Introduction to RCM

8/6/2019 Introduction to RCM

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M. Rausand and J. Vatn. Reliability Centered Maintenance. In C. G. Soares, editor, Risk and Reliability in

Marine Technology. Balkema, Holland, 1998

Reliability Centered Maintenance

6800$5> 1, or at least > 1. In special situations wecan even have < 1.

The expected cost per unit time is:

&F F : W

S P

( )( )

=+

where W(t) = E(N(t)) is the expected number of

failures in 0, t].

We will consider a socalled Weibull process

with W(t) = (t). In this case the time from t= 0until the first failure has a Weibull distribution

with survivor functionR(t) = exp(-(t)).

It can be shown that the expected cost per unittime, C(t), is minimized when:

=

1

1

1

provided > 1.

Hence, to optimize the replacement interval,

estimates for the parameters; cm, c

p, and are

required. cm

is the total cost of a minimal repair,

including any harm to material, personnel andenvironment. Assessing a value of cm may

therefore cause controversies. and are theparameters in the failure distribution of the item.

Often it is more convenient to specify the failure

distribution in terms of mean time to failure

(MTTF) and the shape parameter , yielding:

=+

077)

( )1

1

1 1

Table 1 Optimal replacement interval relative to MTTF. For a given value of and , the tableentry should be multiplied with MTTF to give the optimum replacement interval length

Cost ratio = cu/c

p

2 3 4 5 7 10 20 50 100 200

1.2 13.40 12.20 12.95 12.62 2.050 .897 .393 .165 .090 .050

1.5 8.19 7.97 1.22 .85 .590 .432 .253 .133 .083 .052

1.7 6.60 1.59 .83 .66 .503 .389 .247 .141 .093 .061

2.0 4.84 .86 .67 .57 .464 .377 .259 .161 .113 .080

2.5 .99 .71 .60 .54 .461 .394 .294 .202 .152 .115

3.0 .82 .67 .59 .54 .478 .421 .331 .242 .192 .152

4.0 .75 .66 .61 .57 .523 .476 .398 .316 .265 .223

Model 2 Block replacement policy

The block replacement policy describes a

singleunit system put into operation at time t=

0. The unit is replaced at times ktfor k=1,2,...and at failures. The cost of a planned

replacement is denoted cp, and the total cost of


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Rausand&Vatn Reliability Centered Maintenance

20

an unplanned replacement, i.e. a failure is cu. Let

W(t) denote the renewal function, see e.g.

Hyland and Rausand (1994), for the lifetime

distribution of the unit. The average cost per unit

time is:

&F F : W

S X

( )( )

= +

If the times between failures are Weibull

distributed, W(t) can be found by the algorithm

given by Smith and Leadbetter (1963).

Numerical methods are, however, required to

find the optimal interval . In Table 1, numericalvalues for the optimal replacement interval is

given relative to MTTF.

In order to use Table 1 the value of must bespecified. During the data analysis in Step 5 the

value of should have been found by e.g. expertjudgment.

Model 3 - Functional testing

This model is appropriate for scheduled function

testing. Consider a protective device with a

constant failure rate

. A functional test of the

device is performed at times k for k=1,2,. . .The cost of a Functional test is c

t. If a failure is

detected upon a test, the device is replaced at a

cost of cr. Further assume that the device is

demanded with a frequency f, i.e. the rate of

critical situations. A hazardous situation occurs

if the protective device fails upon a demand. The

total cost of such a situation is ch.

The expected cost per unit time is:

&F

F I FW

U K

+

+

2

2 2

yielding an optimal interval :

F

I F F

077) F

I F F 077)

W

K U

W

K U

=

Model 4 - Scheduled on-condition tasks, the

concept of P-F-intervalsThe idea behind a scheduled on-condition task is

that a coming failure is alerted by some

degradation in performance, or some indicator

variable is alerting about the failure.

Time

Target

value

Acceptabledeviation

Failure

Performance/

Condition

Point where we can find out thatv v s h v y v t ("potential failure")

P

F

P-F interval

Figure 10 P-F interval

In Figure 10 the performance is viewed as a

function of time. The point P is the first point

in time where we are able to reveal the outset of

a failure. When the performance is below some

limiting value a failure will occur. The length

from a potential failure is detectable until a

failure occurs is denoted the P-F interval. The

length of the P-F interval is assumed to vary

from time to time, and is therefore modeled as a

random variable. In order to establish an optimal

maintenance interval, , the following quantitiesmust be defined:

' Delay time, i.e. the time from a

potential failure is revealed until an

appropriate corrective action is

completed. For simplicity the delay time

is considered as a deterministic quantity.

FL

: Cost of (manual) inspection.

FX

: Cost of (unplanned) failure.

73 )

: PF interval (random variable).

: E(73 )

) = Mean value of P-F interval.

: SD(7PF) = Standard deviation of P-Finterval.

MTTF:Mean time to failure if no corrective

maintenance is carried out

The expected cost per unit time is given by:

&

F F H 7 W ' GW L X

W

3 )

( )

Pr( )

=+ +

0

where =1/(MTTF-), and we have assumedthat the time from the component is in a perfect

state until a potential failure reveals is


21/27


21

exponentially distributed.

In order to optimize Eq. (1) numerical values are

required for ci, c

u, MTTF,D, and . Numerical

methods are usually required to optimize Eq.

(1). The calculations will be simplified if we

choose a distribution for TPF with a closed formof the cumulative distribution function.

Model 5 - Continuos on-condition tasks

The idea of continuos on-condition monitoring

is to measure one or more indicator variable.

The reading of the component in this manner

can be used to detect a coming failure. The

variable being monitored is denoted X(t) in

Figure 11.

Time

X(t)

"Failure Limit"

Failure

"Action Limit"

Figure 11 Continuos monitoring

In Figure 11 the deteriorating process is shown.

HereX(t) is can be interpreted as the cumulative

damage at time t. When the damage exceeds

some limit, a failure occurs. In Figure 11 we

have also shown an action limit, upon where

to take a maintenance action. The challenge here

is to decide the optimal action limit. No

general approach seems applicable here since thesolution is highly dependent on how X(t) is

modeled. Aven (1992) discusses one method

where an underlying chock model is assumed.

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Two overriding criteria for selecting

maintenance tasks are used in RCM. Each task

selected must meet two requirements:

It must be applicable

It must be effective

Applicability: meaning that the task is applicable

in relation to our reliability knowledge and in

relation to the consequences of failure. If a task

is found based on the preceding analysis, it

should satisfy the Applicability criterion.

A PM task will be applicable if it can eliminate a

failure, or at least reduce the probability of

occurrence to an acceptable level (Hoch 1990) -

or reduce the impact of failures!

Cost-effectiveness: meaning that the task does

not cost more than the failure(s) it is going to

prevent.

The PM task's effectiveness is a measure of how

well it accomplishes that purpose and if it is

worth doing. Clearly, when evaluating the

effectiveness of a task, we are balancing thecost of performing the maintenance with the

cost of not performing it. In this context, we

may refer to the cost as follows (Hoch 1990):

1. The cost of a PM task may include:

the risk of maintenance personnel error,e.g. maintenance introduced failures

the risk of increasing the effect of a failureof another component while the one is outof service

the use and cost of physical resources

the unavailability of physical resourceselsewhere while in use on this task

production unavailability duringmaintenance

unavailability of protective functionsduring maintenance of these

The more maintenance you do the morerisk you will expose your maintenance

personnel to

2. On the other hand, the cost of a failure mayinclude:

the consequences of the failure should itoccur (i.e. loss of production, possible

violation of laws or regulations, reduction

in plant or personnel safety, or damage to

other equipment)

the consequences of not performing thePM task even if a failure does not occur


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22

(i.e., loss of warranty)

increased premiums for emergency repairs(such as overtime, expediting costs, or

high replacement power cost).

Balancing the various cost elements to achieve a

global optimum will always be a challenge. The

conceptual RCM model in Figure 1 may be a

starting point. If such a model could be

established, and the various cost elements

incorporated, the trade-off analysis is reduced to

an optimization problem with a precisely

defined mathematical model.

Often the resources available for the RCM

analysis do not permit building such an overall

model, hence we can not expect to achieve aglobal optimum. Sub-optimization can to some

extent be achieved by simplifying the model in

Figure 1. For example one could consider only

one consequence at a time and/or only one

maintenance task at a time.

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In Step 4 critical items (MSIs) were selected for

further analysis. A remaining question is what to

do with the items which are not analyzed. For

plants already having a maintenance program it

is reasonable to continue this program for the

non-MSIs. If a maintenance program is not in

effect, maintenance should be carried out

according to vendor specifications if they exist,

else no maintenance should be performed. See

Paglia et al (1991). for further discussion.

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A necessary basis for implementing the result of

the RCM analysis is that the organizational and

technical maintenance support functions are

available. A major issue is therefore to ensure

the availability of the maintenance support

functions. The maintenance actions are typically

grouped into maintenance packages, each

package describing what to do, and when to do

it.

As indicated in the outset of this paper, many

accidents are related to maintenance work.

When implementing a maintenance program it is

therefore of vital importance to consider the risk

associated with the execution of the maintenance

work. Checklists could be used to identify

potential risk involved with maintenance work:

Can maintenance people be injured during

the maintenance work?

Is work permit required for execution of themaintenance work?

Are means taken to avoid problems relatedto re-routing, by-passing etc.?

Can failures be introduced duringmaintenance work?

etc.

Task analysis, see e.g. Kirwan & Ainsworth(1992) may be used to reveal the risk involved

with each maintenance job. See Hoch (1990) for

a further discussion on implementing the RCM

analysis results.

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As mentioned earlier, the reliability data wehave access to at the outset of the analysis may

be scarce, or even second to none. In our

opinion, one of the most significant advantages

of RCM is that we systematically analyze and

document the basis for our initial decisions, and,

hence, can better utilize operating experience to

adjust that decision as operating experience data

is collected. The full benefit of RCM is therefore

only achieved when operation and maintenance

experience is fed back into the analysis process.

The process of updating the analysis results is

also important due to the fact that nothing

remain constant, best seen considering the

following arguments (Smith 1993):

The system analysis process is not perfectand requires periodic adjustments.

The plant itself is not a constant sincedesign, equipment and operating procedures

may change over time.

Knowledge grows, both in terms ofunderstanding how the plant equipment

behaves and how technology can increase


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23

availability and reduce costs.

Reliability trends are often measured in terms of

a non-constant ROCOF (rate of occurrence of

failures), see e.g. Hyland & Rausand (1994).

The ROCOF measures the probability of failure

as a function of calendar time, or global time

since the plant was put into operation. The

ROCOF may change over time, but within one

cycle the ROCOF is assumed to be constant.

This means that analysis updates should be so

frequent that the ROCOF is fairly constant

within one period.

Opposite to the ROCOF, the failure rate or

FOM, is measuring the probability of failure as a

function oflocal time, i.e. the time elapsed since

last repair/replacement. However, the FOM can

not be considered constant, if so there is no

rationale for performing scheduled replace-

ment/repair.

The updating process should be concentrated on

three major time perspectives (Sandtorv &

Rausand 1991):

Short term interval adjustments

Medium term task evaluation

Long term revision of the initial strategyThe short term update can be considered as a

revision of previous analysis results. The input

to such an analysis is updated reliability figures

either due to more data, or updated data because

of reliability trends. This analysis should not

require much resources, as the framework for the

analysis is already established. Only Step 5 and

Step 8 in the RCM process will be affected by

short term updates.

The medium term update will also review thebasis for the selection of maintenance actions in

Step 7. Analysis of maintenance experience may

identify significant failure causes not considered

in the initial analysis, requiring an updated

FMECA analysis in Step 6. The medium term

update therefore affects Step 5 to 8.

The long term revision will consider all steps in

the analysis. It is not sufficient to consider only

the system being analyzed, it is required to

consider the entire plant with it's relations to theoutside world, e.g. contractual considerations,

new laws regulating environmental protection

etc.

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&21&/86,216

The following summarizes some main benefits,

drawbacks and problems encountered duringapplication of the RCM method in some

offshore case studies.

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Cross-discipline utilization of knowledge: To

fully utilize the benefits of the RCM concept,

one needs contributions from a wider scope of

disciplines than what is common practice. This

means that an RCM analysis requirescontribution from the three following discipline

categories working closely together:

1. System/reliability analyst

2. Maintenance/operation specialist

3. Designer/manufacturer

All these categories do not need to take part in

the analysis on a full time engagement. They

should, however, be deeply involved in the

process during pre- and post-analysis review

meetings, and quality review of final results.

The result of this is that knowledge is extracted

and commingled across traditional discipline

borders. It may, however, cost more at the outset

to engage all these personnel categories.

Traceability of decisions: Traditionally, PM

programs tend to be cemented. After some

time one hardly knows on what basis the initial

decisions were made and therefore do not want

to change those decisions. In the RCM conceptall decisions are taken based on a set of

analytical steps, all of which should be

documented in the analysis. When operating

experience accumulates, one may go back and

see on what basis the initial decisions were

taken, and adjust the tasks and intervals as

required based on the operating experience. This

is especially important for initial decisions based

on scarce data.

Recruitment of skilled personnel formaintenance planning and execution: The RCM

way of planning and updating maintenance


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24

requires more professional skills, and is

therefore a greater challenge for skilled

engineers. It also provides the engineers with a

broader and more attractive way of working with

maintenance than what sometimes is common

today.

Cost aspects: As indicated, RCM will require

more efforts both in skills and manhours when

first being introduced in a company. It is,

however, documented by many companies and

organizations that the long term benefits will far

outweigh the initial extra costs. One problem is

that the return of investment has to be looked

upon in a long term perspective, something that

the management is not always willing to take a

chance on.

Benefits related to PM-program achievement:

Based on the case studies we have carried out,

and experience published by others, the general

achievements of RCM in relation to a traditional

PM-programs may be summarized as follows:

By careful analysis of the failureconsequences, the amount of PM tasks can

often be reduced, or replaced by corrective

tasks or more dedicated tasks. We have

therefore chosen to include corrective

maintenance as a possible outcome of theRCM analysis.

Emphasis has been changed from periodicrework or overhaul tasks of the large

assemblies/units to more dedicated object

oriented tasks. Consequently, condition

monitoring has been more frequently used to

detect specific failure modes.

Requirement for spare parts has been

reduced as a result of a better justificationfor replacements.

Design solutions have been discovered thatwere not optimal from a safety and plant

economic point of view.

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Identification of Maintenance Significant Items:

In some cases there may be very little to achieve

by limiting the analysis to only include the

MSIs. Smith (1993) argues that concentrating on

critical components (MSIs) is directly wrong

and that it in most analyses exclude important

equipment from appropriate attention. He writes

(page 82):

. . . we should be very careful

not to prematurely discard

components as non-critical until

we have truly identified their

proper tie and priority status to

the functions and functional

failures.

Other authors argue that the main objective of

the RCM process is to create a basis for

maintenance evaluation and task adjustment.

The selection of MSIs will reduce this basis and

result in an insufficient evaluation process.

The rationale for working with the MSIs onlywas to reduce the analysis work. Thus there is

always a risk of an insufficient analysis when

the non-MSIs are not subjected to a formal

analysis. In our presentation the criterion for

classifying an analysis item as non-MSI is:

The item should not affect any of the critical

system failure modes (Step 4). By using such

an approach the criterion for disregarding an

item is traceable, and may be reevaluated later.

Further we believe that this criterion makes very

good sense.

Lack of reliability data: As indicated the full

benefit of the RCM concept can only be

achieved when we have access to reliability data

for the items being analyzed. Is now RCM

worthless if we have no or very poor data at the

outset? The answer to this question is no, even

in this case the RCM approach will provide

some useful information for assessing

maintenance tasks. PM intervals will, however,

not be available. As a result of the analysis, weshould at least have identified the following:

We know whether the failure involves asafety hazard to personnel, environment or

equipment

We know whether the failure affectsproduction availability

We know whether the failure is evident orhidden

We have a better criterion for evaluatingcost-effectiveness


25/27


25

Lack of reliability data will always be a

problem. First of all there are problems with

getting access to operational data with sufficient

quality. Next, even if we have data, it is not

straight-forward to obtain reliability data from

the operational data. Before we discuss some

problems with collecting and using operationaldata, it should be emphasized that there will

never be a complete lack of reliability figures.

Even if no operational data is available, expert

judgment will be available. However, the

uncertainty in the reliability figures can be very

large.

Based on our various engagements in the

OREDA project and other data collection

projects on offshore installations, we have

experienced the following common difficultiesrelated to acquisition of failure data:

Data is generally very repair oriented and notdirected towards describing failure causes,

modes and effects.

How the failure was detected is rarely stated(e.g. by inspection, monitoring, PM, tests,

casual observation). This information is very

useful in order to select applicable tasks.

Failure modes can sometimes be deduced,but this is generally left to the data collectorto interpret.

The true failure cause is rarely found, but thefailure symptom can to some extent be

traced.

Failure effect on the lower indenture level isreasonably well described, but may often be

missing on higher indenture level (system

level).

Operating conditions when the failureoccurred is frequently missing or vaguely

stated.

As mentioned above, there are often problems

with estimating reliability data from the

operational data. Reliability data comprises

MTTR and MTTF figures together with the

failure rate function. Reasonable estimates for

MTTR and MTTF may be found by various

averaging techniques. The failure rate function,

i.e. the ageing parameter is much harder to

obtain. Available estimation techniques require

no reliability trend (in calendar time) for the

unit(s) being considered. Further if several units

are used to enlarge the data set, these units

should be operated identically under the same

environmental conditions. The requirements

above are very seldom fulfilled, hence the

estimation techniques may collapse. We

therefore recommend use ofexpert judgment toestablish appropriate ageing parameters. The

ageing parameter is a measure of how

deterministic a failure is, and it is reasonable to

believe that this measure is relatively constant

for each failure cause. On the other hand, it

seems meaningless to establish a general set of

recommended MTTF-figures for the various

failure mechanisms.

Trade-off analyses: There are four major criteria

for the assessment of the consequences of afailure: safety, environment, production

availability, and economic losses. During the

analysis, we have to quantify these measures to

some extent to be able to use them as decision

criteria. Further, a trade-off analysis is required

to balance each means against the different

consequences. Referring to Figure 1 we need to

consider the effect of the maintenance tasks

M1,M

2,.., on the consequences C

1,C

2,..,. This will

require comprehensive reliability models.

Further, the transformation of the consequences

C1,C

2,.., into a unidimensional loss function is at

best a difficult and controversial task. A

framework for dealing with these problems is

given in Vatn et al. 1996.

Assessing proper interval: The RCM concept is

very valuable in assessing the proper type of PM

task, but traditionally RCM does not basically

include any tool for deciding optimal

intervals. The "new framework for RCM given

in Figure 1 together with standard PM-modelslisted under Step 8 are believed to form a very

sound basis for deciding optimal intervals.

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RCM is not a simple and straightforward way of

optimizing maintenance, but ensures that one

does not jump to conclusions before all the right

questions are asked and answers given. RCMcan in many respects be compared with Quality

Assurance. By rephrasing the definition of QA,

RCM can be defined


26/27


26

All systematic actions

required to plan and verify

that the efforts spent on

preventive maintenance are

applicable and cost-effective.

Thus, RCM does not contain any basically new

method. Rather, RCM is a more structured way

of utilizing the best of several methods and

disciplines. Quoting Malik (1990) the author

postulates: . . . there is more isolation between

practitioners of maintenance and the

researchers than in any other professional

activity. We see the RCM concept as a way to

reduce this isolation by closing the gap

between the traditionally more design related

reliability methods, and the practical related

operating and maintenance personnel.

5()(5(1&(6

R. T. Anderson and L. Neri. Reliability-Centered

Maintenance. Management and Engineering

Methods. Elsevier Applied Science, London,

1990.

T. Aven. Reliability and Risk Analysis. Elsevier

Science Publishers, London, 1992.K. M. Blache and A. B. Shrivastava. Defining

failure of manufacturing machinery &

equipment. Proceedings Annual Reliability

and Maintainability Symposium, pages 69-

75, 1994.

B. S. Blanchard and W. J. Fabrycky. System

Engineering and Analysis. Prentice-Hall,

Inc., Englewood Cliffs, New Jersey 07632,

1981.

BS 5760-5. Reliability of systems, equipments and

components; Part 5: Guide to failure modes,effects and criticality analysis (FMEA and

FMECA). British Standards Institution,

London, 1991.

N. Cross. Engineering Design Methods: Strategies

for Product Design. John Wiley & Sons,

Chichester, 1994.

R. R. Hoch. A Practical Application of Reliability

Centered Maintenance. The American

Society of Mechanical Engineers, 90-

JPGC/Pwr-51, Joint ASME/IEEE Power

Gen. Conf., Boston, MA, 21-25 Oct., 1990.A. Hyland and M. Rausand. Reliability Theory;

Models and Statistical Methods. John Wiley

& Sons, New York, 1994.

IEC 50(191). International Electrotechnical

Vocabulary (IEV) - Chapter 191 -

Dependability and quality of service.

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Geneva, 1990.

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1985.

B. Kirwan and L. K. Ainsworth. A Guide to Task

Analysis. Taylor & Francis, London, 1992.

M.A. Malik. Reliable preventive maintenance

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M. A. Moss. Designing for Minimal Maintenance

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Summer Nuclear Generating Station. 4th

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Introduction to RCM

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