Excellent summary of Monte Carlo simulation as applied to availability
simulation. To add a note of practical application, the technique is used
either at design stage to prove a proposed design is capable of meeting the
required availability target, to optimise the spares level required to meet
the availability target, to model an existing system to identify the
"important" drivers of performance, to model proposed upgrades to an
existing facility. For those intesrested in having a play, go to
www.reliability.com.au to download free demo of Avsim plus. The latest
generation of this product has ability to model plant capacity, and
attribute a set of times to failure to a block which allows the failure
parameters to be assigned directly. This provides an exciting facility where
models built say at design phase can be updated throughout its life-either
manually or automatically from a CMMS (providing the facility skills its
people in Root Cause Analysis). This latest trend moves asset managers and
maintenance specialists towards providing "Enterprise Reliability
Management" ie a dynamic environment to compliment "Enterprise Asset
Management" to assist organisations make decisons towards optimum
contribution from their assets to the business.

The impact for traditional maintenance specialists is that there is now an
environment where their decisions using judgement, knowledge, expert
applications such as RCM, RCA, etc can be simulated over a lifetime, and the
likely impact on the business evaluated.

Who is actually using this technology? Defence projects, power generation,
petrochem, refining, water utilities, mining, to name a few.

Thanks Peter for your time to set down your description.

Regards

Mick Drew




----- Original Message -----
From: Peter Ball <pgball@...>
To: <plantmaint@...>
Sent: Thursday, July 19, 2001 8:07 AM
Subject: Re: [plantmaint] Manning ratio : Monte Carlo


> Ron,
>
> The following may assist:
>
> Monte Carlo simulation
> When it is necessary to investigate more complicated operation and failure
> patterns, or detailed aspects of equipment repair, such as spares holding,
> delays before repair can start, or priorities where there are repair
> resource limitations, the mathematical analysis can be extremely difficult
> or impossible to solve.
>
> Monte Carlo simulation is a method which can be used to bypass the complex
> mathematics of an analytical solution. It can only be used effectively
with
> a computer and was, at one time, considered too expensive to use
routinely.
> However, with the advent of the microcomputer and the appearance of Monte
> Carlo simulation programmes on the market, the technique is becoming more
> accessible.
>
> The technique is to generate a computer model of the system to be
> investigated, and then to simulate the operation of the system for a
> predetermined period, during which random failures and repairs can occur
to
> the components of the system. The operational states which the system
takes
> up as a result of each failure or repair (or other event) are logged, and
> from the percentages of time spent in each state, the overall system
> availability can be calculated and other useful information may be
inferred.
>
> a) The computer model - The computer model is usually based on a
Reliability
> Block Diagram (RBD) of the system, and controlled by a set of rules which
> specify exactly the model's response to each type of event which can
occur.
> Each block in the RBD (which can represent a component or group of
> components, or even one aspect of a single component), can be assigned an
> individual Failure Rate, Repair Rate, and number of spares available. The
> set of rules specifies such details as which blocks (components) have to
be
> 'taken out of service' if a block fails, which other blocks have to be
'put
> into service', which repair strategy is put into practice, for example,
> whether the 'component' is to be repaired in situ or changed for a spare,
> and whether an exchanged component should be scrapped or refurbished, and
> the effect of the block failure on the percentage throughput of the whole
> system. For example, if a system model includes two 100 per cent
throughput
> feed pumps in parallel, and each pump set is treated as four components in
> series (that is, high pressure pump, booster pump, electric motor, and
> balance of plant) the rules will probably require that if the electric
motor
> of the running pump fails, the remaining components of the running pump
are
> shut down and the standby pump is started. At the same time the rules will
> initiate the repair of the failed motor, perhaps replacing it with a spare
> if one is available and to do so would be quicker than repairing it in
situ.
> In this case, if the standby pump is permitted to start without failure,
the
> availability of the whole system model is unaffected. Further rules are
> required to determine whether the failed pump is returned to service as
soon
> as it has been repaired or remains as standby until the other pump fails.
> b) Simulation of events - When the model has been set up in the
computer,
> it is run by simulating sequences of events (failures, repairs,
consumption
> of spares, and so on) which occur independently to each component of the
> model. For the model to mimic the real system properly, the events must
> occur at intervals related to those which could occur in the real system.
> If, for instance, a real component has an MTBF of 3000 hours and the
> distribution of the times to failure is known (say, exponential
> distribution), then the set of times to failure which occur to that
> component in the model must be drawn from that distribution and must
> represent an MTBF which approximates to 3000 hours. The computer must,
> therefore, generate an independent series of random times for each
component
> parameter, to suit the specified mean times and distributions. Unlike
Markov
> analysis, the Monte Carlo simulation method is not restricted to use of
the
> exponential distribution, but can simulate times drawn from any
distribution
> which seems appropriate (for example, Weibull, log-normal, rectangular,
and
> so on). When the simulation is set in motion, failures, repairs, and such
> like occur to the components of the model at the times specified by the
set
> of random time series and controlled by the model's set of rules. The
state
> of each component is logged after either each unit time interval or each
> change of state of any component. The length of the simulation may be
> expressed in terms of the time for a specified number of failures to occur
> in the model (perhaps several thousand) or the time for a number of cycles
> of specified length, for example, the time between major overhauls or the
> complete life cycle. Because the model can run so much faster in the
> computer than a system in real time, very long runs are possible. In
> general, the longer the run, the closer the sets of random time series
will
> approximate to the desired distributions, and the closer the overall
system
> availability will approach a steady-state value.
> c) Results - At the end of the simulation, the programme totals the time
> spent by each component in its running, standby, and failed states, and
the
> time spent by the overall system in all its possible states, from which
the
> overall system availability can be calculated. Other subsidiary
information
> can also be obtained, such as the number of times each component failed,
or
> standby plant was called to start, the number of times components were
> exchanged for spares, and whether there would have been advantage in
having
> more spares available.
>
> The above description is not of a specific computer programme but shows,
> rather, how a Monte Carlo simulation can be carried out and some of the
> features which may be found in programs. Not all commercially-available
> programmes will have all the features in this description, but may have
some
> additional features.
>
> Monte Carlo simulation is a powerful technique, which is capable of
> producing an answer to any problem posed by the reliability engineer,
> subject only to the ingenuity of the computer programmer (and the cost of
> their time). However, it is not an analytical technique, and the more
> complex the problem, the more difficult it is to check if the programme
has
> been written correctly and, therefore, if the result can be relied upon.
> Also, the more components and rules in the model, the longer it will have
to
> run in order to achieve a steady-state result. While a substantial
> simulation may be carried out in seconds on a large computer, it can take
> perhaps several hours on a microcomputer, so that there will usually have
to
> be a compromise between computing time and costs and the desire to ensure
> that the steady-state solution has been reached. This is particularly so
> when several runs of the same model are required, with different sets of
> random time series to check consistency, or when the model is run many
times
> with different component parameter values or configurations when searching
> for an optimum design for a real system.
>
> Possibly the above explanation serves to illustrate the marked difference
> that exists between Reliability Engineering, and Reliability Centred
> Maintenance. Especially when other techniques such as Markov Analysis and
> Baye's Theorem are introduced.
>
> Peter B.
> ==========
>
> ----- Original Message -----
> From: Ron Doucet <doucetr@...>
> To: <plantmaint@...>
> Sent: Wednesday, July 18, 2001 2:09 AM
> Subject: Re: [plantmaint] Manning ratio : Monte Carlo
>
>
> > What is Monte Carlo simulation?
> >
> >
> > Ron
> >
> >
> >
> >
> > "Selvarajan Murugan" <selva@...> on 07/17/2001 05:49:31 AM
> >
> > Please respond to plantmaint@...
> >
> > To: plantmaint@...
> > cc: (bcc: Ron Doucet/CR/IOC/North)
> > Subject: [plantmaint] Manning ratio : Monte Carlo
> >
> >
> >
> > I am currently working on factory manning indices
> > and I want some advice on how to go about doing this
> > with the aid of the Monte Carlo simulation.
> > Is there any other simulation or method for doing
> > the operator to machine and technician to machine
> > ratio.
> > Any one out can share with me on the matter.
> >
> > Regards,
> > selva
> >
> >
> >
> > If you ever need to get in contact with the owner of the list, (if you
> have
> > trouble unsubscribing, or have questions about the list itself) send
email
> > to <owner-plantmaint@...> .
> >
> >
> >
> >
> >
> >
> >
> >
> > If you ever need to get in contact with the owner of the list, (if you
> have
> > trouble unsubscribing, or have questions about the list itself) send
email
> > to <owner-plantmaint@...> .
>
>
> If you ever need to get in contact with the owner of the list, (if you
have
> trouble unsubscribing, or have questions about the list itself) send email
> to <owner-plantmaint@...> .


If you ever need to get in contact with the owner of the list, (if you have
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