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Jurnal

The study focuses on scheduling preventive maintenance for screw press machines at PT. PAS using the Reliability Centered Maintenance (RCM) method. It identifies critical components and proposes maintenance intervals for various parts to prevent damage, highlighting five priority repair types: worm screws, extension shafts, bearings, press cages, and oil seals. The proposed maintenance intervals range from 250.72 to 307.84 hours, aiming to enhance machine reliability and reduce production downtime.

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0% found this document useful (0 votes)
7 views14 pages

Jurnal

The study focuses on scheduling preventive maintenance for screw press machines at PT. PAS using the Reliability Centered Maintenance (RCM) method. It identifies critical components and proposes maintenance intervals for various parts to prevent damage, highlighting five priority repair types: worm screws, extension shafts, bearings, press cages, and oil seals. The proposed maintenance intervals range from 250.72 to 307.84 hours, aiming to enhance machine reliability and reduce production downtime.

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farhan04bkt
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JURIT

Jurnal Riset Ilmu Teknik


P-ISSN: 2987-7261 Journal homepage: https://jurnaljepip.com/index.php/jurit
E-ISSN: 2987-7253 Vol 1, No. 1, pp;1-14, 2023
DOI: doi.org/10.59976/jurit.v1i1.4

Scheduling Preventive Maintenance to Determine Maintenance Actions


on Screw Press Machine

Ferdi Pohan1*, Imam Saputra2, Rahmat tua Pulungan3


Universitas Sumatera Utara
1,2,3

Jalan Dr. T. Mansur No.9, Padang Bulan, Kec. Medan Baru, Kota Medan, Sumatera Utara
E-mail: ferdi.p@usu.ac.id, imamsaputra11@usu.ac.id, Rahmat_tua@gmail.com

Submitted: 04/17/2023; Reviewed: 05/14/2023; Accepted: 05/31/2023

ABSTRACT
PT. PAS is a company engaged in the manufacturing industry that manages palm oil into Crude Palm Oil (CPO)
and Palm Kernel (PK) with a capacity of 30 tons/hour. The method used in this study is Reliability Centered
Maintenance (RCM). This method can determine the actions of preventive maintenance activities on each
component of the screw press machine. This study aims to provide proposals for scheduling preventative
Maintenance of screw press machines using the Reliability Centered Maintenance (RCM) approach and de-
termine appropriate maintenance actions in damage prevention. Based on the results of the research con-
ducted, it can be concluded that in the Reliability Centered Maintenance (RCM) approach, it is known that five
types of damage are a priority for repair. The types of damage included in the repair priority are worm screws,
extension shafts, bearings, press cages, and oil seals. The proposed maintenance time for each critical com-
ponent is a worm screw component maintenance time interval of 307.84 hours, and changeover schedules
every 2035.3 hours. Component extension shaft maintenance time interval 279.5 hours, changeover schedule
every 1824.5 hours. Bearing components have a maintenance time interval of 300.2 hours and a changeover
schedule of every 1492.5 hours. Oil seal components maintenance time interval 286.1 hours, changeover
schedule every 2769.9 hours. Press cage components maintenance time interval 250.72 hours, changeover
schedule every 3277.8 hours. Actions are taken in the form of direct prevention of the source of component
damage based on the time or age of the component.

Keywords: Machine Screw Press, Preventive Maintenance, Reliability Centered Maintenance (RCM).
`

This is an open-access article under the CC–BY license.

INTRODUCTION
The machine is a tool with energy conversion to help facilitate human work [1]. The device
must be adequately maintained to keep the production process running smoothly according to

1
2

company expectations. Machine maintenance systems are generally divided into two, namely, Cor-
rective Maintenance and Preventive Maintenance [2][3][3]. Corrective Maintenance is a maintenance
activity carried out after the component is damaged or breakdown, while Preventive Maintenance is
carried out before the part is impaired [4]. The impact of periodic machine maintenance includes not
achieving production targets, losing production time, high repair costs, and low productivity [5]. In
addition, good Maintenance can extend the machine's life and prevent damage that can cause some
losses [6].
The research was conducted at PT. PAS at the press station is a company engaged in the man-
ufacturing industry, one of the palm oil processing companies. Found problems that occurred PT. PAS
is the frequent occurrence of damage to the machine that causes the production process to stop.
Damage to the production machine is caused because machine maintenance scheduling is not applied
regularly[7]. At the press station, there are several machines: fruit elevator machines, cake breaker
conveyor machines, digester machines, screw press machines, vibrating screen machines, and others
[8].
One of the machines at the press station that most often suffered damage was the screw press
machine. Some of the causes why the screw press machine becomes damaged, namely: the gearbox
inserts rough objects such as iron chips with a diameter exceeding the size of the screw, worn bolt
heads, the installation of the gearbox shaft and worm screw shaft is not suitable so that it can cause
shaft breakage, and operator negligence can also cause damage to the engine For example, when the
engine has vibrated violently, it can be ascertained that the engine has been damaged and the oper-
ator still forces the machine work. Damage due to interference with the Screw Press unit includes
leakage in the seal, damage to the worm screw, wear on bearings, damage to the drive shaft, damage
to the press cage, and short drive shaft screw press. The purpose of this study is to propose preven-
tive maintenance scheduling for screw press machines and determine appropriate maintenance ac-
tions to prevent damage.
The method used in this study, Reliability Centered Maintenance (RCM), is a logical engineer-
ing process for determining maintenance tasks that will ensure a reliable system design with specific
operating conditions in a typical working environment [9][10]. For this reason, proper maintenance
scheduling planning is carried out. Therefore, this researcher carried out machine maintenance sys-
tem planning using the Reliability Centered Maintenance (RCM) method, and this method can deter-
mine the preventive maintenance activities on each component of the screw press machine [11].

METHOD
Data Collection
Primary Data
Primary data include production amount, machine maintenance system, causes of machine
failure, frequency of damage, engine operating hours, last year's engine downtime data, engine repair
time data, and engine change interval data [ 9] [10][13].

Data Processing
1. Identify critical components of the machine using the RCM method. The stages in the process of
working using the RCM Method are [14]–[22]:
a. Failure Mode and Effect Analysis (FMEA)
Used to determine consequences and decide what to do to anticipate, prevent, detect, or im-
prove them.
b. Logic Tree Analysis (LTA)
It is a qualitative measurement for classifying failure modes. Failure modes can be classified
into four categories, namely [23]–[29]:

Pohan, et al
3

c. Task Selection
It is done to determine the policies that can be applied (practical) and select the most efficient
task for each failure mode.
2. Preventive Maintenance Scheduling Planning.
Maintenance scheduling planning is usually done at planned time intervals. The level of
equipment or machine and load conditions determine this interval distance. Preventive
Maintenance can help extend engine life (up to 3-4 times) and reduce unexpected damage.
The RCM (Reliability Centered Maintenance) method performs interval policy and machine
maintenance activities. This schedule keeps the preventive maintenance program organized
and neat and does not interfere with the production process or other activities.
3. Proposed Maintenance System Improvement.
It is the proper maintenance system improvement plan to be implemented by the company
from the research results.
The data processing flow diagram in this study is shown in Figure 1.

Figure 1. Data Processing Flowchart

RESULTS
Failure Mode Effect Analysis (FMEA)
FMEA determines consequences and decides what to do to anticipate, prevent, detect, or im-
prove them. The FMEA results of the Screw Press Machine are shown in Table 1.

Table 1. FMEA Results of The Screw Press Machine


Failure Effect Causes of Actions
Compo- Fungsi compo- S O D RPN
mode failure Failure taken
nent nents
Main compo- Inlet iron
Cessation of Worm
Worm nents of the CPO- piece, too
the extrac- Break screw re- 9 6 9 486
Screw extracting ma- high hydrau-
tion process placement
chine lic load
Looseness on Compo-
The rotation
Main the locking nent re-
Clutch / Drive of the main Cracked da n
Shaft peg of the placement 8 5 5 200
Media shaft Broken
Short worm, Empty and oil fill-
stopped
oil ing
Pohan, et al
4

Looseness on Compo-
The rotation
Main the locking nent re-
Clutch / Drive of the main Cracks and
Shaft peg of the placement 8 5 5 200
Media shaft Breaks
Long worm, Empty and oil fill-
stopped
oil ing
Tighten-
ing of
Hydraulic
Exten- Worm screw Sagging bolts and
pressure
sion buffering/con- bolts and Break nuts, re- 8 6 10 480
power pack,
Shaft tainment nuts placement
iron fracture
of compo-
nents
The move-
It keeps oil from ment of the Broken seal, Delay in oil Regular
Oil Seal 9 6 6 324
spilling engine is not Leaking oil filling oil filling
smooth
Regular
Rounds do Worn, rup- Empty oil,
bearing
Bearing Main shaft drive not tured Usage over 8 6 10 480
replace-
Stable capacity
ment
Not mov- Belt con-
V belt Drive Liaison Break Overvoltage 10 6 1 60
ing trol
Strainer As a filter for Ineffective Wear/thin- Long work- Regular
4 5 8 160
Plate pressing screening ning ing hours check-ups
Press cage
Press As a filter for Ineffective Wear/thin- Regular
pores are 9 5 9 405
Cage pressing screening ning check-ups
clogged
Compo-
Ccs Con- Stabilizer/Flash- No flash- Overwork-
Wear/break nent re- 6 5 9 270
nector light light ing hours
placement
Compo-
Worm screw Unstable Overwork-
Bushing Wear Out nent re- 4 5 9 180
head positioning ground ing hours
placement
Oil addi-
Pump Per-
tion, hy-
Hidrolick formance
Pressing the Empty hy- draulic
Power Perfor- Rupture lag 3 3 1 9
guide cone draulic oil hose
Pack mance Im-
mainte-
provement
nance
Shaft con-
Cone Overwork-
Shaft Stabilizing the duction,
guide, not Shaft wear ing hours. 3 3 3 27
Cone guide cone lubrica-
flashlight Lack of care
tion
Change of
Fiber-pressing Wet fiber Excessive
Cone Wear wall cone guide
media from oil does working 3 3 3 27
Guide plate e layer
worm screws not melt hours
plate

Pohan, et al
5

Table 1 shows seven types of damage that are priority repairs in the RPN Hight category. The
types of damage included in the repair priority are Worm Screw, Extension Shaft, Bearing, Press Cage,
Oil Seal, Ccs Connector, and Main Shaft Short.

Logic Tree Analysis (LTA)


Logic Tree Analysis (LTA) is a qualitative measurement for classifying failure modes. Deter-
mine LTA priority in the following way: Evident, that is, does the operator know there has been a
disturbance in the system under normal conditions? Safety, that is, does this damage mode cause
safety problems? The outage, i.e., does this damage mode result in all or part of the machine stop-
ping? Category, namely the categorization obtained after answering the questions asked. The ar-
rangement for selecting actions for critical components can be shown in Figure 2.

Does the operator know that


there has been a disturbance in
the system under normal
conditions? (evident)

Yes No
D
Does this crash mode cause
Hidden Failure
safety issues? (safety)

Yes No Yes No
A
Does this fault mode result in Does this fault mode result in all
Does this crash mode cause
Safety Problem all or part of the machine or part of the machine
safety issues? (safety)
stopping?(Outage) stopping?(Outage)

D/A D/B Yes No D/C


B Yes C
Small possibility of
Safety Problem Outage Problem
Outage Problem economic problems
Small possibility of
economic problems
Immediate Impact Indirect Impact

Figure 2 Structure of Logic Tree Analysis

Logic Tree Analysis (LTA) Screw Press Machine is shown in Table 2.

Table 2 Logic Tree Analysis (LTA) Screw Press Machine


Effect of com- Causes of Fail-
Component Evident Safety Outage Category
ponent failure ure
Worm Screw Inlet iron piece, too
Break Y N Y B
high hydraulic load
Hydraulic pressure
Extension Shaft
Break power pack, iron Y N Y B
fracture
Empty oil, Us-
Worn, ruptured
Bearing age over capac- Y N Y B
ity
Press Cage Wear/thinning Press cage N N Y D/B

Pohan, et al
6

pores are
clogged
Broken seal, Leak-
Oil Seal ing oil Delay in oil filling
N N Y D/B

Overworking
Ccs Connector Wear/break N N Y D/B
hours
Main Shaft Looseness on the
Cracked and Bro-
Short locking peg of the Y N Y B
ken
worm, Empty oil

Based on Table 2 of the Logic Tree Analysis (LTA) of the Screw Press Machine, it can be seen
that: Worm Screw components, Extension Shafts, bearings, and Main Shaft Short are included in cat-
egory B (Outage problem) in failure mode can shut down the system. While the Oil Seal Component,
Press Cage, and Ccs Connector are included in the D/B (hidden Failure) category, namely, the failure
mode that occurs cannot be known by the operator and can interfere with production.

Task Selection
Action selection is the final stage of the RCM process. A list of practical actions from each
damage mode is created. The Screw Press, Machine Selection Task, is shown in the table3

Table 3. The Screw Press Machine Selection Task


Effect of Selection Guide
Causes of Fail-
Component Component Task Selec-
ure 1 2 3 4 5 6 7
Failure tion
Inlet iron piece,
Worm Screw
Break too high hydraulic Y Y N N N N N TD
load
Hydraulic pres-
Extension Shaft
Break sure power pack, Y Y N N N N N TD
iron fracture
Worn, rup- Empty oil, Us-
Bearing tured age over ca- Y Y N N N N N TD
pacity
Press cage
Wear/thin-
Press Cage pores are N N N N N Y Y TD
ning
clogged
Broken seal,
Oil Seal Leaking oil Delay in oil filling Y Y N N N N N TD

Overworking
Ccs Connector Wear/break Y N N Y Y N N FF
hours
Main Shaft Looseness on the
Cracked and
Short locking peg of the Y N Y N N N N CD
Broken
worm, Empty oil

Pohan, et al
7

Based on Table 3, Selection of actions from damage to Screw Press Machine components, it
can be seen that Worm Screw Components, Extension Shafts, Bearings, Oil Seals, and Press Cages are
components included in the selection of Time Directed (TD) actions. Actions are taken in the form of
direct prevention of the source of component damage based on the time or age of the component.
The Ccs Connector component is included in selecting Failure Finding (FF) actions. The action taken
is to find hidden component damage with periodic inspections. The Main Shaft Short component de-
termines Condition Directed (CD) actions. The action taken is in the form of detecting damage by
inspecting components.

Preventive Maintenance Scheduling Planning Reliability


Based on the results of RCM analysis, reliability calculations are carried out on components
included in the selection of Time Directed (TD) actions. These components include Worm Screw, Ex-
tension Shaft, Bearing, Oil Seal, and Press Cage. Each component's time to repair (TTR) and Time To
Failure (TTF) calculations are carried out to determine the appropriate distribution for damage.
These distribution identifiers include the Exponential, Normal, Lognormal, and Weibull distributions.
To determine the distribution pattern by damage data, 5 Components of Worm Screw, Extension
Shaft, Bearing, Press Cage, and Oil Seal are shown in Figure 3 to Figure 7.

Figure 3. PDF Diagram of Worm Screw Compo- Figure 4. PDF Diagram of Extension Shaft
nent Damage Component Damage

Pohan, et al
8

Figure 5. PDF Diagram of Bearing Component Figure 6. Oil Seal Component Damage PDF Di-
Damage agram

Figure 7. Press Cage Component Damage PDF


Diagram

Figures 3 through 7 determine the distribution pattern corresponding to the data. The Prob-
ability Density Function (PDF) closest to the downward shifting line is the most appropriate to the
data, and it can be said that the data has followed that distribution pattern. To see the corresponding
distribution can use the information from the text output, which can be seen in Table 4.

Table 4. Distribution and Parameter Test Summary


Distribution Pat-
Component Statistics Parameters
terns
Worm Screw Lognormal 0.17795  =  = 
Extension Shaft Usual 0.16367  =   = 
Bearing Weibull 0.19798  =   = 
Pohan, et al
9

Oil Seal Usual 0.22667  =   = 


Press Cage Usual 0.28583  =   = 

Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR)


Mean Time to Failure (MTTF) is the average time of component damage used only on fre-
quently damaged components and must be replaced with new or good components. At the same time,
the Mean Time to Repair (MTTR) is the average time to repair these engine components. The follow-
ing is the Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) from the critical component
data of the Screw press machine.
The following Mean Time To Failure (MTTF) of critical component data of the Screw press
machine through output calculations from easyfit 5.6 professional software is shown in Figure 8 to
Figure 12.

Figure 8. Worm Screw Components Figure 9. MTTF Component Extension Shaft

Figure 10. MTTF Bearing Components Figure 11. MTTF Oil Seal Components

Pohan, et al
10

Figure 12. MTTF Component Press Cage

Based on Figure 8 to Figure 12, it can be seen that the Mean Time to Failure (MTTF) damage
to the Worm Screw Component is 2035.3 hours, the Extension Shaft Component damage is 1824.5
hours, the Bearing Component damage is 1492.5 hours, Oil Seal Component damage is 2769.9 hours,
Press Cage Component damage is 3277.8 hours.

Determination of treatment time intervals


The determination of the maintenance time interval is aimed at finding out the optimal Time for the
Maintenance of components, the calculation of which is as follows:
1. Component Worm Screw
Average production hours per month = 486.4 Hours
The amount of damage in 1 year = 10 Times
Average repair time (MTTR) = 9 hours
The average inspection time = 3 hours
a. Average time to repair
1 MTTR 9 hours
= = = 0.019 hours
µ Average production hours per month 486.4 hours
1 1
µ= 1⁄ = 0,019 = 54.04 hours
µ
b. Average inspection time
1 Average one−time inspection 3 hours
= = = 0.006 hours
𝑖 Average production hours per month 486.4 hours
1 1
i= 1⁄ =
0,006
= 162.1 hours
𝑖

c. Average damage
Total damage in 1 year 10
𝑘 = = = 0.83
12 12

d. Optimal inspection frequency


𝑘𝑥𝑖 0.83 𝑥 162,1
𝑛 =√ = √ = 1.58
µ 54.04

e. Maintenance time interval

Pohan, et al
11

Average production hours per month 486.4 hours


𝑡𝑖 = = = 307.84 hours
𝑛 1.58
Recapitulation of maintenance time intervals of Screw Press Machine components can be seen in Ta-
ble 5.

Table 5. Recapitulation of Maintenance Time Intervals of Screw Press Machine


Amount of damage MTTF Maintenance time
Component MTTR (Hours)
(times) (Hours) interval (Hours)
Worm Screw 10 2035.3 9 307.84
Extension Shaft 10 1824.5 11 279.5
Bearing 8 1492.5 8 300.2
Oil Seal 7 2769.9 5 286.1
Press Cage 5 3277.8 9 250.72

Preventive Maintenance
To avoid downtime on the machine, the proposal is in the form of preventive maintenance
component maintenance using the Average Time To Failure as the maintenance schedule. At the
same time, the Maintenance of components with the proposed actions is shown in Table 6.

Table 6. Maintenance of components with proposed actions


Component Fungsi components Actions taken
Direct prevention of sources of component damage
Worm Screw main components of the
based on the time or life of the component by replac-
CPO extracting machine
ing the defective part with a new one.
Tightening of bolts and nuts: when the bolts are loose
Extension Shaft Worm screw buffer-
and worn, do bolt tightening or bolt replacement, and
ing/containment
damaged components are replaced.
Regular bearing replacement is on schedule and does
Bearing Main shaft drive
not exceed component life.
Oil Seal Regular oil filling, following a maintenance schedule,
It keeps oil from spilling.
and frequent periodic checks.
Check regularly and change components according to
the maintenance schedule so that the pores of the
Press Cage As a filter for pressing
press cage are not clogged and no damage can inter-
fere with the filtering poses.

DISCUSSION
The Mean Time to Repair (MTTR) of the worm screw component was obtained from the data
on the machine repairs carried out, which was 9 hours, and the maintenance time interval for the
Worm Screw component was every 307.84 hours. Preventive maintenance measures for the worm
screw component can be carried out by directly preventing the source of component damage by re-
placing the damaged component with a new component. This action aligns with [30] research, where
his research on replacing actions reduces downtime from 7.29 hours/month to 7.08 hours/month,
or by 0.21 hours/month (2.85%). Meanwhile, the decrease in machine maintenance costs with pre-
ventive Maintenance was from IDR 14,469,590.00 to IDR 8,908,230.00, or a savings of 38%. The
MTTR of the extension shaft is 11 hours, and the maintenance time interval for the extension shaft
Pohan, et al
12

component is every 279.5 hours. Preventive Maintenance of the extension shaft component can be
done by tightening the bolts and nuts. If the bolts are loose and worn out, tighten the bolts or replace
the bolts, replace the damaged components. According to [31] research, optimal inspection fre-
quency is two times in one month, and availability is 98.87%. There are two results of suitable
maintenance activities: time-directed life-renewal task and time-directed life-renewal task & Failure
finding task. The Mean Time to Failure (MTTF) for bearing component damage is 1492.5 hours. The
maintenance time interval for bearing components is every 300.2 hours. Preventive Maintenance of
bearing components can be carried out by replacing bearings regularly according to a schedule and
not exceeding the service life of the components. These results are reinforced by [32] research re-
sults, namely maintenance time intervals and optimal replacement of Spring Carbon Brush compo-
nents on Callender machines that have a value of the highest reliability of 85% and occur in 35 days.
The most optimal replacement time interval is 18 days, and 16 component repairs occur in 1 year. •
Savings on downtime and costs incurred when carrying out preventive Maintenance for 35 days with
16 treatments of IDR. 2,880,000,-. Compared to before using the Age Replacement method, the com-
pany paid a fee of IDR. 4,590,000

CONCLUSION

Five types of damage are priority repairs. The types of damage included in the repair priori
are worm screws, extension shafts, bearings, press cages, and oil seals. The proposed maintenance
time for each critical component is a worm screw component maintenance time interval of 307.84
hours; changeover schedule every 2035.3 hours. Component extension shaft maintenance time in-
terval 279.5 hours, changeover schedule every 1824.5 hours. Bearing components maintenance time
interval 300.2 hours, changeover schedule every 1492.5 hours. Oil seal components maintenance
time interval 286.1 hours, changeover schedule every 2769.9 hours. Press cage components mainte-
nance time interval 250.72 hours, changeover schedule every 3277.8 hours. Actions are taken in the
form of direct prevention of the source of component damage based on the time or age of the com-
ponent.

REFERENCES
[1] N. Sembiring, "The spare part maintenance of cake breaker conveyor with reliability centered
spares method," IOP Conference Series: Materials Science and Engineering, vol. 523, no. 1. 2019.
doi: 10.1088/1757-899X/523/1/012079.
[2] N. A. Sidabutar, "Propose Improvement Maintenance Activities of Screw Press to Reduce
Waste Using Lean Maintenance Concept," IOP Conference Series: Materials Science and
Engineering, vol. 505, no. 1. 2019. doi: 10.1088/1757-899X/505/1/012045.
[3] A. Popa, "A framework of best practices for delivering successful artificial intelligence projects.
A case study demonstration," Proceedings - SPE Annual Technical Conference and Exhibition,
vol. 2021. 2021. doi: 10.2118/206014-MS.
[4] L. Zhang, "Optimizing imperfect preventive maintenance in multi-component repairable
systems under s-dependent competing risks," Reliab. Eng. Syst. Saf., vol. 219, 2022, doi:
10.1016/j.ress.2021.108177.
[5] A. A. Arzhaev, "On the Importance of Applying RCM Technology to Passive Components of
Russian NPPs," Lecture Notes in Mechanical Engineering. pp. 217–230, 2022. doi:
10.1007/978-981-16-9376-2_22.
[6] L. Ciani, "Condition-based Maintenance for OilGas system basing on Failure Modes and Effects
Analysis," 5th International Forum on Research and Technologies for Society and Industry:

Pohan, et al
13

Innovation to Shape the Future, RTSI 2019 - Proceedings. pp. 85–90, 2019. doi:
10.1109/RTSI.2019.8895587.
[7] A. Rahman, "Industry 4.0 and society 5.0 through lens of condition based maintenance (CBM)
and machine learning of artificial intelligence (MLAI)," IOP Conference Series: Materials Science
and Engineering, vol. 852, no. 1. 2020. doi: 10.1088/1757-899X/852/1/012022.
[8] M. S. Alvarez-Alvarado, "Reliability-based smart-maintenance model for power system
generators," IET Gener. Transm. Distrib., vol. 14, no. 9, pp. 1770–1780, 2020, doi: 10.1049/iet-
gtd.2019.1186.
[9] J. Wang, "Digital twin based intelligent risk decision-making system of compressor station
equipment," Nat. Gas Ind., vol. 41, no. 7, pp. 115–123, 2021, doi: 10.3787/j.issn.1000-
0976.2021.07.013.
[10] R. Negi, "Experience in Asset Performance Management Analytics for decision support on
Transmission Distribution Assets," Asia-Pacific Power and Energy Engineering Conference,
APPEEC, vol. 2019. 2019. doi: 10.1109/APPEEC45492.2019.8994622.
[11] J. Dias, "Productivity improvement of transmission electron microscopes - A case study,"
Procedia Manufacturing, vol. 51. pp. 1559–1566, 2020. doi: 10.1016/j.promfg.2020.10.217.
[12] C. C. D. F. Moraes, "Using the multi-criteria model for optimization of operational routes of
thermal power plants," Energies, vol. 14, no. 12, 2021, doi: 10.3390/en14123682.
[13] S. N. Kamble, "Machine health monitoring with life cycle cost analysis by condition
monitoring," Materials Today: Proceedings, vol. 52. pp. 893–897, 2022. doi:
10.1016/j.matpr.2021.10.296.
[14] S. K. Palei, "Reliability-Centered Maintenance of Rapier Dragline for Optimizing Replacement
Interval of Dragline Components," Mining, Metall. Explor., vol. 37, no. 4, pp. 1121–1136, 2020,
doi: 10.1007/s42461-020-00226-5.
[15] S. J. Kim, "Comparative Study for Inspection Planning of Aircraft Structural Components," Int.
J. Aeronaut. Sp. Sci., vol. 22, no. 2, pp. 328–337, 2021, doi: 10.1007/s42405-020-00319-x.
[16] A. Jiang, "An operating environment-based preventive maintenance decision model," J. Qual.
Maint. Eng., vol. 26, no. 4, pp. 592–610, 2020, doi: 10.1108/JQME-01-2019-0003.
[17] K. Chung, "Cloud based u-healthcare network with QoS guarantee for mobile health service,"
Cluster Comput., vol. 22, pp. 2001–2015, 2019, doi: 10.1007/s10586-017-1120-0.
[18] E. F. Mami, "Maintenance optimisation through quality management: A case study in 'Alzinc'
Plant in Algeria," Int. J. Product. Qual. Manag., vol. 27, no. 1, pp. 97–123, 2019, doi:
10.1504/IJPQM.2019.099629.
[19] S. B. Singh, "Reliability centered maintenance used in metro railways," J. Eur. des Syst. Autom.,
vol. 53, no. 12, pp. 11–19, 2020, doi: 10.18280/jesa.530102.
[20] S. Luongo, "Human Machine Interface Issues for Drone Fleet Management," Advances in
Intelligent Systems and Computing, vol. 876. pp. 791–796, 2019. doi: 10.1007/978-3-030-
02053-8_120.
[21] Z. Hussain, "Establishing simulation model for optimizing efficiency of CNC machine using
reliability-centered maintenance approach," Int. J. Model. Simulation, Sci. Comput., vol. 10, no.
6, 2019, doi: 10.1142/S179396231950034X.
[22] K. F. Tee, "Reliability-based preventive maintenance strategies of road junction systems," Int.
J. Qual. Reliab. Manag., vol. 36, no. 5, pp. 752–781, 2019, doi: 10.1108/IJQRM-01-2018-0018.
[23] I. P. M. Derks, "Predictors and patterns of eating behaviors across childhood: Results from The
Generation R study," Appetite, vol. 141, 2019, doi: 10.1016/j.appet.2019.05.026.
[24] Y. T. Prasetyo, "Equipment Reliability Optimization Using Predictive Reliability Centered
Maintenance: A Case-Study Illustration and Comprehensive Literature Review," 2020 7th
International Conference on Frontiers of Industrial Engineering, ICFIE 2020. pp. 93–97, 2020.

Pohan, et al
14

doi: 10.1109/ICFIE50845.2020.9266728.
[25] M. A. Arjomandi, "A fuzzy DEMATEL-ANP-VIKOR analytical model for maintenance strategy
selection of safety critical assets," Adv. Mech. Eng., vol. 13, no. 4, 2021, doi:
10.1177/1687814021994965.
[26] J. Geisbush, "Developing a Reliability Centered Maintenance Model for Large Diameter
Pipeline Maintenance," Pipelines 2022: Planning and Design - Proceedings of Sessions of the
Pipelines 2022 Conference, vol. 2. pp. 150–160, 2022. doi: 10.1061/9780784484289.018.
[27] J. Taverniers, "The Tides of the Zodiac MK VI HD: Comparing the Usability of Inflatable Boats
for Seaborne Operations," IISE Trans. Occup. Ergon. Hum. Factors, vol. 7, no. 1, pp. 22–30, 2019,
doi: 10.1080/24725838.2019.1584775.
[28] M. Shafiee, "An integrated FTA-FMEA model for risk analysis of engineering systems: A case
study of subsea blowout preventers," Appl. Sci., vol. 9, no. 6, 2019, doi: 10.3390/app9061192.
[29] A. P. Marugán, "Reliability analysis of detecting false alarms that employ neural networks: A
real case study on wind turbines," Reliab. Eng. Syst. Saf., vol. 191, 2019, doi:
10.1016/j.ress.2019.106574.
[30] Y. Praharsi, I. K. Sriwana, and D. M. Sari, “Perancangan Penjadwalan Preventive Maintenance
Pada Pt. Artha Prima Sukses Makmur,” J. Ilm. Tek. Ind., vol. 14, no. 1, pp. 59–65, 2015.
[31] O. D. Cahyani and I. Iftadi, “Penjadwalan Preventive Maintenance dengan Metode Reliability
Centered Maintenance pada Stasiun Cabinet PU di PT IJK,” Teknoin, vol. 27, no. 1, pp. 25–34,
2021.
[32] M. I. Haq and D. Riandadari, “Penentuan Penjadwalan Preventive Maintenance Pada
Komponen Mesin Callender Di Pt. Karet Ngagel Surabaya Wira Jatim,” J. Pendidik. Tek. Mesin,
vol. 9, no. 1, 2019.

Pohan, et al

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