Application of Lifetime Models in Maintenance (Case Study: Thermal Electricity Generation in Sudan)

IOSR Journal of Mathematics (IOSR-JM)
e-ISSN: 2278-5728, p-ISSN: 2319-765X. Volume 11, Issue 6 Ver. V (Nov. - Dec. 2015), PP 26-39
www.iosrjournals.org
DOI: 10.9790/5728-11652639 www.iosrjournals.org 26 | Page
Application of Lifetime Models in Maintenance (Case Study:
Thermal Electricity Generation in Sudan)
Mohammedelameen Eissa Qurashi1
*, Ahamed Mohamed Abdalla Hamdi2
1, 2
Sudan University of Science & Technology, Faculty of Science, Department of Statistics
Abstract: The main objective of this paper is that to apply lifetime models on the fault of thermal electricity
generation in Sudan to predict faults and failures during period of working and increase its lifetime to insure
electricity production sustainability and reducing maintenance cost. Lifetimes data has been taken from Bahri
Thermal Station for electricity generation, which is, belong to the National Electricity Authority in Sudan during
the period (2011-2015). Through the lifetime models estimation fault distribution, reliability, hazard rate,
availability and MTBF have been calculated for the five machines from analysis; it is clear that, fault time for
all machines follow Weibull distribution with 2-parapmeters. The machines no (3, 4 and 6) have high reliability
whereas the machines no (1 and 5) have low reliability, when we predict the reliability according to the time we
found the that the reliability decrease and hazard rate increase and there is relationship between MTBF and
reliability.
Keywords: Lifetime, Reliability, Failure, Fault rate, Hazard rate, Weibull, Maintenance, MTBF
I. Introduction:
The concept of maintenance in the past was limited to reform, which followed the machine was out of
work. The maintenance was synonymous with reform that concerned with what has been corrupted when
actually there was fault. The causes of the fault were not discovered until it repeated and took a period of time.
After the Second World War the tough focused on controlling of maintenance expenses using mathematical
models, now the practical application revealed the ineffectiveness of many of these models and they have been
changed with more advanced models.
The machines maintenance system considered the safety gateway to authenticate the electricity
generation stability; it serves as the guard who prevents the machine from the sudden faults and plays the role on
the stability and safety of the machine which it has significant impact on reduction of production and operational
costs and it result positively in the economy activity. The research problem is that, there is no stochastic models
application for the lifetime, reliability and failure to predict failures that occur for the machines and the
consequent of technical malfunctions of the machines, which made its power, run of for the consumer and it
raise production and operational costs, which reflect on economic development
The objectives of the study are to apply lifetime models on the electricity generation machines in
Sudan, study reliability and failure model, study of probability distributions, which used in the lifetime and
compare them in terms of preference.
The data of this study have been collocated for five machines with exceptional to the machine no(2)
because it did never got fault in duration of the study. The sample size has been determine according to the
method that not tided to the number of time of failure occurs condition for each machine. The technical fault
data collected from the efficiency department in the station and it was (failure time) during the period (2011-
2015). There are two types of faults; mechanical faults and the faults due to preventive maintenance, in this
study, we used the data of mechanical faults.
This study based on the following assumptions:
 Applications of lifetime on machines have a positive impact on the electricity stability.
 Fault times follows Weibull distribution.
 The electricity generating machines have a high reliability.
II. Theoretical Framework:
2.1: Reliability:
Reliability define as the probability of success or the probability that the system will perform it
intended function under specified design limits  1 .
Reliability that is more specific is the probability that a product are part will operate properly for
specified period of time (design life) under the design operating condition without feature. In other words,
reliability may be used as measure of the systems success in providing, it is function properly. Reliability is
once of quality characteristics that consumer require form the menu facture of products.

Application of Lifetime Models in Maintenance
Mathematically: reliability R(t) is the probability that a system will be successful in the interval from time 0 to
time t:
( ) ( )R t P T t  0t  ……………………(1)
Where T is a random variable denoting the time -to-failure or failure time.
Unreliability F(t), a measure of failure, is defined as the probability that the system will fail by time t:
( ) ( )F t P T t  for 0t 
In the other words, F(t) is the failure distribution function. If the time-To-failure random variable T has a density
function f(t) , then
( ) ( )
t
R t f s ds

  ……………………….(2)
or, equivalently
 ( ) ( )
d
f t R t
dt
 
The density function can be mathematically described in terms of T as:
0
lim ( )
t
p t T t t
 
   ……………………..(3)
This can be interpreted as the probability that the failure time T will occur between the operating time t and the
next interval of operation, .
Consider new and successfully tested system that operates well when put into service at time 0t  , the system
becomes less likely to remain successful as the time interval of course, is zero  1 .
2.2: Fault Rate:
The possibility of fault machine in specific time period 1t and 2t can be expressed with following non
reliability, equation 2 .
2 1 2
1
1 2( ) ( ) ( ) ( ) ( )
t t t
t
f t dt f t dt f t dt F t F t
 
     
or can be expressed with reliability
2
1 1 2
1 2( ) ( ) ( ) ( ) ( )
t
t t t
f t dt f t dt f t dt R t R t
 
     
The rate that a fault took place with in specific period of time is called as "Fault Rate" throughout the period 1t
indicates for no fault in the beginning of period and therefore the equation can be expressed as follows:
 
2
2 1 1
( ) ( )
( )
R t R t
t t R t


…………………………..(4)
It has been observed that the Fault Rate depend on time if the period 1t denoted as t t  the equation (4)
stated as follow:
( ) ( )
. ( )
R t R t t
t R t
  

…………………………(5)
and means with rate it number of faults in each unit time.
2.3: Hazard Rate:
Define as limits of rate of faults for a period of near-zero equation can be written in the form 2 :
0
( ) ( ) 1 ( )
( ) lim
. ( ) ( )t
R t R t t dR t
h t
t R t R t dt 
    
     

( )
( )
( )
f t
h t
R t
 …………………………….(6)
To find out possibility of fault machine it have age t in time period  ,t t t  written as:
( ).posf h t dt ………………………………(7)
The hazard rate refer to change in rate fault through age of machine. To find out hazard rate for the sample
machines N (machine consisting of n element), we will assume ( )sN t is random variable denotes to number
of machines working successfully at time t thus, the ( )sN t is binomial distribution.
     ( ) ( ) 1 ( )
.( )
n N n
s
N
P N t n R t R t
N N n

   

0,1,...,n N
The expected value for ( )sN t :
   ( ) . ( ) ( )sE N t N R t N t 
hence:
( ( )) ( )
( ) sE N t N t
R t
N N
  ……………………….(8)
and reliability in time t, it is arithmetic mean for rate success in t thus:
( ) ( )
( ) 1 ( ) 1
N t N N t
F t R t
N N

     ……………….(9)
and rate density fall equal:
( ) 1 ( )
( ) .
dF t dN t
F t
dt N dt
  
2.4: System Mean Time to Failure:
Suppose that the reliability function for a system is given by R(b), the expected feature time during which a
component is expected to perform success fully, or the system mean time to feature (MTTF)  1 , given by:
0
( )MTTF tf t dt

  ……………………………..(10)
substituting:
 ( ) ( )
d
f t R t
dt
 
From equation (10) and performing integration by part, we obtain:
   0
0 0
( ) ( ) ( )MTTF tdt R t tR t R t dt
 

      ………………(11)
The first term on the right hand side of above equation equals zero at both limits, since the system must fail after
a finite amount of operating time, therefore, we must have tR(t)→0 as t→∞ so equation (11) becomes:
0
( )MTTF R t dt

  ………………………………………………….(12)
Thus, MTTF is the definite integral evolution of the reliability function. In general if ( )t is defined as the
failure rate function, then by definition MTTF is not equal to .
The MTTF should be used when the feature time distribution function is specified because the reliability level
implicit by the MTTF depends on the underlying feature time distribution. Although the MTTF measure is one
of the most widely used reliability calculation, it also one of most missed calculations, it has been misinterpreted
as “guaranteed minimum life time".

2.5: Availability:
Reliability is a measure that requires system success for an entire mission time, no failure or repairs are allowed.
The availability of a system is defined as the probability that the system is successful at time t, mathematically:
system up time
Availability =
system up time+system down time
……………………..(13)
Availability is a measure of success used primarily for repairable system, for non-repairable system availability
A(t) equals reliability R(t). In repairable system A(t) will be equal to or greater then R(t).
The mean time between failures (MTBF) is an important measure in repairable system. This implies that the
system has MTBF is an expected value of the random variable time between failures mathematically.
MTBF=MTTF+MTTR ………………………….(14)
2.6: Weibull distribution Parameters Estimation:
Let 1 2, ,..., nt t t be a random sample from Weibull distribution with p.d.f
1
1 2( , ,..., , , ) t
nf t t t t e

 
    
 for 0t  …………………….(15)
The likelihood function is
1
1 2
1
( , ,..., ) i
nn n
t
n i
i
L t t t t e
 
   

 
1 2
1 1
ln ( , ,..., ) ln ln ( 1) ln
n n
n i i
i i
L t t t n n t t

   
 
     
1 1
ln
ln ln 0
n n
i i
i i
L n
t t t


   

   

 
1
ln
0
ˆ
n
i
i
L n
t

  

  


This, the maximum likelihood estimation of  and  are
1
ˆ
ln
n
i i
n
T
t



 
 
 

……………………………………….(16)
ˆ
ˆ
n
t 
  ……………………………………………(17)
III. Application Aspect:
The application aspect includes to what explained in the theoretical aspect and depending on statistical
software "STATGRAPHIC", we would describe the data , test the distribution of the data , estimate lifetime
models and comparison between machines in fault distribution, Reliability, hazard rate, availability and MTBF.
3.1: Description of Failure Times:
This study will be described for the failure time, for the five machines with some descriptive measures in order
to know the nature of study's data type.
Table (1): Rates of failure times for each machine
Machine Mean(hr) Std. (hr) 95% Confidence Interval for Mean
Lower Bound Upper Bound
Machine no(1) 6.764 9.249 9.249 10.217
Machine no(3) 6.442 9.023 9.023 9.1530
Machine no(4) 6.747 11.808 11.808 9.2630
Machine no(5) 8.528 10.719 10.719 13.545
Machine no(6) 15.451 15.174 15.174 22.552
Mean 7.720 11.374 11.374 9.2980

6.764
6.442
6.747
8.528
15.451
0
2
4
6
8
10
12
14
16
18
Machine no(1) Machine no(3) Machine no(4) Machine no(5) Machine no(6)
Machines
Failuertime(hr)
Figure (1): Rates of failure times for each machine
From above table and figure, it has shown that according to the mean values for the five machines, the
machine no(6) have the highest mean failure depending on the value of the largest mean (15.45) hours, followed
by machine no(5) depending on the value of the second largest mean (8.53) hours, followed by machine no(1)
depending on the value of the third largest mean (6.76) hours, lastly machines no(1) and (4) are (6.76) and
(6.75) respectively.
3.2: Data distribution Test:
Here we test the following hypothesis:
Ho: The failure data follow Weibull distribution
H1: The failure data not follow Weibull distribution
Table (2): Kolmogorov-Smirnov test for machines
Machine Statistic Sample Size P-value
Machine no(1) 0.23796 30 0.05595
Machine no(3) 0.13482 44 0.47132
Machine no(4) 0.13482 84 0.41859
Machine no(5) 0.27464 23 0.05035
Machine no(6) 0.24790 44 0.14390
From above table, it shows the p-value of Kolmogorov-Smirnov test of all machines is greater than
significant level (0.05) that mean the failures time data follow Weibull distribution with 2-parameters, which
means the underlying distribution of the lifetime model is Weibull
3.3: Lifetime Models for Machine no(1):
The lifetime test has been conducted for machine no(1) for a period of time (100 hours) and the following
measure has been calculated:
Table (3): Results of Lifetime test for machine no(1)
Measure Value
Distribution of fault ( )f t 0.51858
Reliability ( )R t 0.48142
Hazard rate ( )h t 1.07719
Availability ( )A t 0.98
From the table (3), it has shown that:
 The probability fault of the machine no(1) is ( 100) 0.51858f t   during (100) hours, this indicate the
probability fault of machine no(1) is very high during this period.
 The reliability for machine no(1) is weak since ( 100) 0.48142R t   , this mean that the probability
for machine to work for (100) hours without fault is (0.48).

 The rate of randomly fault occurred for machine no(1) ( 100) 1.07719h t   , this indicate that the rate
that occurred fault randomly during (100) hours is very high.
 The probability of available time to repair machine no(1) when it fault is (0.97), this indicate that the
machine has high availability.
Table (4): Life table (times) for machine no(1)
Time
Reliability ( )R t Cum.Hazard ( )h t
0 1.00000 0.0000
100 0.48142 0.7310
200 0.24286 1.4153
300 0.12457 2.0829
400 0.06457 2.7400
500 0.03373 3.3893
600 0.01773 4.0326
From the table (4), it has shown that the reliability decreases whenever the working time of the
machine increase. When the time (t=100) hours the reliability is about (48%), at time (t=200) hours the
reliability is about (24%), at time (t=300) hours the reliability (12%), at time (t=400) hours the reliability is
about (6%), at time (t=500) hours the reliability is about (3%), at time (t=600) hours the reliability is about
(2%). The hazard rate increases whenever the working time increases too.
Estimated Reliability Function
Reliabilityprobability
0 40 80 120 160 200 240
Time
0
0.2
0.4
0.6
0.8
1
Figure (2): Reliability function vs time for machine no(1)
From the figure (2), it has shown that the reliability decreases whenever the working time of the machine
increase in until equal zero.
Estimated Cumulative Hazard Function
0 40 80 120 160 200 240
Time
0
1
2
3
4
cumulativehazard
Figure (3): Cumulative hazard function vs time for machine no(1)
From the figure (3), the hazard function increases whenever the working time increases too.
3.4: Lifetime Model for Machine no(3):
The lifetime test has been conducted for machine no(3) for a period of time (100 hours) and the
following measure has been calculated:

Table (5): Results of Lifetime test for machine no(3)
Measure Value
 The probability fault of the machine no(3) is ( 100) 0.28492f t   during (100) hours, this indicate that
the probability fault of this machine is low during this period.
 The reliability for machine no(3) is ( 100) 0.71508R t   it is weak reliability, this mean that the
probability for machine to work for (100) hours without fault is (0.72), the reliability is very high.
occurred fault randomly during (100) hours is low.
 The probability of available time to repair machine no(3) when it fault is (0.97), this indicate that this
Table (6): Life table (times) for machine no(3)
Time
0 1.00000 0.0000
100 0.71508 0.3354
200 0.50892 0.6755
300 0.36153 1.0174
400 0.25653 1.3605
500 0.18186 1.7045
600 0.12883 2.0492
(13%). The hazard function increases whenever the working time increases too.
0 100 200 300 400
Time
0
0.2
0.4
0.6
0.8
1
survivalprobability
increase in until equal zero.

0 100 200 300 400
Time
0
1
2
3
4
cumulativehazard
Figure (5): Cumulative hazard function vs time for machine no(3)
The lifetime model has been conducted for machine (4) for a period of time (100 hours) and the following
Table (7): Results of Life time test for machine no(4)
Measure Value
the probability fault of this machine (4) is low during this period.
 The reliability for machine no(4) is ( 100) 0.62706R t   it is high reliability, this mean that the
that occurred fault randomly during (100) hours is middle .
 The probability of available time to repair machine no(1) when it fault is (0.99), this indicate that the
Table (8): Life Tables (Times) for machine no(4)
Time
0 1.00000 0.0000
100 0.62706 0.5947
200 0.44404 0.8118
300 0.32552 1.1223
400 0.24360 1.4122
500 0.18494 1.6877
600 0.14195 1.9523

0 100 200 300 400 500 600
Time
0
0.2
0.4
0.6
0.8
1
survivalprobability
increase.
0 100 200 300 400 500 600
Time
0
1
2
3
4
5
cumulativehazard
Figure (7): hazard function vs time for machine no(4)
The lifetime model has been conducted for machine no(5) for a period of time (100) hours and the following
Table (9): Measures of lifetime model for machine no(5)
Measure Value
the probability fault of this machine is low during this period.
 The reliability for machine no(5) is ( 100) 0.47489R t   it is high reliability, this mean that the
probability for machine to work for (100) hours without fault is (0.47), the reliability is weak.
that occurred fault randomly during (100) hours is very high.

Table (10): Lifetable (times) for machine no(5)
Time
0 1.00000 0.0000
100 0.47489 1.1058
200 0.24683 1.3991
300 0.13223 2.0232
400 0.07219 2.6285
500 0.03995 3.2202
600 0.02235 3.8011
Estimated ReliabilityFunction
0 40 80 120 160 200
Time
0
0.2
0.4
0.6
0.8
1
survivalprobability
increase.
0 40 80 120 160 200
Time
0
1
2
3
4
cumulativehazard
Figure (9): Hazard function vs time for machine no(5)
The lifetime model has been conducted for machine no(6) for a period of time (100 hours) and the following
Table (11): Measures of lifetime Model for Machine no(6)
Measure Value

the probability fault of this machine is high during this period.
 The reliability for machine no(4) is ( 100) 0.74126R t   it is weak reliability, this mean that the
that occurred fault randomly during (100) hours is very weak.
Table (12): Lifetable (times) for machine no(6)
Time
0 1.00000 0.0000
100 0.74126 1.1058
200 0.48040 0.7331
300 0.28999 1.2379
400 0.16610 1.7952
500 0.09117 2.3951
600 0.04826 3.0312
0 100 200 300 400
Time
0
0.2
0.4
0.6
0.8
1
survivalprobability
increase.

0 100 200 300 400
Time
0
0.5
1
1.5
2
2.5
3
cumulativehazard
Figure (11): Hazard function vs time for machine no(6)
3.8: Comparison between Machines:
We compare the five machines according to lifetime model, the comparison was among the following measures
probability of fault, reliability, hazard rate and availiability:
Table (13): Lifetime models comparison
( )A t( )h t( )R t( )f tmachine
0.981.077190.481420.51858Machine no(1)
0.970.398440.715080.28492Machine no(3)
0.990.594740.627060.37294Machine no(4)
0.991.105750.474890.52511Machine no(5)
0.970.349050.741260.25874Machine no(6)
0.48142
0.71508
0.62706
0.47489
0.74126
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Machine no(1) Machine no(3) Machine no(4) Machine no(5) Machine no(6)
Machines
Reliability
Figure (12): Reliability vs machines
Form above table and figure we note that ,the machines no (3,4 and 6) have high reliability and the
machines no (1 and 5) have low reliability, the machines with high reliability have a low faults probability and
hazard rate but the machines with low reliability have high faults probability and hazard rate.
Table (14): MTBF and reliability comparison
( )R tMTBFMachine
0.481426.70036Machine no(1)
0.715088.37763Machine no(3)
0.627066.18406Machine no(4)
0.474897.95355Machine no(5)
0.7412616.5139Machine no(6)

From above table , we note that whenever mean time between renewals (repairable) increseased the reliability
increased too and that appear clearly in the machinen no(6) result which its mean between renewals is
approximately (17 hours) and the reliability (0.74).
IV. Conclusions:
The main findings of this paper are:
1. Fault time of machines follows Weibull distribution with 2-parameters which means the underlying
distribution of the lifetime model is Weibull
2. When operation time of machines increase the performance decreased or the machine got fault.
3. The machines no (3, 4 and 6) have high reliability and the machines no (1 and 5) have low reliability.
4. The hazard rate of machines increase according to the time.
5. The machines with high reliability have a low faults probability and hazard rate but the machines with low
reliability have high faults probability and hazard rate.
6. 6. Whenever mean time between failures (MTBF) for machines increase that indicate the machine has
high reliability.
7. All the machines have high availability.
Acknowledgement:
I would take this opportunity to thank my research supervisor Dr. Ahamed Mohamed Abdalla Hamdi.
Special thanks to our great teacher and my idle role Also my thanks to(Dr. Bassam Younis Ibrahim Ahamed,
Head of Statistics & Research Section Strategic Planning Division Abu Dhabi,UAE) Engineer Khalid Eltahir
Abdall-Basit and my friend Mr. Mohammed Omer Musa for their support and guidance without which this
research would not have been possible.
References
1. Honag Pham, System Software Reliability, The State University of New Jersey, USA, Springer, 2006.
2. Honag Pham, Handbook of Engineering Statistics, The State University of New Jersey, USA, Springer, 2006.
3. Adamantios Mellas & Wenbiao Zhao, Modeling and Analysis of Repairable System with Gerenral, Relia Soft Corporation, 2005.
4. Van Noortwijk, J.M. & Klatter, H.E., The use of Lifetime Distributions in Bridge Maintenance and Replacement Modeling,
Computers and Structures, Vol. 82, 2004.
5. Saad Talib Hasson, The Building a Reliability model to Improve System Performance, Journal of Babylon University, Vol. 22,
2014.
6. Alicja Jokiel,Rokita,Ryszard Magiera, Selected Stochastic Models in Reliability, Wroclaw University of Technology, 2009.
7. J. A. M. van der Weide and Suyono and J. M. Van Noortwijk, Renewal Theory with Exponential and Hyperbolic Discounting,
Probability in the Engineering and Informational Sciences, 53-74, 2008.
8. Rackwitz, R., Optimizing Systematically Renewed Structures, Reliability Engineering and System Safety, Vol. 73, 2001.
9. Yanez, M., Joglar, F, & Modarres, M., Generalized Renewal Process for Analysis of Repairable systems with limited failure
experience, Reliability Engineering and System Safety, Vol. 77, 2002.
10. Savits, T. H., A Cost Relationship between Age and Block Replacement Policies, Journal of Applied Probability, Vol. 25, 1988.
11. Bo Bergman, Optimal Replacement under a General Failure Model, Advances in Applied Probability, Vol. 10, 1978.
12. Kapur, K.C. and L.R. Lamberson, Reliability in Engineering Design, John Wiley & Sons, Inc., New York, 1977.
13. Kececioglu, D., Reliability Engineering Handbook, Prentice Hall, Inc., New Jersey, 1991.
14. Kececioglu, D., Maintainability, Availability, & Operational Readiness Engineering, Volume 1, Prentice Hall PTR, New Jersey,
1995.
15. Leemis, L.M., Reliability - Probabilistic Models and Statistical Methods, Prentice Hall, Inc., Englewood Cliffs, New Jersey, 1995.
16. Lloyd, D.K. and M. Lipow, Reliability: Management, Methods, and Mathematics, Prentice Hall, Englewood Cliffs, New Jersey,
1962.
17. Mann, N.R., R.E. Schafer and N.D. Singpurwalla, Methods for Statistical Analysis of Reliability and Life Data, John Wiley & Sons,
Inc., New York, 1974.
18. Meeker, W.Q. and L.A. Escobar, Statistical Methods for Reliability Data, John Wiley & Sons, Inc., New York, 1998.
19. Mettas, A., Reliability Allocation and Optimization for Complex Systems, Proceedings of the Annual Reliability & Maintainability
Symposium, 2000.
20. Nelson, W., Applied Life Data Analysis, John Wiley & Sons, Inc., New York, 1982.
APPENDIX
Failure data for five machines during period (2011-2015)
Failure time
Machine no(6)Machine no(5)Machine no(4)Machine no(3)Machine no(1)
245.980.957680
288528244848
1.73482419224
65.87160864810081704
7.775.98120240504
35.4352824010.9848
11.85481207214.38
20.78160826424962.20

Failure time
Machine no(6)Machine no(5)Machine no(4)Machine no(3)Machine no(1)
18.251.32243368.92
27.4310.5341921.42
2.172.071202.4530.18
10.035.531683.4030.53
3.5028.803120.001.42
11.9513.681443.432.75
3.000.95240.824.50
9.2221.25962.37.77
5.421.951443.2536.47
10.331.7728814.901.05
6.477.75965.331.80
10.132.15482.027.77
4.821684.082.85
1.9245642.271.58
22.555762.773.62
41.58964.171.70
1.2216829.651.97
9.35961.774.03
16.652640.981.21
31.171923.880.88
16.274823.532.77
6.306721.673.50
3.832881.17
2.781.302.77
10.670.954.67
5.982.121.30
2.021.731.08
2.070.9517.22
2.654.730.78
1.536.277.18
1.9310.3312.77
5.4773.8310.22
2.833.931.77
2.851.3814.37
0.84.820.88
13.731.407.32
3.622.970.83
5.308.45
8.92
3.95
1.68
1.67
3.23
2.73
4.28
3.52
18.07
20.67
3.25
0.92
2.70
0.0
5.53
2.67
3.07
Source: Bahri Thermal Station, Efficiency Department, 201

Application of Lifetime Models in Maintenance (Case Study: Thermal Electricity Generation in Sudan)

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