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Maintaining asset’s desired availability can be a daunting
task but not anymore (Part 1): A reliability approach
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
PAGE 2 OF 23
List of Contents
Summary 3
Introduction 4
What is Reliability Centred Maintenance (RCM)? 5
Reliability Centred Maintenance Model Deliverables 6
RCM Process 8
Reliability Engineering: Failure Model 10
Flexing PDM Inspection Frequency 13
Performance based Partnership 17
Virtual Tools – Performance and Data Analysis 20
Prognostic and diagnostic tools 21
Conclusion 22
References 23
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
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Summary
This paper addresses some of the key disadvantages associated with conventional
calendar based maintenance, where an average client spends an extensive amount of
money on asset maintenance and in return the existing calendar based maintenance
model often offers poor visibility on asset’s operational status, availability and
savings. With very few options facility owners often seek to reduce their spending on
maintenance but without the appropriate performance indicators clients may face a
potential risk of losing visibility on their life critical assets operating conditions which
can often lead to unscheduled downtime, poor availability and surge in maintenance
cost.
Uptime Plus proposes a maintenance model which is an adaptation of both Reliability
Centred Maintenance and Performance based partnership model [1],[3],[5] aimed to
overcome the major disadvantages of conventional calendar based maintenance and
provide a cost effective solution to improve asset’s reliability, efficiency and most
importantly identifies Key Performance Indicators (KPIs). With the aid of condition and
performance monitoring tools clients can now have a broader spectrum of their
asset’s operational status consequently mitigating any potential failures at an early
stage where the cost of intervention is less.
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
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Introduction
Infrastructure maintenance industry has
seen a tremendous growth over the span of
10 years, where the introduction of new
technologies have paved a way for clients to
increase their life critical asset’s reliability
and efficiency, but the cost of maintaining a
critical asset at a desired availability is still
high due to hours and the labour required to
perform the necessary maintenance tasks
and quite often clients and service providers
lose visibility of assets operational status
and other key parameters (reliability,
probability of failures, availability, failure rate
and Mean Time Failures). Considering the
recent economical climate, clients are often
forced to resort to more conservative
policies that would enable them to reduce
their spending on maintenance and label
some of the risks caused due to lack of
maintenance as “Accepted Risk” but in
reality it has an indirect impact on the
engineering resilience of the facility.
In addition to that, identifying spares for the
critical assets is a task that challenges both
parties (client/ service provider), where a
huge sum of money is spent on buying
those spares without any information on
asset’s operational status, In most cases the
acquired spares are either kept on site or at
a safe house, quite prepared for the
impending failures. It seems logical and can
be deemed as “Common Sense”, the
questions that needs to be answered is
“Does the asset really need a spare at this
point in time?” and “what happens if that
asset or some of its components becomes
obsolete?” Oversized inventory can lead to
insufficient use of the capital and can cause
serious impact on savings.
Clients can also have a tough time in
making decision regarding when to replace
an asset as in most cases they don’t have
any site/field information that tells them how
well their critical asset is currently
performing, and usually in the facility
maintenance industry asset replacement is
carried out on “better safe than sorry”
principle, which again sounds appropriate
but the policy is not suitable for all assets.
The primary objective for any maintenance
policy is to let the user know that the
deployed policies and procedures have
actually helped an asset to reach or extend
its documented life expectancy and maintain
the desired availability throughout its service
life.
Unscheduled downtime is the single
parameter that every individual in the
industry tries to avoid, every firm who offers
critical infrastructures services have their
own proactive measures to mitigate the
failures but only a handful of them
acknowledges the facts that unscheduled
downtimes are inevitable and reliability
studies have shown over 20% of asset
failures are age related. It is imperative to
clearly understand the deployed policies &
procedures should focus on finding the
optimum inspection interval. The traditional
approach towards this issue is performing
preventive inspections/tasks at predefined
frequencies (i.e. Monthly, six monthly,
yearly), now that sounds like the right option
but what if the actual maintenance activity is
itself the root cause of the asset failure? In
FM these failures are known as
“Maintenance Induced Failures*”. [See footnote]
*Maintenance Induced Failure: Type of failure occurs when a maintenance technician performs an intrusive inspection or service on equipment and induces or causes a failure.
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
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What is Reliability Centred Maintenance (RCM)?
The term “Reliability Centred Maintenance”
has various definitions and the most suitable
one is defined as “The process used to
determine the maintenance requirements of
any physical asset in its operating context”
[1], In simple words, it means the
maintenance tasks are performed only when
its required by identifying failure modes for
the particular asset and collating ages to
failure data to determine Predictive (PdM)
and Preventive (PM) inspection intervals. It
is a method that identifies applicable and
effective maintenance tasks required to
maintain the inherent reliability of an asset
with minimum cost.
This methodology has been widely
acknowledged in the process industries,
hospitals and by the aviation manufacturers
where reliability and business continuity is
the life line for these industries and any
unscheduled downtime can cause serious
financial or health & safety impact. In an
ideal world, RCM should be a key process in
facility maintenance (FM) industry but
unfortunately FMs perspective towards RCM
is not very optimistic as it has been
classified as a “complex” process with too
many “variables” involved and the most
common response for not leaning towards
this methodology is the myths that
surrounds around RCM.
Myth #1: “Operational cost is HIGH
during RCM implementation”
The answer is YES, but it is a one off cost,
in RCM world this surge in the operational
cost is called “Start up Cost” which is
caused during hardware acquisition process
i.e. buying the tools and setting up training
required for the engineers to implement and
maintain the desired RCM standards. It has
been proven and acknowledged by the
reliability engineering community, the Return
on Investment (ROI) from RCM is on
average between 25%-30% [1]
Myth #2:“Sounds good in theory but in
reality performing less maintenance in a
critical assets poses threat to asset’s
operational efficiency and it is vulnerable
to failures”
Surprisingly NO, over 80% of asset failures
are not due to age therefore performing
conventional calendar based maintenance,
replacements or overhauls do not increase
asset’s reliability, In addition to that
performing calendar based maintenance
might increase the risk of maintenance
induced failures which are often hidden.
Failure rate of an asset subjected to RCM is
far less compared to an asset that
undergoes conventional calendar based
maintenance because sometimes “Less is
more”.
Myth #3: “Replacing PPM tasks with
predictive inspections has a negative
impact on the maintenance model”
Performing predictive tasks does not replace
the original preventive tasks; it is an efficient
decisive method that allows engineers to
identify when to perform the specified
intrusive maintenance.
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
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Reliability Centred Maintenance: Deliverables
Maintenance models are basically devised
to improve the operational efficiency of
assets, reduce downtime and enable facility
managers to allocate their resources more
efficiently by providing clear visibility on
asset’s KPIs, i.e. how well they are
performing at any given state?, Does
conventional calendar based maintenance
model deliver these aspects?
Sadly no, all it does is carry out tasks at a
regular interval i.e. constantly intervening
with the asset, and creates an optimistic
view that “failures are reduced or eliminated
because maintenance was carried out
before any failures could occur”, the
statement above is not aimed to dismiss
preventive maintenance strategy (PM) and
say “It is all wrong”. PM is the most essential
aspect in asset maintenance and its full
efficiency can be only achieved, if it’s utilised
in part. Constantly intervening with an asset
acts as a catalyst during asset deterioration
process and in most cases makes assets
prone to premature failures.
The proposed maintenance model is a
fusion of performance based partnership
model [refer section 3] and reliability centred
maintenance, where the latter is used to
identify the failure modes, key performance
indicators and reliability parameters via ages
to failure data and the former is used to
identify the minimum maintenance
conditions and the PM and Pdm tasks
intervals to meet its specified performance
level. By combining the two maintenance
models, some of the major disadvantages of
performance based partnership approach
such as loss of flexibility and the ability to
deal with changes is mitigated as the model
is devised to evolve constantly based on its
performance.
Ages to failure Data
FMEA
Predicitve
Inspection
PPM
RCM
Performance
Availability
SavingsReduced
Downtime
Reliability
parameters
Figure 1: Graphical representation of RCM inputs and service outputs.
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The model allows the user to identify Key
Performance Indicators (KPIs) on critical
assets by identifying the possible causes
that could affect the specified KPIs. For
example, consider an 11KV transformer
required to be kept at an availability of
99.98%, the first step in identifying the KPI is
to perform FMEA analysis [8] and identify
the possible failure modes that can occur in
that transformer based on its current
operational context. Once failures modes
are identified, select appropriate
maintenance tasks (PdM, PM) to be
performed at appropriate intervals. In this
case, KPIs for an 11KV transformer will be
the secondary voltage (tolerance of ±5%),
cooling, winding temperature and Insulation.
Based on these parameters a minimum
operating condition can be devised which
provides a clear objective for the engineers
that would allow them to address some of
the key questions:
- How the asset should be
maintained?
- The level of maintenance required?
- What are Key Performance
Indicators (KPIs) to be monitored?
- What are the potential causes that
could affect the specified KPIs?
The model is aimed to
- Improve the performance of critical
assets
- Increase asset availability and
reliability
- Reduce asset downtime
- Increase cost savings
- Optimise asset replacement
strategy
- Identify hidden failures and monitor
current use of time and resources
Figure 2: Typical Performance monitoring chart
Asset No: TX2123355
0
20
40
60
80
100
Month
Performance Scale
2010
2011
2012
2010 50 50 50 80 80 80 90 90 90 90 90 90
2011 70 70 70 90 90 90 70 70 70 90 90 60
2012 90 90 90 80 80 80 60 60 60 60 60 50
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Threshold limit-70
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RCM Process
A. Asset classification
Asset classification is the most essential
process in implementing RCM, where it
needs to be thorough and assets should be
classified in any one of the two types. The
whole idea behind this process is to use the
time and resource efficiently and clearly
identify level of maintenance tasks required
based on asset’s criticality (Business and
Functional).
Type Criteria
Critical 1-1 Important to business function and continuity where
the user can’t afford for unscheduled downtimes.
1-2 Asset’s failure can induced failures to other critical
asset connected to it.
Non
Critical
2-1 Does not pose serious threat to business continuity
and does not incur financial loss.
2-2 The user can afford for unscheduled downtime.
2-3 Assets with random failures*
2-4 User can afford run to fail.*
Table 1: Asset Classification Criteria
B. Failure Modes and Effects Analysis (FMEA)
Failure Modes and effects analysis (FMEA)
is a form of reliability study that identifies
possible failure modes in an asset which in
turn enables the engineers to decide the
appropriate maintenance tasks that can be
of predictive or preventive in nature that
would enable them to mitigate possible
failures modes.
- Failure Modes - (what could go
wrong?)
- Cause (what could cause those
failure modes?)
- Effects - (what is the consequence?)
Once these elements are identified, each
failure mode will be rated from [1 – 10] for
their Severity, Likelihood of Occurrence and
Likelihood of Detection based on asset
history & available condition monitoring
tools, then the Risk Priority Number (RPN)
can be calculated based on eq (1), The RPN
provides a clear indication on failure modes
that are critical and has high probability of
occurrence based on the scale mentioned in
table [2]
RPN = Severity x Occurrence x Detection ………………………………… Eq (1)
Scale Status
RPN = 0- 25
RPN = 26- 125
RPN>125
Table 2: Generic RPN scale
*- Assets with random failure patterns, where it is no longer is cost effective to maintain it can also be classified as rogue asset
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C. Failure Pots
The term “Failure Pots” refers to ages to
failure data, which is crucial for calculating
the failure distribution and reliability
parameters. The process of collating ages to
failures data is a continuous process and
accurate predictions can be obtained if it’s
constantly updated and returned to the RCM
facilitator on a monthly basis.
Why do we need it?
Every failure has a pattern; in order to
identify the failure pattern the engineers
should have the visibility of “when the asset
failed?”, “what caused the failure?” and
“number of occurrences?” The possible
cause of failures can be identified by
performing FMEA analysis
Asset PUMP#23 Function Pumps cold water to the chillers 2 & 3
Failure modes F1 F2 F3 F4 F5 F6
Number of Failures 2 0 1 1 3 0
Time (hours) 25000 33000 37000 37500 23900 50000
Table 3: Typical Ages to failure data
The above table contains sample ages to
failure data of a pump, where F1 to F6
represents the failure modes as mentioned
in the table [4] and time (hours) indicates the
time those failures were detected or
occurred, and the specified failures modes
are not limited since FMEA is meant to be a
continuous process
.
Failure Modes Causes
F1 Damaged Impeller
F2 Bearings
F3 Cavitations / Clogged suction pipe
F4 Excessive Loads, Overheating, Lubricant failure, corrosion
F5 Excessive vibration
F6 Age
Table 4: Typical Failure Modes
Failure Distribution
0
10000
20000
30000
40000
50000
60000
2 1 1 1 3 1
Number of Failures
Ho
urs No.of.failures
Failure Modes
Figure 2: Typical Failure distribution
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
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Reliability Engineering: Failure Model
Calculating reliability parameters and
predicting asset failures [2] is often deemed
as “A time consuming” process but in reality
it is not that hard once the process for
acquiring ages to failure (i.e. raw data) has
been laid out. The two key parameters
required to perform failure predictions are
the number of failures and the time it was
detected or occurred. The process is aimed
to shed some light on following the
questions:
- When an asset is going to fail?
- What is time interval between two
successive failures for a particular
asset?
- What is the reliability of the asset?
- How much time do I have to perform
the remedial actions?
Failure Rate (λ):
A failure rate is the ratio between number of
failures occurred and the time at which they
were detected, it is usually denoted in
failures per year, it is crucial to understand
what is a failure* and what are the
assumptions?, [see footnote]
λ=R/ T…….. Eq (2)
Where,
R – Number of failures
T – Sample time or Operational time
it was detected
Mean Time between Failures (MTBF)
It is one of the most misunderstood
variables in RCM, as it is often confused
with assets life expectancy. It is defined as
the mean or average time between two
successive failures. The simplest method to
calculate MTBF is mentioned below,
MTBF=1/λ…Eq (3)
Where, λ – Failure rate, refer [eq (2)]
Failures
Average time between two successive failures
FA MTBF FB
Time
Figure 3: Showing Interval between two successive failures FA and FB respectively *The term “Failure” is defined as the termination of the ability of the asset as whole to perform its required
function, termination of the ability of any individual component to perform its required function but not the
termination oft he ability of the asset as whole to perform.
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
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Reliability [R (T)]
It is the measure of resistance to failure of
an asset and it is directly proportional to
MTBF or failure rate
R(t)=e–t/MTBF
…Eq(4)
Figure 4: Snap shot of reliability parameters dialog box from the Predictive Maintenance
Management (PMM) tool
Probability of Failure [F (t)]
It is a measure that indicates the
unreliability of an asset based on its failure
data, the output basically denotes whether
the likelihood of failures will increase or
decrease at any given time.
F (t) = 1- R (t)….Eq (5)
Annual Failure Rate (AFR)
This parameter calculates failure rate for a
group of assets that are operational 24 x 7
and has same functional objective, it is
denoted by
AFR = Failures in the sample period x (52
weeks/ Number of weeks in the sample
period)
Number of Units in the population
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2.1 Maintenance Review
As mentioned in the earlier section, RCM is
an optimum mix of predictive, preventive
and reactive maintenance, it is vital that this
principle is reflected on the maintenance
planner by performing a maintenance review
and identify the most appropriate method of
maintenance activity required for an asset.
- Acquire the list of maintenance
tasks.
- Identify and exempt the tasks that
are required to satisfy health and
safety legislations which can only be
performed by intrusive maintenance
from the review.
- Factor FMEA results
- Identify the tasks that can be
subjected to predictive Inspections.
- Identify the tasks that can be
subjected to preventive
Maintenance
- Identify the tasks that can be
subjected to visual Inspections
- While amending or assigning the
frequencies for PdM inspections, the
type of the condition monitoring test
and objective of the original PM task
should be considered, for example –
If a maintenance task aimed to
verify whether there are any
excessive vibrations in a pump on a
yearly basis, a non intrusive
vibration analysis is preferred and
recommended to be performed on a
six monthly basis, as the cause of
excessive vibration in any rotary
asset can grow rapidly thus
increasing inspection frequency will
enable engineers to keep track on
the operational status of the asset
and perform remedial works before
it the exceeds specified tolerance
level.
- It is important that the assigned
frequency should be feasible and
cost effective; the entire
maintenance review should be
performed by the RCM team since
most of the decisions are made
based on engineers/managers field
experience.
Mean Time to Repair (MTTR)
It is the measure of the average time
required to repair a failed asset or its
components and it is usually expressed
in hours
MTTR = Total Downtime in hours .Eq (6)
Number of Breakdowns
Availability (A)
It is generally defined as the degree to
which an asset and its component are in
operable and committable state at any
point in time when it is needed.
A = MTBF/ MTBF+MTTR ….Eq (7)
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
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Flexing PdM Inspections Intervals
Maintenance models often don’t allow the
user to change the inspections interval as it
could have perceived impact on the cost,
availability or resilience of the facility. The
proposed RCM model allows “Flexing” the
PdM inspection interval based on the
following parameters:
- Asset’s Failure Pattern
- Age
- Operational life
Failure pattern of an asset is dependent on
the failure rate, and there isn’t a definitive
pattern for all assets, it varies based on the
load, environmental condition, temperature,
design, shipping, and installation. But most
assets follow a failure pattern called
“Bathtub-Curve”.
Failure Rate
Time
The curve itself is classified into three
phases and it is dependent upon “shape
parameter” denoted by the symbol β (Beta),
If β < 0, it is classified as Phase 1 (Infant
Mortality) or asset prone to early failures.
If β =1, it is classified as Phase 2
(Operational life) which indicates the asset
entered into its operational life or useful life
and prone to random failure and finally if β >
1, it is classified as Phase 3 (Wear out
Period) which indicates engineers that the
asset has high probability of failing and
suitable replacement or remedial actions is
required. The ages to failure data is again
crucial to calculate these parameters, based
on the β value engineers can schedule or
decide the appropriate maintenance tasks,
in most cases the PdM inspection frequency
will be increased in order to monitor the
status of the asset, where it gives sufficient
time for the engineers to organise the
remedial actions. The analysis works well for
assets that are operational 24 x 7, but for
critical back up assets (for example a
standby generators) the probability of failure
can be calculated by
Qn = 1 – exp [(n-1) * τ-γ] / n * exp [-[nt- γ / n] β
………..Eq (8)
Where,
Qn= Probability of Failure over the entire interval n; η = Characteristic Life Parameter;
β = Shape Parameter; γ = Location Parameter; τ = Inspection Interval
n = Number of times the component operated in it s life.
Phase 2: Operating Life
Phase 1: Infant Mortality Phase 3: Wear Out Period
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
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Weibull Analysis
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 500 1000 1500 2000 2500 3000 3500
Hours
Be
ta
CDF= 0.632
CDF= 0.632
Scale parameter
= 2200hrs
Beta
= 1.71
Figure 6: Typical Weibull Probability Plot (based on the sample data)
P-F Curve
It is commonly defined as “A visual
representation of the behaviour of an asset
as it approaches failure”; The P-F Curve is
plotted against two parameters asset
condition and time. Once a failure has been
identified (Via PdM or Visual Inspection) it is
labelled as Point ‘P” called Potential Failure,
which means the asset or its components
had shown an early sign of deterioration and
it can lead to the Catastrophic or Functional
Failure point ‘F’ where an asset can no
longer be in operation or can no longer
perform its specified function
Usually the potential failures become visible
at around 70% of asset’s operational life,
and the interval between potential failure
and functional failure is called as “P-F
Interval”. The general rule is, during the P-F
interval the asset must be inspected at least
once; the inspection can be predictive,
intrusive/visual.
MAINTAINING ASSET’S DES IRED AVAILABIL ITY CAN BE A DAUNTING TASK BUT NOT ANYMORE (PART 1) : A REL IABIL ITY APPROACH
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Figure 7: Typical P-F Curve
The Inspection interval (I) can be calculated by,
I = PF/n…Eq (9) [see footnote]
Where,
PF = Duration or Interval between Potential
Failure and Functional Failure
n = Number of inspection carried out during
PF Interval
*Example: If PF Interval = 8 years, Minimum number of inspections carried during the PF interval is 2 I =PF/N = 8/2 = 4 or 4 monthly
Based on the asset’s failure and
deterioration pattern, the predictive
inspection frequency is varied. Intrusive
maintenance is performed only when the
asset operational condition is in amber to
red transition period, i.e. the optimum point
of intervention and the maintenance interval
is tuned accordingly so that engineers do
not lose the visibility of the source. It is a
manual process and it’ll be usually be
carried out by a PPM manager based on the
reliability and the field information provided
by the RCM facilitator
The failure pattern illustrated below is a
typical bath tub curve, but it is very unlikely
that all the assets will follow this pattern as
there are six different types of failure
pattern. As discussed in the earlier section,
based on the ages to failure data asset
specific failure pattern can be identified and
can be used during the flexing process. [See
note]
OOPPEERRAATTIINNGG AAGGEE//TTiimmee
22000099
PPOOTTEENNTTIIAALL
FFAAIILLUURREE SSyymmppttoomm 11 DDeetteecctteedd 22001111
CC
PPddMM IInnssppeeccttiioonn IInntteerrvvaall ((II)) == 66
MMoonntthhllyy
IISS TTAASSKK IINNTTEERRVVAALL
PPRRAACCTTIICCAALL? = Yes
I
22001100
BB
PPFF Interval
22001122
FFuunnccttiioonnaall
IInnccrreeaassee tthhee iinnssppeeccttiioonn iinntteerrvvaall ttoo 66 mmoonntthhllyy ttoo eexxaaccttllyy pprreeddiicctt PPooiinntt ‘‘FF’’ aanndd aallssoo iitt iiss
mmoorree ffeeaassiibbllee aanndd ccoosstt eeffffeeccttiivvee aapppprrooaacchh
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PAGE 16 OF 23
Asset Condition
Asset Condition Curve
PdM Inspection Interval
Time
Figure 8: Predictive Inspection interval (PdM) flexing
* Asset’s MTBF is not factored during PdM inspection interval flexing, as in most cases manufacturers MTBF and
asset operational MTBF will not be the same
Operating Life
Infant Mortality Wear Out Period
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Performance Based Partnership – Business Model Performance based partnership /
Performance based maintenance [4],[5],[6] is
a maintenance approach where Uptime Plus
acts as an engineering consultant and takes
full responsibility in maintaining the condition
of all the assets in the facility within the
agreed budget. In this approach the
performance standards are agreed instead of
maintenance techniques consequently
shifting the risk from client to Uptime Plus
prior to contract agreement.
The performance requirements for this
approach can be divided into qualitative and
quantitative requirements where the former
implies that the client needs are expressed in
the form goals and objectives which are
usually derived from the functional and
performance requirements, the latter implies
the standard verification methods (audits).
The performance of a critical infrastructure
can be determined by its asset’s condition
and deterioration rate where this approach
predominantly focuses on condition based
maintenance or monitoring tools which would
enable the engineering consultant to have
the full spectrum of an asset’s operational
information.
Performance requirements are not just
technical, the performance of service
delivery (e.g. Response time) is also
accounted. The flow chart depicted in figure -
9 is a visual representation of the
performance based partnership approach
and the objective of this model is to improve
the quality & reliability of the assets, make
cost savings and provide budget certainty
and development of a long term relationship.
In the initial stage, client will liaise with a
group of maintenance contractors where in
this stage Uptime Plus will act as an
engineering consultant and contribute to the
planning process in which the maintenance
intervals are predetermined, and proposes
bespoke maintenance strategy within the
constraints of performance requirements.
Key Performance Deliverables:
- Improve asset’s reliability and quality
- Aid client to achieve direct cost
savings
- Reduce risks associated with
compliance and legislation.
- Provide clear visibility of asset’s
operational status
- Manage and monitor the
performance of life critical assets
- Being innovative in developing new
maintenance strategies.
An engineering consultant will take the
responsibility for providing evidence of
business related financial risks associated
with various maintenance scenarios. For
example, when the consultant reports a
defect or deterioration on a critical asset, e.g.
an 11KV transformer, client will be supplied
with the following information
- Type of fault
- Source of fault
- Ages to failure date (In visual form)
- Deterioration rate
- Time to fail
- Remedial and replacement strategy
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During the performance specification phase,
both the client and consultant would liaise
with one another and decide the
performance threshold level for all critical
assets, i.e. they will decide the required
availability for the assets and the minimum
level of maintenance conditions. Business
continuity and the impact caused due to
failures are the decisive parameters for this
process, i.e. “How important is that asset to
my business?” and “what will be the impact
on business, if it fails?” and this task can be
time consuming and the role of an
engineering consultant is to help the client to
conclude a general agreement during this
process by providing an universal table of
critical assets, desired availability, key
performance indicators (asset specific) and
inspection interval to monitor the
performance.
Key performance indicators and the level of
maintenance required to satisfy the
specifications will be derived by Uptime Plus
facilitators. After the agreement, a bespoke
maintenance model will be devised and sent
to the client for final approval. In the
execution phase, completion of each task will
be reported back to the client where all the
tasks and its execution frequency will be
monitored and assessed by the client. In the
assessment period, the audit results of both
parties will be compared and evaluated
whether to confirm the Service Level
Agreements (SLA) and the specified
performance requirements which were
agreed during the specification phase are
met.
Asset Performance Indicator
API = [Conditional assessment of
the asset x 10] …eq [10]
Where the rating interprets [see foot note],
>80 API – Asset at good or high service level
70 > API < 80 – Asset at marginal condition
60 > API < 70 – Asset at deteriorating
condition
API < 60 – Asset at poor or critical condition
Asset Performance Indicator
Asset Number 122675 Description Packaged Chiller Unit Supports
Ist Floor COMMS room
Interval First quarter Second Quarter Third Quarter Fourth Quarter
2010 50 50 50 80 80 80 90 90 90 90 90 90
2011 70 70 70 90 90 90 70 70 70 90 90 90
2012 90 90 90 80 80 80 60 60 60 60 60 60
Table 6: API table
* - Asset performance indicator less than 70 requires preventive /intrusive maintenance
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Long term agreement process:
Specify
Maintenance
requirements
Conclude General
Contract
Determine
starting
Point
Devise
maintenance
scenarios and
performance
criteria
Conclude Partnership
Agreement
Conclude
performance and
maintenance interval
Agree performance
guarantees
Evaluate the
partnership
Collate Project
Information
Project assessment
Condition
assessment
Devise maintenance
plan
Devise activity plan
Devise project plan
and PPM
Execution of work
Completion of work
Periodic
performance audit
Adjust maintenance
scenarios and activity
plan
Budgeting
maintenance project
Specify
provisional
performance
criteria
Supervise the
process
Assessment of
completed tasks
Assessment of
performance
indicators
Figure 9: Performance based Partnership flow chart
Client Uptime Plus
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Virtual Tools – Performance and Data Analysis Performance monitoring tool is the most
essential aspect in the model, where all the
ages to failure data, current operational
status, asset information and their hierarchy
are stored. The tool allows the user to edit or
add an asset and provide a visual
representation of asset’s operational status
and keep track on the existing PPM planner,
remedial actions. In return the tool enables
the user to acquire the valuable historic data
that would allow the RCM facilitator to
determine asset’s failure pattern
consequently results in devising bespoke
maintenance strategy
Deriving bespoke asset replacement and
critical spares strategy is possible which is
usually based on the failure rate. Low MTBF
doesn’t necessarily means that the asset
should be replaced because asset
replacement is entirely age related not on
MTBF, having the historic information of
critical assets enables the user to distinguish
between MTBF and asset life expectancy,
resulting in optimising the existing asset
replacement model.
Figure 10: Snapshot of the PMM database and asset life expectancy window
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Prognostic and Diagnostic tools
A quintessential aspect in both RCM and
performance based partnership approach is
the condition assessment. Visual inspection
is often the primary method to access the
operational status of the asset, but the
amount information that can be extracted via
this method is limited as there are
constraints that limit its efficiency (e.g.
human errors, spurious alarms).
In some cases, detecting failures can often
challenge even the most experienced
engineers since some of the early signs of
deterioration are hard to detect or almost
impossible during visual inspections. With
the rapid growth of sensors and signal
processing technology [9] engineers can
now have a much broader spectrum of their
asset’s operational status and allows them
to detect early deterioration signs and even
some of the hidden failures.
Thermal Imaging [7] (Thermography) is one
of the efficient non-intrusive methods to
detect any thermal anomalies on electrical
assets, and works on the principles of joules
heating effect, these heat signatures
increase when the current in a particular
conductor increases (overloaded) and it can
be easily be detected by Infra-red scanning.
It is suitable for detecting over-loads and
loose connections in fuses, switch gears,
transformers and bus bars.
In HVAC, thermal imaging is used to detect
refrigerant leaks, leaking pressure gauges
where the method can be used to replace
the quarterly intrusive leak detection checks
on chillers. In rotary assets, it is ideal for
locating the root cause of overheating. It is
suitable for identifying overheated bearings
or rollers, misalignment of shaft, pulley or
coupling and lubrication failure
Deterioration in fuel tanks, oil filled
transformers and pipe works can be
identified via fluid sampling that basically
detects any fluid contamination and provides
indication on the level of deterioration.
Partial Discharge (PD) is an electrical
discharge that does not completely bridge
the space between two conductors. The
discharge may be in a gas filled void, in a
solid insulating material, in a gas bubble, in a
liquid insulator. When partial discharge
occurs in a gas, it is usually known as
corona. Partial discharge is accepted as a
standard protocol test for high voltage assets
by power sectors. In addition to that partial
discharge detectors are equipped with
ultrasonic sensors where they are used to
detect arcing and corona in HV/MV
switchgears and transformers.
Vibration analysis is an efficient non-
destructive testing tool for the building’s
rotary assets, basically the tool analyses the
vibration signature of high speed rotary
equipments such motors, pumps which has
a on board diagnosis tool with the clever
algorithm that can prioritise repair
recommendations. The vibration analyser is
equipped with tri-axial accelerometer and a
two- point laser tachometer (speed
measurement) for precise vibration sampling
to identify bearings looseness,
misalignment, unbalance, gear problems
and bent shaft.
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Conclusion
The proposed model provides complete transparency over critical asset’s KPIs and with aid of
reliability predictions supported by performance & condition monitoring tools, asset failures are
detected at an early stage where the cost of intervention is minimum, consequently enabling
facility owners to achieve substantial cost savings and enables them to maximise asset’s service
life and in certain case extends asset’s life expectancy. In addition to that asset’s failure pattern is
determined in order to identify the desired maintenance frequency consequently resulting in a
dynamic maintenance planner which is mapped against asset’s failure pattern, operational status
and age. This approach increase asset’s reliability, availability and maintains downtime well below
the threshold level. Overall the reliability and performance based approach for asset maintenance
is an effective replacement to the conventional calendar based maintenance.
About Authors:
Andrew Dutton Laxmi Vajravel
CEM Director, Integral UK, Critical Infrastructure Manager
1290, Aztec West 1290, Aztec West
Almondsbury, Bristol Almondsbury, Bristol
BS32 4SG BS32 4SG
Email: Andrew.Duttton@integral.co.uk Email: Laxmi.Vajravel@integral.co.uk
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References
[1] Introduction to Reliability-Centred Maintenance by John Moubray, ISBN-10, 0750602309,
ISBN-13 9780750602303
[2] Paul Barringer, P.E, Predict Failures: Crow-AMSAA 101 and Weibull 101, Barringer &
Associates, Inc, Proceedings of IMEC 2004 International Mechanical Engineering Conference
December 5-8, 2004, Kuwait, Published by Kuwait Society of Engineers.
[3] Smith & Hinchcliffe, RCM--Gateway to World Class Maintenance, 1st Edition, 2003, Butterworth-Heinemann, ISBN: 9780080474137
[4] Ad Straub, Performance based Partnership forms for Maintenance by Dutch housing
Associations by, 2005, OTB Research Institute for Housing, Urban and Mobility Studies, Delft
University of Technology
[5] Igal M. Shohet, & Ad Straub, Performance-Based-Maintenance: A Comparative Study between the Netherland and Israel, 2010 EFMC (European Facility Maintenance Conference) [6] Ad Straub, The Maintenance Contractor as Services’ Innovator In Performance-Based Partnerships, TU Delft OTB Research Institute for Housing, Urban and Mobility Studies ,The Netherlands. [7] Business Focused Maintenance, Samples and Schedules by Jo Harris and Paddy Hastings, 2004, BSRIA 70174 December 2004 ISBN 0 86022 604 2 Printed by Multiplex Medway Ltd. [8] V. Narayan, Effective Maintenance Management – Risk and Reliability Strategies for Optimizing Performance, April 2004, Industrial Press Inc., ISBN 0-8311-3178-0 [9] Andrew K.S. Jardine, Daming Lin, Dragan Banjevic, A review on machinery diagnostic and prognostics implementing condition based maintenance, Mechanical Systems and Signal Processing, Volume 20, Issue 7, p. 1483-1510 [10] Alan Pride, CMRP, Reliability Centred Maintenance: http://www.wbdg.org/resources/rcm.php, last Updated on 06-07-2012.
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