

5 5

A

- + - + & y)Jpu (yi) = 0

3.1 Boundary and Initial Conditions

p ( 0, i ) = # p0 (i)

p2 (0, i) = 2bp0 (i) p3 ( 0, i ) = 4ß3 p0 ( 0, i) p4 (0, i) = b (p0 (i)+p2 (0, t)+p3 (0, i)) p5 (0, i) = b (p0 (i)+p2 (0, i)+p3 (0, i)) p6 (0, t) = 4£p2 (0, t) p7 (0, t ) = (0, t) A (0, t ) = b (A (0, t) + pn (0, t))

p9 (0, t) = 3ß3P3 (0, t) pn (0, t) = 2ß3 (P7 (0, t) + P9 (0, t)) pn ( 0, t ) = 2ß2 P3 ( 0, t)

All state transition probabilities are zero whenever t = 0 except p0 (0) = 1.

3.2 Solution of Reliability Model

Captivating Laplace transformation of the equations (1) to (25) with the support of boundary

conditions the following equations are obtained.

¥ ¥ ¥

( S + ßi + 2ß + 4ß3 + ß4 + ß5 ) P 0 (s) = 1 + Jf ( y) P 2 ( y, s ) dy + J f ( z ) P 3 ( z, S ) dz + J Mo (X) Pi (X, S) dx +

0 0 0

¥ ¥ ¥ ¥

J Mo (y) P8 (^ s) dy + J Mo (z) Pi0 (Z s) dz + J Mo (m ) P4 (m>s) dm + J Mo (n) Pi (n> s) dn (26)

0 0 0 0

' a "

(13)

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(14)

(15)

(14)

(15)

(16)

(17)

(18) (20) (21) (22)

(23)

(24)

(25)

^ + ~dx + ßo ( x) IP i ( x, 5 ) = 0

(27)

i d ö 5+—+ß + ß2+4b3 + b + bi +f(y) k(y,s) = 0 (28)

V

dy

5+|-+b + 2b + 3b+b+b + f( z ) ( zs ) = 0

(29)

Boundary conditions

^ + ~lm +Mo (m)J P ^ ^ = 0

(30)

^ + ln (") J P5 (n' ^ ) = 0

(31)

5 + £ + 3b + f( z ) j p 6 ( z, s ) = 0

(32)

5 + £ + 2b + f( z ) j p 7 ( z, s ) = 0

(33)

V

5 V

5 + 1T + A, (y) P8 (y, 5) = 0 fy 0

(34)

^ + £ + 2b + f( z ) j p 9 ( z, s ) = 0

(35)

5 + JZ + U0 ( z ) ) P10 ( z ' 5 ) = 0

(36)

d

^

5 + + Pi +f(y) Pii (y,s) = 0

. ^ 0

(37)

Pi (0 5) = b P0 (5) (38)

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Pi (0, i ) = 2b p0 (i ) (39)

p3 (0, *) = 4b p0 (*) (40)

P4 (0,5) = b (p0 (0) + p2 (0, s) + p3 (0, *)) (41)

P5 (0,5) = b (p (0) + Pi (0, i ) + P3 (0, *)) (42)

p6 (0,5) = 4b p2 (0, i ) (43)

P7 ( 0,5 ) = 3b p ( 0, i ) (44)

p ( 0,5 ) = b ( P ( 0, i )+Pii( 0, i )) (45)

P ( 0,5 ) = 3 b p ( 0, * ) (46)

Pi0 (0, 5) = 2b (p7 (0, *) + p9 (0, *)) (47)

pu (0,5) = 2b P3 (0,5)

(49)

Outlining of equation (3.24) - (3.35) with the support of equation (3.36) to (3.46) the following result is obtained.

Po (* ) =

Pi (5) =

1

D ( * )

b J1 - 5„ (5)

D ( 5 )

(50)

(51)

P3 ( s ) =

- (s ) =b 11 - s, (s+b+b+4b+b + b)'

D(s) [ s + Pi + b + 4b + b + b r

4b fi -S, (s + b + 2bi + 3b + b + bs)

P 4 (S ) =

D (s )| s + b + 2b + 3b + b + b b + 2bb + 4bb

^ ^ 1 - ^ (s)

Ps(S)=

P6 (5 )=

P7 (5) =

P8 (S) =

P9 (s) =

D(S)

b + 2bb + 4 b4b5

D(s)

8bb i1 -5, (5+3b)

D (5 ) 1 5 + 3b3

24P2PI J1 -(5+2b)

1 - S,0 (s)

D (5) | 5 + 2P3

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' 2b: + 8b:2 b H1 - s ,0 (s)

v D(s)

12P32 f 1 - s f (s + 2b )

D (s ) 1 s + 2P3

P10 (s ) =

P11 ( s ) =

' 48b b3 + 24b3 If1 - s ,0 (s)

v D(s) J{

8P2P3 i1 -s, (s+b)

D ( s )

s + P2

(53)

(54)

(55)

(56)

(57)

(58)

(59)

(60)

(61)

Where D(s) is well-defined as

D ( 5 ) =

5 + b + 2b + 4b + b + b -

2b (5+b + b + 4b3 + b + b) +

4b 5, (5+2b + 2b + 3b+b4+b)+

b + ( 2b2 + 8b2b ) + (48bb3 + 24b3) +

(b + 2bb4 + 4b3b4 ) + (b + 2b2b5 + 4b4b5 ). (60)

If all Laplace transformations of the state transition probabilities that the system is operating is added together the result is obtained as:

Pup (*) = [p<> (5) + p2 (5) + P3 (*) + P6 (*) + P7 (*) + P9 (*) + P11 (*)]

( 5 )

(63)

Therefore,

Pp (s )=

D (s)

f

1 + 2b2

4b

1 - s, (s + b1 + b2 + 4b + b + bs)

s+b1 + b2 + 4b3 + b4 + bs

1 - s, (s + b + 2b2 + 3b3 + b4 + bs)'

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A

s+b + 2b2 + 3b3 + b4 + bs

8b2b3

+12b2

A

1 - s, (s + 3b ) s + 3b

1 - s, ( s + 2b3)

+ 24b2b

2

2 b

+ 8bb

- s, (s + 2b3)

s + 2b3

3

1 - s, ( s + b2 )

s + b

(64)

(65)

s + 2 b3

On the other hand, the sum of all Laplace transformations of the state probabilities that the system fails can be summarized in the equation below;

Pdown (S) = 1 - Pup (S) )

4. Analytical study of the model for particular cases

4.1 Availability analysis of the model for copula repair method Supposing

exp[ + {logf( x)}q]1/q

S,o(s) = S

exp[ xq+{log f ( x)}q]

1/q(s) =

s + exp[ xq + {logf (x)}q]

q 1/q

Sf(s) =

f

s + f

(66)

(67)

and considering the same values of failure rates as b = b2 = b = b4 = bs = 0.02 , f = ^ = x = y = z = m = n = 1 and repair rates as f (y) = f (z) = 1 in equation (62), and carrying inverse Laplace transform, the expression obtained is availability function.

-0.002s31e-lM000t + 0.02s983e~2 79218i

pup (5) = i -0.019633e~1 25934i + 0.999411e -0.001770e- 02000i - 0.0014s9e

0.00677i -0.06000i

(68)

Supposing different values of time variable t = 0, 1.. .10, units of time in equation (68), availability is computed (Table 1).

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1

Table 1: Availability computation using copula repair method

Time 0 1 2 3 4 5 6 7 8 9 10

Availability 1.0000 0.9866 0.9837 0.9761 0.9724 0.9660 0.9595 0.9531 0.9467 0.9403 0.9339

12

Figure 3: Availability against time when copula repair method is used

4.2 Availability analysis of the model for general repair method

O / \ ^

Supposing, oAs) =-,and considering the same values of failure rates

S+(/)

= P2 = pi = b = b =0.02 ,0 = jU = X = y = z = m = n = land repair rates (f*{y) = = 1 in equation (62), and carrying inverse Laplace transform, the expression obtained is availability function.

as

as

P«p(s) =

-0.03 6975e_1 0400i:" +0.001825e~1'02000i +0.008761^ 29593' + 0.068653g-1-03754' +0.961175e-0 00651' -0.003440e-° 06001"

(69)

Allowing different values of time variable t = 0, 1...10, units of time in equation (69), availability is computed (Table 2).

Table 2: Availability computation using general rqrnir method

Time 0 1 2 3 4 5 6 7 8 9 10

Availability 1.0000 0.9680 0.9532 0.9441 0.9369 0.9305 0.9243 0.9183 0.9123 0.9064 0.9005

Figure 4: Availability against time when general repair method is used

0123456789 10 Time

■ Copula Repair ■ General Repair Figure 5: Variation of Availability with to time under different repair policies

4.3 Reliability Analysis of the model

Allowing all repair rates ju0 in equation (62) to zero, considering the values of

failure rates as j3x = /?9 = jB3 = jB4 = /?5 = 0.02 and captivating inverse Laplace transformation, the expression follows is reliability function.

_ i-5.082323e-°-18000' + 6e-° 16000t + 0.026666^°06000'

^ " {+0.03 5657e-004000' + 0.020000^°02000' Taking different values of time variable t = 0, 1...10, units of time in equation (70), reliability is computed (Table 3).

Table 3: Computation of reliability with respect to time

Time 0 1 2 3 4 5 6 7 8 9 10

Reliability 1.0000 0.9467 0.8868 0.8237 0.7597 0.6967 0.6358 0.5779 0.5235 0.4729 0.4261

1,2 1 0,8

>

I °'6

a> cc

0,4 0,2 0

Time

Figure 6: Reliability function against time variable

4.4 MTTF Analysis of the model

Letting all repairs to zero in equation (62) and the limit as s becoming close to zero, MTTF expression is obtained as:

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MTTF = X\mp (5) =

s-»0 p ^ '

1

^ + 12/?2/?3+14/?3

(71)

Assuming = P2 = /?3 = /?4 = P5 = 0.02 and varying /?l3/32,/33, /34,P5 one by one respectively as 0.01, 0.02...0.09 in equation (67), MTTF of the model is calculated with respect to failure rate (Table

4)-

Failure rates MTTF ft MTTF p2 MTTF J33 MTTF /34 MTTF p}

0.01 12.5772 12.5864 13.1197 12.5772 12.5772

0.02 11.6007 11.6007 11.6007 11.6007 11.6007

0.03 10.7579 10.8229 10.5096 10.7579 10.7579

0.04 10.0240 10.1850 9.6802 10.0240 10.0240

0.05 9.3795 9.6470 9.0273 9.3795 9.3795

0.06 8.8096 9.1835 8.4996 8.8096 8.8096

0.07 8.3024 8.7779 8.0643 8.3024 8.3024

0.08 7.8482 8.4184 7.6993 7.8482 7.8482

0.09 7.4394 8.0964 7.3887 7.4394 7.4394

14 12 10 8 6 4 2 0

10

12

Failure rate

Figure 7: MTTF against Failure rates

4.5 Sensitivity Analysis of the model

Taking partial differential of the MTTF with respect to failure rates yield sensitivity of the model, there and then considering the failure rates as, Px = /?9 = /?3 = /?4 = P5 = 0.02 in the partial differential function of the MTTF gives result as presented in Table 5.

Table 5: Computation of sensitivity with respect to failure rate

Failure rate d(MTTF) d(MTTF) d(MTTF) d(MTTF) d(MTTF)

A A Pi P4 &

0.01 -105.3564 -112.4063 -183.4378 -105.3564 -105.3564

0.02 -90.4902 -86.7073 -126.5997 -90.4902 -90.4902

0.03 -78.4748 -69.9443 -94.1730 -78.4748 -78.4748

0.04 -68.6385 -58.2793 -73.0555 -68.6385 -68.6385

0.05 -60.4937 -49.7372 -58.3667 -60.4937 -60.4937

0.06 -53.6804 -43.2241 -47.7029 -53.6804 -53.6804

0.07 -47.9283 -38.0960 -39.7100 -47.9283 -47.9283

0.08 -43.0317 -33.9527 -33.5643 -43.0317 -43.0317

0.09 -38.8316 -30.5346 -28.7380 -38.8316 -38.8316

-200

Failure rate

Figure 8: Sensitivity against failure rates

4.6 Profit Analysis of the model

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4.6.1 Profit analysis using copula repair method:

Cost or profit investigation which is known as expected profit is done by integrating the p (/) of the

system, then multiplying the result by revenue per unit time (ki) and eventually subtracting service cost per unit time (k?) the relation that follows summaries the saying.

Ep{t) = Kl\Pup{t)dt-K2t

(72)

Captivating fixed values of parameters of equation (62), equation (69) just arrive as

'0.002434e-1 04000' - 0.009305e-179218' + 0.015590e 1 25934' -«147.575111 kf000677' + 0.001735e 1 02000' + 0.001376e 1 06000' 147.5632

"MO

(73)

Assuming Ki= land K2= 0.1, 0.2..., 0.5, respectively and changing t = 0, 1, 2...10, units of time, the expected profit computations are done in the subsequent (Table 6).

Table 6: Computation of profit within the limit (0, t], when copula repair is used

Time

K2=0.1 Ki=0.2 K2=0.3 Ki=0.4 K2=0.5

0 0 0 0 0 0

1 0.8899 0.7899 0.6899 0.5899 0.4899

2 1.7753 1.5753 1.3753 1.1753 0.9753

3 2.6566 2.3566 2.0566 1.7566 1.4566

4 3.5322 3.1322 2.7322 2.3322 1.9322

5 4.4015 3.9015 3.4015 2.9015 2.4015

6 5.2643 4.6643 4.0643 3.4643 2.8643

7 6.1207 5.4207 4.7207 4.0207 3.3207

8 6.9706 6.1706 5.3706 4.5706 3.7706

9 7.8141 6.9141 6.0141 5.1141 4.2141

10 8.6513 7.6513 6.6513 5.6513 4.6513

o

Q.

K2=0.1 -■- K2=0.2 K2=0.3 -*— K2=0.4 K2=0.5

Time

Figure 9: Profit against time when copula repair method is used

4.6.2 Profit inquiry using general repair method:

Ep{t) = Kl\pup{t)dt-K2t

0

Captivating fixed values of parameters of equation (62), equation (71) just arrive as

0.035553e-1 04000' - 0.001789e1 02000' -0.006760e-1 29593' -0.066169e-1 03754' -147.527359e-000651' + 0.003246e-1 06000' ■ 147.5632

-k2(t)

(74)

(75)

Assuming Ki= land K2= 0.1, 0.2..., 0.5, respectively and changing t = 0, 1, 2...10. Units of time, the expected profit computations are done in Table 7.

Table 7: Computation of profit within the limit (0, t], when general repair is used

Time

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K2=0.1 Ki=0.2 K2=0.3 Ki=0.4 K2=0.5

0 0 0 0 0 0

1 0.8816 0.7816 0.6816 0.5816 0.4816

2 1.7415 1.5415 1.3415 1.1415 0.9415

3 2.5899 2.2899 1.9899 1.6999 1.3899

4 3.4303 3.0303 2.6303 2.2303 1.8303

5 4.2641 3.7641 3.2641 2.7641 2.2641

6 5.0915 4.4915 3.8915 3.2915 2.6915

7 5.9129 5.2129 4.5129 3.8129 3.1129

8 6.7282 5.9282 5.1282 4.3282 3.5282

9 7.5376 6.6376 5.7376 4.8376 3.9376

10 8.3411 7.3411 6.3411 5.3411 4.3411

9 8 7 6

£ 5 o

£ 4

3 2 1 0

Time

Figure 10: Profit against time when general repair method is used

5. Discussion and Concluding Remark

Table 1 and Figure 3 depicted the availability variation with respect of time. From Figure 3 it is clear that as failure rates increases than availability deceases when copula repair policy is employed. Similar observation can be Table 2 and Figure 4 presents the availability of system when the repair follows general distribution.

Table 3 and Figure 6 presents variation of reliability with respect to time. The reliability of system decreases with time when the failure rates increases. From Table 3 and Figure 6, it is enough to conclude that reliability has lower values in comparison with availability values in Tables 1 and 2.

Table 4 and Figure 7 depicts the mean-time-to-failure (MTTF) of the system with respect to variation in failure rates Px, /?9, /?3, /?4 and /?5 respectively when other parameters are kept constant. The variation in MTTF corresponding to Px, /?3, /?4 and P5 are slightly closer. This analysis suggests that the failure rates Px, , /?3, /?4 and P5 are more responsible for successful operation of the system. Table 6 and Figure 8 gives the information of the sensitivity analysis studied in this

K2=0.1 K2=0.2 K2=0.3 -X- K2=0.4 K2=0.5

10

12

Ibrahim Yusuf, Nafisatu M. Usman, Abdulkareem Lado Ismail „„„ . .T „

RT&A, No 3 (69)

RELIABILITY ESTIMATION OF A SERIAL SYSTEM_Volume 17, September 2022

work. Table 5 and Figure 8 gives the information of the sensitivity analysis studied in section 6.5 of this work.

Table 6 and Figure 9 and Table 7 and Figure 10 depicted the profit variation with respect of time via two types of repair employing copula repair approach and general repair. From Table 6 and Figure 9 it is clear that as failure rates increases than profit deceases when copula repair policy is employed. Similarly, Table 7 and Figure 10 presents the profit of system when the repair follows general distribution. It is clear that as failure rates increases than profit deceases when general repair policy is employed.

Comparative analysis of copula and general repair in Table 1 and Table 2 are presented in Figure 5 which compare the two results of availability with respect to time under copula and general repair. It is evident from Table 1 and Table 2 and Figure 5 that availability of the system is better when the repair follows copula distribution.

Comparative analysis of copula and general repair in Table 6 and Table 7 which compare the results of profit with respect to time under copula and general repair when K2= 0.5,04,0.3,0.2,0.1. It is evident from Table 6 and Table 7 when comparing the two procedures, repair policy of general and copula distributions, it appears that the predicted profit is larger when the repair policy is follows by copula distribution and lower when the repair follows general distribution. In both circumstances, the predicted profit is highest when the service cost is lowest and lowest when the service cost is highest. In conclusion, copula repair method yields better result, compared to general repair method; therefore, copula repair method is recommended for effective performance of the system.

References

[1] Abdelfattah Mustafa, A. (2017). Improving the Reliability of a Series-Parallel System Using Modified Weibull Distribution, International Mathematical Forum,12(6), 257 269 https://doi.org/10.12988/imf.2017.611155

[2] Aggarwal A. K, Kumar S, Singh V, Garg TK, 2014. Markov modelling and reliability analysis of urea synthesis system of a fertilizer plant. Journal of Industrial Engineering International, 11, 114, DOI 10. 1007/s40092-014-0091-5.

[3] Aggarwal, A. K.,Kumar, S. and Singh, V, 2017. Mathematical modeling and fuzzy availability analysis for serial processes in the crystallization system of a sugar plant, Journal of Industrial Engineering International, 13:47-58, DOI 10.1007/s40092-016-0166-6.

[4] Arvind K Lal, A. K., Manwinder Kaur M and Lata, S. (2013). Behavioral study of piston manufacturing plant through stochastic models, Journal of Industrial Engineering International, 9(24), 1-10. http: //www .jiei-tsb.com/content/9/1 /24

[5] Chauhan, S.K and S.C. Malik. (2016). Reliability Evaluation of Series-Parallel and Parallel-Series Systems for Arbitrary Values of the Parameters, International Journal of Statistics and Reliability Engineering Vol. 3(1), pp. 10-19.

[6] Fadi N. Sibai, F.N. (2014). Modelling and output power evaluation of series parallel photovoltaic modules, International Journal of Advanced Computer Science and Applications,5(1),129-136.

[7] Fallahnezhad, M. S. and Najafian, E. (2016): A model of preventive maintenance for parallel, series, and single-item replacement systems based on statistical analysis, Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2016.1183781

*i*Не можете найти то, что вам нужно? Попробуйте сервис подбора литературы.

[8] Freiheit, T., Shpitalni, M and Hu, S.J, 2004. Productivity of paced parallel-serial manufacturing lines with and without crossover, Journal of Manufacturing Science and Engineering, 126, 361-367.

[9] Gahlot, M., Singh, V.V., Ayagi, H.I. and Goel, C.K. (2018) 'Performance assessment of repairable system in series configuration under different types of failure and repair policies using copula linguistics', International Journal of Reliability and Safety, Vol. 12, No. 4, pp.348-374.

[10] Gulati, J., Singh, V.V., Rawal, D.K. and Goel, C.K. (2016) 'Performance analysis of complex system in series configuration under different failure and repair discipline using copula', International Journal of Reliability, Quality and Safety Engineering, Vol. 23, No. 2, pp.812-832.

[11] Garg D, Kumar K, and Singh J., 2010a. Availability analysis of a cattle feed plant using matrix method. Int J Eng 3(2):201-219

[12] Garg S, Singh J and Singh D. V., 2010b. Availability analysis of crankcase manufacturing in a two-wheeler automobile industry. Appl Math Model 34:1672-1683

[13] Garg, H and Sharma S.P., 2011. Multi-objective optimization of crystallization unit in a fertilizer plant using particle swarm optimization. Int J Appl Sci Eng 9(4):261-276

[14] Gupta S, and Tewari P.C., 2011. Simulation modeling in a availability thermal power plant. J Eng Sci Technol Rev 4(2):110-117.

[15] Hu, L., Yue, D and Li, J., 2012. Availability analysis and design optimization for repairable series-parallel system with failure dependencies, International Journal of Innovative Computing, Information and Control, 8(10A), 6693-6705.

[16] Khanduja R, Tewari PC, Kumar D., 2012. Steady state behaviour and maintenance planning of bleaching system in a paper plant. Int J Ind Eng 7(12):39-44

[17] Kadiyan S, Garg RK, Gautam R., 2012. Reliability and availability analysis of uncaser system in a brewery plant. Int J Res Mech Eng Technol 2(2):7-11.

[18] Khatab, A, Aghezzaf, E, Diallo, C and Djelloul, I. (2017). Selective maintenance optimization for series-parallel systems alternating missions and scheduled breaks with stochastic durations, International Journal of Production Research, 55:10, 3008-3024. DOI: 10.1080/00207543.2017.1290295

[19] Kaur, M., Lal, A.K., Bhatia, S.S and Reddy, A.S., 2013c. On use of corrective maintenance data for performance analysis of preventively maintained textile industry, Journal of Reliability and Statistical Studies, 6(2), 51-63.

[20] Kumar, A., Saini, M. and Malik, S. C., 2014. Stochastic modelling of a concrete mixture plant with preventive maintenance, Application and Applied Mathematics, 9(1): 13-27.

[21] Kumar V and Mudgil V., 2014. Availability optimization of ice cream making unit of milk plant using genetic algorithm, IntManag Bus Stu, 4(3), 17-19.

[22] Kumar S, and Tewari PC., 2011. Mathematical modelling and performance optimization of CO2 cooling system of a fertilizer plant. Int J IndustEng Comp, 2, 689-698

[23] Kumar R, Sharma AK, Tewari PC., 2011. Performance modelling of furnace draft air cycle in a thermal plant. Int J EngSci Tech, 3(8), 6792-6798.

[24] Kumar, A and Lata, S., 2012. Reliability evaluation of condensate system using fuzzy Markov Model, Annals of Fuzzy Mathematics and Informatics, 4(2), 281-291.

[25] Lado, A.K. and Singh, V.V. (2019) 'Cost assessment of complex repairable system consisting of two subsystems in the series configuration using Gumbel Hougaard family copula'. International Journal of Quality Reliability and Management, Vol. 36, No. 10, pp.1683-1698.

[26] Lado, A.K., Singh, V.V., Kabiru, H.I. and Yusuf, I. (2018) 'Performance and cost assessment of repairable complex system with two subsystems connected in series configuration', International Journal Reliability and Applications, Vol. 19, No. 1, pp.27-42.

[27] Mohammadi, M, Mortazavi, S.M and Karbasian, M.(2018). Developing a Method for Reliability Allocation of Series-Parallel Systems by Considering Common Cause Failure, International Journal of Industrial Engineering & Production Research, 29(2),213 - 230

[28] Peng, R, Zhai,Q, Xing, L and Yang,J. (2016) Reliability analysis and optimal structure of series-parallel phased-mission systems subject to fault-level coverage, IIE Transactions, 48:8, 736746, DOI: 10.1080/0740817X.2016.1146424

[29] Ram, M and Manglik, M., 2016. An analysis to multi-state manufacturing system with common cause failure and waiting repair strategy, Cogent Engineering 3: 1266185, 1-20, http://dx.doi.org/10.1080/23311916.2016.1266185

[30] Ram, M., Singh, S.B. and Singh, V.V. (2013) 'Stochastic analysis of a standby system with waiting repair strategy', IEEE Transactions on System, Man, and Cybernetics System, Vol. 43, No. 3, pp.698-707.

[31] Rawal, D.K., Ram, M. and Singh, V.V. (2014) 'Modeling and availability analysis of internet data center with various maintenance policies', International Journal of Engineering, IJE Transactions A: Basics, Vol. 27, No. 4, pp.599-608.

[32] Rawal, D.K., Ram, M. and Singh, VV. (2015) 'Study of reliability measures of a local area network via copula linguistics approach', International J. of Quality Reliability and Management, Vol. 32, No. 1, pp.97-111.

[33] Singh, V.V. and Ayagi, H.I. (2018) 'Stochastic analysis of a complex system under preemptive resume repair policy using Gumbel-Hougaard family copula', International Journal of Mathematics in Operation Research, Vol. 12, No. 2, pp.273-291.

[34] Tewari PC, Khaduja R, and Gupta, M., 2012 Performance enhancement for crystallization unit of a sugar plant using genetic algorithm technique. J Ind Eng Int 8(1):1-6

[35] Joachims J. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms, Kluwer, 2002.

[36] Xie,L., Lundteigen, M.A and Liu, Y. (2020).Reliability and barrier assessment of seriesparallel systems subject to cascading failures, https://doi.org/10.1177/1748006X19899235

[37] Xie, L., Wei, Y and Li, P. (2019) .On optimal heterogeneous components grouping in series-parallel and parallel-series systems, Probability in the Engineering and Informational Sciences, Volume 33, Issue 4, 564-578. https://doi.org/10.1017/S0269964818000499

[38] Xu,Q,-Z, Guo,L, -M, Shi,H,-P and Wang, N. (2016). Selective maintenance problem for series-parallel system under economic dependence, Defence Technology, 12(5), 388-400. https://doi.org/10.1016/j.dt.2016.04.004

[39] Yusuf, I., Lado, A.K. and Ali, U.A. (2020) 'Reliability analysis of communication network system with redundant relay station under partial and complete failure', J. Math. Comput. Sci., Vol. 10, No. 4, pp.863-880

[40] Yusuf, I., Sani, B and Yusuf, B. (2019). Profit analysis of a series-parallel system under partial and complete failures, Journal of Applied Sciences, 19(6),565-574.

[41] Yusuf, I., 2014. Comparative analysis of profit between three dissimilar repairable redundant systems using supporting external device for operation, Journal of Industrial Engineering International,10:77, 1-9, DOI 10.1007/s40092-014-0077-3

[42] Yusuf, I., Maihulla, A. S and Bala, S. I. (2021). Reliability and Performance Analysis of a Series-Parallel System using Gumbel-Hougaard Family Copula. Journal of Computational and Cognitive Engineering, 01-10. https://doi.org/10.47852/bonviewJCCE2022010101

[43] Zhu, S, Xiang, Y and Coit, D. (2018).Redundancy Allocation for Serial-Parallel System Considering Heterogeneity of Components, ASME 2018 13th International Manufacturing Science and Engineering Conference, https://doi.org/10.1115/MSEC2018-6481