Transfer inovácií 31/2015
2015
SIMULATION OF MAINTENANCE AND REPAIR OF MACHINES
Ing. Alena Pešková
doc. Ing. Jozef Svetlík, PhD.
Technical University of Košice
Faculty of Mechanical Engineering
Department of Production Systems
Němcovej 32, 042 00 Košice, Slovakia
e-mail: [email protected]
Abstract
We present the simulation tool Insight
Maker in this paper applied to problems of
maintenance and repair of machines .It is also
suitable for users who do not have programming
experience.
This
open
software
allows
communication with the spreadsheets, where
simulation results can be processed. We demostrate
the possibilities of Insight Maker in the illustrative
example of dynamic elimination of machine
failures. We propose to plan a maintenance in the
situations when elimination of the failures has
precedence over planned preventive maintenance.
The results of simulations are graphs that describe
the creation of failures and repairs of machines
during their preventive maintenance.
Key words: Insight Maker, dynamic simulation,
maintenance and repair
INTRODUCTION
For those users of simulation tools who
do not have programming experience it is advised
to look for the simplest program to eliminate this
disadvantage. For example it is possible to use the
graphical program Vensim [1]. More articles and
work, in which the authors found the
implementation of this program to solve the
problem of maintenance were abroad created. Let
us mention dissertation [2] "Modelling Industrial
Maintenance Systems and the Effects of Automatic
Condition Monitoring", where maintenance is
solved from a system point of view. The main
results confirm that the subjective character of the
failure, available information of machine and
repeatability maintenance are the most important
factors in the management of a maintenance
system. Author effectively use Vensim in
modelling workers allocation in maintenance,
elimination of component failures, inspection of
preventive maintenance and
supply chain
modeling. The disadvantage of the program is that
it is commercial, freely available is only for a
limited time.
In paper [3] we investigated possibilities
of simulation renewal and maintenance using Petri
nets. We tried to experiment with open source
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software Snoopy and Pipe2. It was found out that
their manageability depends on considerable
knowledge of simulation technology. Also this led
us to choose a simulation tool Insight Maker [4].
Condition for work with the program is to create a
user account. Created simulations can be saved or
published. Published simulation can be consulted
with other users of the program. The current area of
simulation program is wide. It is possible to find
published simulations of the economy, banking,
healthcare, supply theory, solutions to production
problems, etc. It is also possible to create groups by
topic of interest where new users can participate.
The simulation tool also offers tutorials for quick
familiarization with the program. The big
advantage is that if we do not have much
experience with this program, we can refer to
experienced users (forum, e-mail) when necessary.
Insight Maker is a useful tool especially for those
who work with Excel or other spreadsheets. [7]
The simulation outputs can be exported in the form
of *.csv and then can be worked on in this format.
THE SIMULATION MODEL MAINTENANCE
AND REPAIR OF MACHINES
We demostrate the simulation tool on
illustrated example. We limited for just basic
elements in Insight Maker, which are necessary for
the simulation of dynamic systems.
The formulation of the problem
Let us consider a situation where it is
necessary to dynamically simulate activities during
maintenance and repair of machines in continuous
work in given monitored period:
It is given the number of repairmen that
is necessary for carrying out regular maintenance of
machine and to eliminate the failures. One person
per day is allocated to maintenance of machines and
he will carry it out at regular scheduled intervals at
known time. If the worker does not have scheduled
maintenance, is inactive unless a failure occurs.
Workers are mutually substitutable. No rest break
for worker is considered. The failures are
eliminated as soon as possible after the occurrence,
and it is known intensity of their occurrence and the
average time for elimination (random occurrence).
It is considered such strategy between cooperation
activities of maintenance and repair that elimination
of failures takes precedence over preventive
maintenance, which may be excluded. The
simulation results should be time development
indicators:

number of free repairmen,
Transfer inovácií 31/2015


number of implemented repairs of
machines,
number of preventive maintenances of
machines.
2015
machines, we have to multiply the intensity by this
number.
In the simulation we have chosen
monitored period - 10 days i.e. 240 hours, the
number of repairmen - 5, number of machines - 10,
scheduled interval of preventative maintenance - 4
days (96 hrs.) taking 3 hours, the failure occurence
is generated via Poisson flow with an average gap
between failures - 5 days (120 hrs.), the average
time to elimination of failure is 3 hrs. and it is
modelled by exponential distribution. We see that it
is good to measure time in hours.
1. MODEL OF MAINTENANCE
Fig. 2 Simulation diagram of repair model
Now we can unify these two independent models
into one.
Fig. 1 Simulation diagram of maintenance model
At first we will create a simple
deterministic model of maintenance with one
maintenance man. We need for it two elements
called Primitive in Insight Maker, that is Stock and
Variable . On Fig. 1, we have a diagram that
contains Maintenance such as warehouse where the
failures are cummulated and Interval indicating the
time between two immediately following
requirements for maintenance. In the variable
Interval we have stored constant the size of 96 hrs.
And in variable Scheduled time we have stored
constant 3 hrs. Time calculation of maintenance is a
little bit more difficult. We need to actually achieve
a state, where after each multiple of interval of
maintenance, maintenance of the machine was
carried out. After the simulation we obtain
development of maintenance of one machine,
maintenance is repeated after 96 hours at a time
0,96,192 in range of 3 hours. As we can see, in that
deterministic model the difficulty of its creation is
focused on proposal of diagram graph of primitives
with flows and information links (arrows - oriented
edges) between them.
2. MODEL OF REPAIR
We have stochastic flow of machine
failures in case of machine repair, time on their
elimination is constant for a start. Input flow of
failures on Fig.2 would be for one machine
determined by Poisson distribution with intensity
equal to the inverted value of average length of
gaps between failures, that we have stored in
variable Average gap. But since we have 10
3. MAINTENANCE AND REPAIR MODEL
In this model we will unify maintenance
with repair and we will additionally assume that
also time repair of machine is stochastic and it is
modelled by exponential distribution.
Fig. 3 Simulation diagram of maintenance and
repair model
Maintenance is here affected by number
of machines on which is necessary to carry out
preventive maintenance and by the number of free
repairmen. The most difficult place in the diagram
on Fig.3 is probably condition in flow, where
maintenance started. Performing maintenance is
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Transfer inovácií 31/2015
conditionally depends on the existence of at least
one free repairman at the scheduled time of
preventive maintenance. Modelling exponential
time of service is analogical to modelling input
flow of failures. After the simulation we get graph
of the development required indicators of activities,
shown on Fig.4.
Fig. 4 Development of monitored indicators of
maintenance and repair model
Mentioned graph of the development
indicators can be imported to a file in table form
and then statistically processed.
CONCLUSION
In this paper we deal with only with
dynamic modelling of maintenance and repair of
machines at priority repair of machine against
maintenance. In future work, we plan to focus on
analysis of the various strategies of coordination of
these activities in terms of economic efficiency. In
case of incorporation of reliability indicators of
machines it will be needed to familiarize also with
simulation tools based on agents, which puts even
greater requirements on users. First experience with
the simulation tool Insight Maker persuaded us that
it is very good choice for users with minimal
programming knowledge. We appreciate the
amount of available examples on the internet from
which inspiration may be drawn, as well as friendly
forum of Insight Maker users. The fact that it is a
web application independent on the web browser
will please not only beginners.
References
[1] http://Vensim.com (cit. 27.6.2015)
[2] Honkanen,
T.:
Modelling
Industrial
Maintenance Systems and the Effects of
Automatic
Condition
Monitoring},
dissertation,
Helsinki
University
of
96
2015
Technology, Information and Computer
Systems in Automation, Espoo, 2004, 122p.
[3] Pešková, A.: Modelling Maintenance and
Renewal in Petri Nets, In: Applied Mechanics
and Materials: ROBTEP 2012, 11th
International Conference on Idndustrial,
Service and Humanoid Robotics, Štrbské
Pleso, Slovakia, 14-16 November 2012, Vol.
282 (2013), pp. 282-286, ISBN 978303785603-1, ISSN 1660-9336.
[4] Roe, S. F.: Insight Maker: A General-purpose
Tool for Web-based Modeling Simulation,
Simulation Modelling Practice and Theory 47,
2014, pp. 28–45.
[5] Kreheľ, R., Straka, Ľ.,
Krenický, T.:
Diagnostics of production systems operation
based on thermal processes evaluation
2013. In: Applied Mechanics and Materials.
Vol. 308, p. 121-126. - ISSN 1660-9336.
[6] Krenický, T., Salokyová, Š., Dobránsky, J. :
Experimentálna analýza v diagnostike
prevádzky technických zariadení, Košice, TU ,
2015, 204 s. ISBN 978-80-553-2004-5.
[7] http://insightmaker.com (cit. 27.6.2015)
This work was supported by Project
VEGA n. 1/0124/15 Research and development
of advanced methods for virtual prototyping of
production machines.