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Implementing a reliability-centered maintenance model for a power

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Conference Paper · April 2015

DOI: 10.1109/IEOM.2015.7093799

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 Implementing a Reliability-Centered Maintenance
Model for a Power Distribution System: A Case
Study in Saudi Arabia

Alshahrani, Salem Dr. A Hadidi, Laith

Engineering Department Construction Engineering & Management
National Guard, Health Affair King Fahd University of Petroleum & Minerals
Al-Ahsa, Saudi Arabia Dhahran, Saudi Arabia
G200083570@kfupm.edu.sa Lhadidi@kfupm.edu.sa

Abstract—Reliability-centered maintenance (RCM) has been desired ones so as to set necessary maintenance tactics. A set
applied to different industries with noticeable results, but there is of possible maintenance tactics corresponding to critical
still no full analytical model geared to power system sector. The equipment go through a cost-benefit assessment to prioritize
referenced model presents a comprehensive model tailored to them beforehand. These tactics are applied in an iterative
power system. This paper puts the model into an application of a
manner till the desired reliability indices are achieved. Last
real case study conducted in Saudi Arabia, where the proposed
procedure is elaborately implemented. The data were gathered stage is about documentation and filling of the previous stages
from available resources along with realistic technical for future considerations.
assumptions. The practical considerations are included, and the A case study is presented for a power distribution system in
results are discussed. Saudi Arabia. It was found that the type of equipment is a
major factor to label equipment as critical. It also addresses the
Keywords—Maintenance management; reliability-centered importance of utilizing statistical surveys to have a successful
maintenance (RCM); power distribution system; maintenance RCM model implementation when historical records of
management program. components are missing. The power system configuration and
the industry in which the system functions are major influences
I. INTRODUCTION of the reliability indices that vary accordingly. Generally, some
electric equipment types are more critical than others, although
Interests in reliability-centered maintenance RCM in different factors affect this criticality, like voltage level and
electrical engineering arose in the 1980s in the wake of its
successful deployment in the aircraft and aerospace industry in environmental factors. The detailed steps for the associated
the 1960s. More achievements in RCM applications have been algorithm are found in [16]. This paper implements the
reported in the nuclear industry, chemical industry, and referenced model in a power distribution system in Saudi
process/oil and gas. From there, the application of RCM was Arabia at the National Guard (NG) facilities in Al-Ahsa.
expanded to power delivery, starting first with the substation
area before further expansion into the distribution systems area. II. CASE STUDY
There is now ample experience in the distribution area, and,
like the other areas, it has shown potential for cost savings and A. System Description
reliability enhancement. Distribution faces a different set of The NG power system spans the entire Ahsa site, and
challenges; with less active equipment, and a far greater serves several customer categories, namely- utility, residential,
amount of equipment, the approach to RCM application and commercial. The utility sector comprises Sewage
distribution varies from substations, yet focuses on the same Treatment Plant (STP), Chiller Plant, and Domestic Water
maintenance optimization objectives. [13, 14, 15] Tank. The residential sector, on the other hand, is composed of
The referenced model [16] comprises three stages through College of Nursing, College of Applied Medical Science,
which the whole power system reliability indices are Administrative Division, and housing area. Lastly, the
numerically assessed. The first stage is about gathering commercial sector includes Recreational Building encompasses
necessary data on which system reliability is based, including different shops. The proposed algorithm of RCM application
single-line diagram, equipment types, critical system to power system is to be applied to the NG power distribution
segments, system reliability targets, etc. The second stage, on system as shown subsequently.
the other hand, is the main-analysis stage whereby critical The voltage level comes from Saudi Electricity Company
equipment types are identified along with their associated (SEC) at a 115 kV level to be transformed into a 13.8 kV at
failure modes and failure rates. After that, the entire power SEC side. The NG substation receives the 13.8 kV and
system reliability indices are computed and compared to the transformer it into different low voltage levels for different

978-1-4799-6065-1/15/$31.00 ©2015 IEEE

applications, including 400 V, 460 V, etc. This case study
focuses only on the medium voltage system and the low
voltage components that are connected to the medium voltage
premises; all other low voltage equipment types are excluded.

Figure 2: Reliability block diagram of load1

3. System boundary identification:

The system under focus is the medium voltage
network whose voltage rate is 13.8 kV, which forms
Figure 1: NG system the system boundary being studied. This medium
voltage network consists of vacuum-type
switchgears, package transformers, bus bars, and
B. Pre-Analysis
cables. The standby medium voltage system (i.e.
As per [16], the Pre-Analysis stage covers the necessary diesel generator switchgear) is excluded since it not
requirements of the algorithm, and it consists of the following: part of the normal system. Also, all low voltage
1. System single-line diagram: equipment types are not considered for this study,
It is shown in figure 1. including switchgears, switchboards, panel boards,
motor control centers, etc. Nevertheless, the low
2. Fulfilling data requirements: voltage components interconnecting the medium
All of the required reliability data for load points and voltage system to the low voltage are considered for
different equipment types were prepared. Moreover, the study. The system configuration is open loop with
the economic implications of different preventive and two incomers interconnected by a bus-tie breaker.
corrective maintenance tactics were provided. This The open-loop layout states that only one incomer
data were obtained from relevant references, expert breaker must be closed at a time along with the tie
and specialist assumptions, and statistical surveying. breaker so as to have all loads powered. In other
words, the switchgear has an on duty incomer breaker
and a stand-by incomer breaker, and RCM states that
a system with redundancy should employ a run-to-
failure maintenance tactic provided that the faulty
equipment would be rectified during the running time
of the stand-by equipment (i.e. redundancy shall be
removed as per RCM) [18]. Therefore, only one
incomer is considered for this study, as the second
incomer is a stand-by that is part of the RCM.
Moreover, it is not uncommon to assume that the
 incomers’ breakers are fully reliable since they are Table 1: Reliability indices target
designed well to take the required load. Reliability Indices λLoad1, Des. ULoad1, Des.
The system has three main load points: load 1, load 2, (Occ./yr.) (Hr./yr.)
and load 3. Load 3 feeds the housing area of the Target Value 0.80 1.5
campus, while load 1 and load 2 feed the utility as
well as the academic campus. The load point 3 is a 5. System reliability target determination:
small percentage of the total connected loads and the The assumed reliability targets were set based on
housing area can be fed from other stand-by incomer, operation policies and maintenance process as shown
so it is not critical enough to be considered for the in table 1. Note that the Energy Not Supplied (EENS)
study. On the other hand, the loads points 1 and 2 are index is omitted because it is applicable to utility
more critical, but the load point 1 serves the utility organization where charging customers generates
areas that are essential for the entire campus. revenue, but NG doesn’t belong to this category of
Moreover, the load point 1 can feed the academic organizations, so EENS is simply discarded.
area and the housing area with some arrangements.
Thus, the load point 1 is the load point that needs Table 2: Load1 reliability indices for C8
improvements in the associated reliability indices. Scenario Scenario 1 Scenario 2
In this study, the stand-by lines that feed the loads are Equipment λ r u λ r U
assumed to be reliable, as RCM philosophy supports C1 0.01 1 0.01 0.01 1 0.01
such an assumption, so these stand-by lines are not C2 0.09 2 0.18 0.09 2 0.18
included in the study. The distribution system under C3 0.02442 6 0.146 0.02442 6 0.146
consideration comprises a variety of components that C4 0.09 2 0.18 0.09 2 0.18
C5 0.01 1 0.01 0.01 1 0.01
are interconnected in series and parallel. The
Ceq 0.7923 2.2 1.748 0.719533 2.3 1.655
reliability block diagram depicts those components as Indices 1.0168 2.274 0.943953 2.181
shown in figure 2, where parallel equipment types are
equivalence with Ceq. The equivalence failure rate (λ) C. Main-Analysis:
and repair time (r) are found as shown in equations 1 The processes in this stage are as follows:
and 4. 1. Critical component identification:
λ(Ceq)= λ1λ2(r1+r2) (1) The critical components are identified using the
proposed method by running two distinct scenarios
λ 1= λ7+ λ8+ λ9+ λ10+ λ11 (2) for each component to find the component criticality
factors, using equations 1 to 6. The procedure is
λ 2= λ12+ λ13+ λ14+ λ15+ λ16 (3)
listed in table 2 for C8 equipment, where reliability
r(Ceq)= (r1r2)/(r1+r2) (4) data needed for scenario 1 are the average of values
associated with each equipment type; C8 value is
r1 = (λ7r7+ λ8r8+ λ9r9+ λ10r10+ λ11r11)/ λ1 (5)
reflected in Ceq. The details of this method can be
r2 = (λ12r12+ λ13r13+ λ14r14+ λ15r15+ λ16r16)/ λ2 (6) found in [16], and the related equipment reliability
values are taken from statistical surveys presented in
4. Component-type selection for analysis: [1].
The medium voltage system consists of 28 major Having found all reliability indices for all equipment
equipment types that boil down to the following five types with the two scenarios, the associated criticality
component types: factors were computed. The coefficients α, β, and γ,
• 13.8 kV bus bars. required for the computation of the criticality factors,
are assumed to be 3, 4, and 0. The γ is set to zero to
• 13.8 kV circuit breakers (Vacuum).
rid of the EENS index. Such criticality factors were
• 13.8 kV underground cables. computed as in [16, (1)], and the resulted factors
were prioritized as listed in table 3. It is clear that
• 13.8/0.4 kV transformers. transformers (C8 & C13) and cables (C9 & C14) are
• 0.4 kV underground cables. the most critical components of the power system.
However, it is essential to determine which
• 0.4 kV circuit breakers (Vacuum). components of these identified critical components is
All of the component types are candidates for RCM the one whose impact is the largest on the load point
process, including the 0.4 kV breakers and 1 reliability indices. That is, the next stage is about
underground cables. Furthermore, the bus-tie breaker finding out the suitable number of critical equipment
is not considered for the RCM process to remove the types for this study. The cumulative factors of critical
redundancy in the system, so each incomer is tested values listed in table 4 are utilized such that the
individually. number of equipment obtained satisfies the inequality
constraint explained in [16, (2)]. In accordance with
 the inequality constraint [16, (2)], the µ is assumed to and utility manager after thorough inspection. The
be 1.6, so the inequality yields the following: weights reflect the importance of failure modes and
causes (the higher the better) whilst the scores reflect
CF m,j > 1.6 CFm,dest = 1.3 (7) the equipment condition at the time of inspection;
high scores mean good-conditioned equipment.
Having compared the satisfaction value of 1.3 with Having computed all weighing tables for the critical
the cumulative criticality factors of table 4, the first components, the resulted values are listed in table 8.
four components are to be selected as the critical. Successively, the component failure rates at the time
of inspection are to be calculated through Xtk (see
equation 10). The best as well as the worst scores of
Table 3: Component criticality factors of load1
Components λ2Load1 U2Load1 Criticality CF
the critical equipment must be assumed as given in
Factor table 9. The corresponding failure rates of the critical
C8 0.943953 2.181627 0.498026 0.498026 components at the time of inspection are listed in table
C13 0.943953 2.181627 0.498026 0.996052 10.
C9 0.831198 2.282542 0.179483 1.175535
C14 0.831198 2.282542 0.179483 1.355018 Xkt = X1k- Xkt(CS) / X1k – X0k (10)
C2 0.9968 2.234 0.092091 1.417079
Where Xkt(CS)
is the condition score of the k critical th
C4 0.9968 2.234 0.092091 1.50917
C7 0.9870061 2.274935 0.059571 1.568741
equipment at time t; X0k and X1k are the worst and best
C12 0.9870061 2.274935 0.059571 1.628312 condition scores, respectively.
C10 0.980429 2.245195 0.052192 1.680504
0.980429 2.245195 0.052192 1.732696
Table 5: Failure Modes & Causes of Transformers
C15
Equipment Failure Mode Failure Cause
C3 1.0116 2.243 0.05119 1.783886
Leakage Porcelain failure
C6 1.008617 2.24822 0.049937 1.833823 Corrosion
C11 1.008617 2.24822 0.049937 1.88376 Loose connection
C1 1.0158 2.273 0.032858 1.916618 Valve leak
C5 1.0158 2.273 0.032858 1.949476 Weld failure
Over pressurization
Gasket failure
Table 4: Desired criticality factor Deteriorated insulation Low oil level
Reliability Indices λ U CFLoad1, Des.
Power Transformers C8 & C13

Insulation failure
(Occ./yr.) (Hr./yr.) Tap changer failure
Base-Case 1.0168 2.2748 0.788841 Solid insulation failure
Winding insulation
Desired-Case 0.80 1.5
failure
Oil dielectric failure
2. Failure mode detection and critical failure Bushing failure
Oil contamination
mode/cause recognition of critical components: False output reading/no Short winding turns
The failure modes that contribute to the equipment output Open circuit
failure rates are to be addressed. The critical two High impedance load
transformers and the two underground cables are to path
Out of calibration
outline their corresponding failure modes and the Auxiliary control
associated causes as listed in tables 5 &6. failure
Radiator clogged
Fan failure
3. Failure rate modeling of critical components: Pump failure
It is accomplished using the exponential manner by Restricted oil flow
the use of weighing tables for all critical equipment Bushing CT failure
types introduced in [7]. The failure-rate functions
associated with the critical components: transformers Table 6: Failure Modes & Causes of Underground Cables
and underground cables are shown in equations 8 Equipment Failure Mode Failure Cause
and9, respectively. The coefficients of the equations Connection/termination Connection:
failure loose/corroded
Underground Cables C9 & C14

are deduced from practical studies [7].

Fail to provide conduction Conductor failure
λ(Xtk) = 0.05165 e2.2478602Xtk – 0.008148148 (8) path
Solid dielectric
failure
λ(Xtk) = 0.00453 e5.559723Xtk – 0.003031386 (9) Loose of concentric neutral Corrosion
Radio/TV interference Connection:
The Xtk values represent the components conditions loose/corroded
and are derived from the corresponding weighing High impedance path Conductor failure
tables. For instance, the weighing table of the
transformer C8 is shown in table 7. The weights and Connection:
loose/corroded
scores were set by a group of engineers, technicians,
Table 7: Inspection Failure-Rate Estimate of Transformer compute the reliability indices of the load point 1
C8 which are given in table 11. It is clear that the timely
Inspection Item Weight (W) Score (S) W*S reliability indices of the load point 1 are worse than
Porcelain failure 2 0.70 1.4 that of the desired indices, so there have to be
Corrosion 3 0.60 1.8 different maintenance tactics for improvements.
Loose connection 7 0.55 3.85
Valve leak 4 0.75 3
Weld failure 2 0.65 1.3
Table 11: Load Point 1 Timely Reliability Indices
Over pressurization 7 0.55 3.85 λ U
Gasket failure 3 0.60 1.8 (Occ./yr.) (Hr./yr.)
Low oil level 4 0.60 2.4 0.9181881 2.248136
Insulation failure 4 0.60 2.4
Tap changer failure 5 0.65 3.25 5. Outline possible maintenance tactics:
Solid insulation failure 5 0.50 2.5 In accordance with components’ failure modes tables,
Winding insulation failure 4 0.75 3
Oil dielectric failure
one should present all possible maintenance tactics for
6 0.60 3.6
Bushing failure 3 0.70 2.1 each critical equipment type. These tactics should
Oil contamination 6 0.65 3.9 obey the local maintenance policies, and it’s preferred
Short winding turns 4 0.75 3 to include different maintenance tactics in one
Open circuit 3 0.65 1.95 category such that several issues are rectified at once.
High impedance load path 3 0.65 1.95 Certainly, this approach would save a lot of man
Out of calibration 3 0.50 1.5 hours and outage times. For example, the transformer
Auxiliary control failure 4 0.55 2.2 C8 maintenance tactics are listed in table 12.
Radiator clogged 6 0.45 2.7
Fan failure 3 0.50 1.5
Pump failure 4 0.55 2.2 6. Cost-benefit analysis and tactics ranking:
Restricted oil flow 4 0.50 2 The cost-to-benefit process comes into play where all
Bushing CT failure 3 0.60 1.8 possible maintenance tactics for all critical equipment
Sum 102 60.95 are evaluated in terms of their cost-to-benefit
Weighted Average 60.95/102 = 0.59755 efficiency whereby costs and benefits per each
maintenance tactic are estimated, and the benefit-to-
cost ratio (BCR) index is thereafter calculated. The
Table 8: Timely Condition Score of Critical Equipment
Component Condition Score
cost involves labor cost, material cost, and tool cost.
Transformer C8 0.59755 On the other hand, the corrective maintenance costs
Transformer C13 0.56618 postponed owing to preventive maintenance (PM)
Cable C9 0.5022727 execution is the achieved benefits. The costs and
Cable C14 0.536363 benefits factors associated with the two transformers
are addressed in table 13. The utility experts and
manager were consulted to have a reasonable
Table 9: Critical Equipment Best & Worst Condition assumption of the data used for this matter. The
Scores details of this approach are found in [16].
Component Best Possible Worst Possible
Condition Score Condition Score
Transformer C8 0.97 0.34 Table 12: Possible Maintenance Tactics for Transformer
Transformer C13 0.96 0.32 C8
Cable C9 0.92 0.30 Plan Maintenance Tactics
Cable C14 0.96 0.34 Plan Oil-level test; gasket & oil sampling valve; oil
Table 10: Critical Equipment's Timely Condition Scores & 1 contamination & dielectric failure analyses; oil
conservator assessment
Failure Rates Plan Valve leak & weld failure checkups; seal failure & paper
Component Xkt λ 2 sample inspection; solid insulation inspection
Transformer C8 0.59119 0.186937 Plan Corrosion inspection; calibration checkup; high
Transformer C13 0.615344 0.197822 3 impedance load path test
Cable C9 0.673754 0.188799 Plan Loose connections inspection; over pressurization test;
Cable C14 0.683285 0.199238 4 short circuit voltage analysis; no-load current & losses
Plan Buchholz relay inspection; pressure relief device,
5 indicators, control circuits, and winding resistance
4. Load point/system reliability evaluation: analysis; protection analysis
The load point 1 is to be evaluated from the reliability Plan Inspection of bushings, oil level & gasket, surge arrestors
viewpoint so as to determine whether there is a need 6 & bushings connections inspection
for any modification for the existing maintenance Plan Tap changer inspection; dehydrating breather assessment;
7 motor drive condition inspection; contacts & leads
program or not at the time of inspection. It can be insulation of tap changer connections & leads inspection
done by the comparison of calculated reliability Plan Radiator, coolers, fans, and pumps inspection
indices and the desired ones. The critical components’ 8
failure rates listed in table 10 and the average failure
rates of other non-critical components are used to
 7. Selection of optimal maintenance plan: Table 14: Modified Inspection Failure-Rate Estimate of
The cost-effective maintenance plans are applied to Transformer C8, Plan1
critical components in a descending order as listed in Inspection Item Weight (W) Score (S) W*S
table 3. Hence, it starts with the transformer (C8) to Porcelain failure 2 0.70 1.4
compute its contribution to improving the load point 1 Corrosion 3 0.60 1.8
reliability indices. Should the desired reliability Loose connection 7 0.55 3.85
Valve leak 4 0.75 3.0
indices achieved, the algorithm stops. Otherwise, the Weld failure 2 0.65 1.3
second transformer (C13) comes into picture, and the Over pressurization 7 0.55 3.85
algorithm continues for all critical equipment until the Gasket failure 3 0.75 2.25
desired reliability indices are achieved. For the Low oil level 4 0.80 3.2
transformer (C8), the modified weighing table for Insulation failure 4 0.60 2.4
plan 1is listed in table 14. The new consequent failure Tap changer failure 5 0.65 3.25
rate of the load point 1 is 0.906, which is still too high Solid insulation failure 5 0.50 2.5
to be accepted. The optimal maintenance strategy for Winding insulation failure 4 0.75 3.0
Oil dielectric failure 6 0.75 4.5
the transformer (C8) is plan 5 as shown in table 15. Bushing failure 3 0.70 2.1
Nevertheless, neither the desired failure rate, nor the Oil contamination 6 0.80 4.8
desired outage time is achieved; hence, the Short winding turns 4 0.75 3.0
transformer (C13) is assessed in the same manner for Open circuit 3 0.65 1.95
the load point 1 reliability improvement. In High impedance load path 3 0.65 1.95
accordance with the modified reliability indices tables Out of calibration 3 0.50 1.5
[], the optimal maintenance plans for the two Auxiliary control failure 4 0.55 2.2
Radiator clogged 6 0.45 2.7
transformers combined (plan 5) achieve the desired
Fan failure 3 0.50 1.5
reliability indices as shown in table 16. Thus, the Pump failure 4 0.55 2.2
cables are not considered as part of the RCM plan. Restricted oil flow 4 0.50 2.0
However, the two achieved reliability indices differ Bushing CT failure 3 0.60 1.8
slightly from that of the desired ones, but including Sum 102 64.00
the third reliability equipment is not feasible Weighted Average 64.00/102 = 0.6274
considering the RCM expenses. To sum up, the
maintenance plan 5 is to be applied to both
transformers (C8 & C13) so as to attain the desired Table 15: Load1 Reliability Indices after Cost-Effective
reliability indices. Modification on C8
Plan λ U
(Occ./yr.) (Hr./yr.)
D. Post-Analysis:
Lastly, the results documentation and data structuring 1 0.906 1.99
2 0.910 2.0
process is held to feed future studies on the power
3 0.900 1.98
system. It includes the economic as well as the technical 4 0.898 1.98
aspects of the study. Also, the usage of cutting-edge 7 0.914 2.10
data management tools is necessary to ease handling 5 0.871 1.92
this huge information, let alone the iterative nature of 8 0.905 1.99
the process. Actually, most of the computations 6 0.908 2.0
included in the study were done using excel sheet, so
future studies would be easier.
Table 16: Accomplished Reliability Indices
Table 13: Cost-Benefit Maintenance Plans of Transformer λ U
C8 (Occ./yr.) (Hr./yr.)
0.810749 1.561864
Maintenance Maintenance Maintenance
Plan Cost Benefit BCR
Plan 1 11,012.5 25,695.83 2.33
Plan 2 61,050 132,873.5 2.18 III. CONCLUSION
Plan 3 21,025 42,050 2.0
Plan 4 2,512.5 4,606.25 1.83 This paper addresses the application of the referenced
Plan 7 2,925 5,175 1.77 model as a case study that incorporates almost all three stages
Plan 5 22,862.5 39,192.86 1.71 in the original algorithm-namely, Pre-Analysis, Main-Analysis,
Plan 8 2,812.5 4,821.43 1.71 and Post-Analysis. These stages are comprised of several steps
Plan 6 21,337.5 35,562.5 1.67 that are evaluated numerically. However, theorization is
different from implementation due to some practical difficulties
in which the thorough model implementation is deemed
impractical. For example, the historical reliability records of
systems and equipment are not always available, so statistical
surveys would offset such a deficiency. Furthermore, the
original model is tailored to utility industry, so some factors
turn out to be not applicable to other industries (e.g. EENS [8] Deepak Prabhakar P., Dr. Jagathy Raj V. P. "A New Model For
factor in criticality assessment). Reliability Centered Maintenance In Petroleum Refineries." Scientific
Paper. 2013.
The system-equipment approach was identified in a way [9] Geun-Pyo Park, Jae-Haeng Heo, Sang-Seung Lee and Yong Tae Yoon.
so that system components’ criticality factors are assessed not "Generalized Reliability Centered Maintenance Modeling Through
only from the equipment viewpoint, but from the overall Modified Semi-Markov Chain in Power System." Journal of Electrical
Engineering & Technology 2011: 7.
system viewpoint. In other words, the criticality contribution of
[10] James McCalley, Ward Jewell. "Risk Based Resource Allocation for
each equipment type to the overall system reliability is the key Distribution System Maintenance ." Technical. n.d. Document.
factor. The equipment failure rate is modeled using equipment [11] Jeff Golarz, Jim Webber. Operations Intelligence Provided By Circuit
timely condition scores in which exponential model is utilized. Breaker Condition Monitors. Technical . Saco: Intelligent Controls,
The economic considerations are present through the BCR 2006. Document .
index, where it deals with PM associated costs (labor, [12] L. Bertling, R. Eriksson, R. N. Allan. "Survey of Causes of Failures
materials, & tools) and the benefits through which CM work is Based on Statistics and Practice for Improvements of Preventive
postponed due to RCM implementation. The last stage is about Maintenance Plans." 14th PSCC. Sevilla: Kungliga Teniska Hogskolan
(KTH), 2002. 7. Document.
documentation of records for future considerations, which is
very important, as actual figures reflect the system status [13] N.K.K.Prasanna, Tushar. N. Desai. "ADVANCED QUALITY
MANAGEMENT PHILOSOPHIES AND TECHNIQUES - APPLIED
accurately. The statistical surveys, however, that was used for TO MAINTENANCE MANAGEMENT IN A PETROCHEMICAL
this study don’t suffer from potential bias that the historical INDUSTRY." Journal of Engineering Research and Studies Sept. 2011:
records do, so one may use them both comparatively for 9.
optimal results. The algorithm is applied on a distribution [14] Ng, H. "Reliability Centered Maintenance for (RCM) Distribution
power system that was illustrated thoroughly. Systems and Equipment." Sel-Assessment Report. 1999.
[15] Palo Alto, Ca. "Power Delivery Reliability Initiative." Technical Report.
2001.
ACKNOWLEDGMENT [16] Payman Dehghanian, Student Member, IEEE, Mahmud Fotuhi-
The authors thank King Fahd University for Petroleum and Firuzabad, Senior Member, IEEE,. "A Comprehensive Scheme for
Minerals for the support and facilities that made this research Reliability Centered Maintenance in Power Distribution Systems—PartI:
Methodology." IEEE TRANSACTIONS ON POWER DELIVERY 2012:
possible. Also, the authors thank the National Guard for 10.
participating in the discussion about their power system [17] Penrose, Howard W. Basic Overview of Reliability-Centered
reliability indices. Maintenance Based Approach for Motor Management Programs.
Technical. Chicago, Il: T-Solutions, Inc., 2004. Document.
REFERENCES [18] Purucker, Steven. "Feasibility Study: Application of RCM Techniques
for Substation Maintenance at the Bonneville Power Adminstration."
Oak Ridge National Labaratory . Oak Ridge TN: Martin Marietta, 28
[1] Payman Dehghanian, Student Member, IEEE, Mahmud Fotuhi- May 1992. Document.
Firuzabad, Senior Member, IEEE,. "A Comprehesive Scheme for [19] Reyes-Picknell, John D. Campbell and James V. "UPTIME." Reyes-
Reliability-Centered Maintenance in Power Distribution Systems- Part Picknell, John D. Campbell and James V. Startegies for Excellence in
II: Numerical Analysis." Technical. 2013. Document. Maintenance Management. Portland: Taylor & Francis, 2006. 384.
[2] Adapa, R. "Grid Equipment Reliability Study." Technical Report. 2001. Hardcover.
[3] ADOGHE, ANTHONY UWAKHONYE. RELIABILITY CENTERED [20] Roy Billinton, Ronald Allan. "Network Modeling and Evaluation of
MAINTENANCE (RCM) FOR ASSET MANAGEMENT IN Simple Systems." Roy Billinton, Ronald Allan. Reliability Evaluation of
ELECTRIC POWER DISTRIBUTION SYSTEM. Thesis. OGUN: Engineering Systems: Concepts and Techniques. New York: Plenum
ANTHONY UWAKHONYE ADOGHE, 2010. Document. Press, n.d. 20. Document.
[4] Bahatt. Power Quality Predictive Maintenance (PQPM) Systems [21] Vanittanakom. "Reliability Centered Maintenance for PEA Power
Application for Circuit Breakers and Reclosers. Technical. Knoxville, Distribution Systems." The International Conference on Electrical
TN: ESRI, 2000. Engineering. Bangkok, Thailand: Kasetsart University, 2008. 5.
[5] Bhatt. The Application of Power Quality Monitoring Data for Reliability Document.
Centered Maintenance. Technical. Knoxville, TN: ESRI, 2000. [22] Vujovic, P. "Reliability Centered Maintenance (RCM) Technical
[6] Bhatt, S. "Case Studies for Preventive Maintenance." Technical Report. Reference for Power Delivery." Technical Report. 2003.
2000.
[7] Brown, Richard. "Failure Rate Modeling Using Equipment Inspection
Data." n.d. Document.

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