Palisade User Conference
April 22nd & 23rd
London
Maximising the Net Present Value of Investment
in the Maintenance of Assets
- Ujjwal Bharadwaj, TWI Ltd, Cambridge, UK
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Presentation Outline
•
•
•
•
Overview of TWI
Risk Management
Risk Based Asset Management
The Risk Based Approach to Plant Maintenance
– Qualitative Assessment
– Quantitative Analyses
– Risk Based Optimisation
• Benefits of using the RB methodology
• Some issues
• Questions
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Overview of TWI
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What is TWI?
•Independent Research & Technology
Organisation for welding and joining
related technologies
•Serves industrial member
companies/government
•Non-profit distributing and limited by
guarantee of members
•Derives from The Welding Institute
and the British Welding Research
Association
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Key Figures
• 530 + staff
• 300 graduate status
• 3500 industrial members from
• 66 countries world-wide
• 4 major UK locations
• 4 overseas operations and
training bases
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Where is TWI HQ?
On Granta Science Park
8 miles South of
Cambridge
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TWI – UK Locations
North East
Yorkshire
Wales
Cambridge
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TWI Overseas
Training base at Malaysia
• Operations/training in Malaysia, Brazil, Iran & China
• Associates in Australia, France, Ukraine & US
• Agents/presence in US, India, Korea, Japan, Indonesia,
Italy, Saudi Arabia and the United Arab Emirates,
Kazakhstan.
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Key Industry Sectors Served
Aerospace
Electronics
Automotive
Medical
Construction
Oil & Gas
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Equipment,
Consumables & Materials
Power
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Risk Management
• Risk:
– Combination of the probability of an event and its consequences
(ISO/IEC Guide 73)
• What is Risk Management?
– Direction and control with regard to risk
– In a financial setting
• Concerned with events that pose opportunities for gain as well
as potential for loss
– Currency fluctuation, interest rates etc
– In an engineering setting
• Risk is a combination of occurrence of harm and the severity of
that harm ( ISO/IEC Guide 51:1999 )
• Harm is physical injury or damage to the health of people, or
damage to property or the environment
• Risk management process…
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Risk Management Strategic Objectives
Risk Assessment
Risk Analysis
Modification
Risk Evaluation
Risk Mitigation
Formal Audit
Risk Identification
Risk Description
Risk Estimation
Risk Acceptance
Risk Communication
Monitoring
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Risk Based Asset Management
• Asset Integrity Management
– Ensure integrity of engineered systems
– ‘Fit for purpose’ throughout asset lifecycle
– Ability of an asset to perform required function
effectively and efficiently whilst safeguarding life
and the environment
• Objective: Maximize returns, minimize risks
– Safety, Health and Environmental (SHE)
risks
– Business risks
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Risk Based Asset Management
• Attributes of a good RBM for asset mgmt
– Consistent, Transparent and Auditable
– Identify potential and active DMs of plant
– Plan inspection, such that residual risk for each
DM is within acceptable limits
– Increased reliability, safety and availability
– Reduced scope of work for shutdown inspection
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Failure rate over asset life cycle
Failure
rate
Infant
mortality
failures
Wear out
failures
Constant
random
failures
Life of the asset
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Failure rate over asset life cycle
Infant
Mortality
Failure
Useful Life
Ageing/ Final Stage
Stage
rate
Operating Life
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The Ageing Stage in a Plant’s Life
• Accumulated damage e.g.
– Thinning due to Corrosion/ Erosion
– Fatigue due to Cyclic Stresses
– Creep due to high temperature
• Remaining Life Assessment/ Estimates
– Prediction for replacement/maintenance
– Deterministic
– Probabilistic
» to capture the uncertainty involved
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The Risk Based Approach to Plant
Maintenance
• Risk is a combination of probability of an event and
its consequence (API 580)
• Step 1: Preliminary Risk Analysis of the System
– By Qualitative Risk Analysis
– Identify high risk components
• Step 2: Detailed Risk Analysis of identified System
components
– By Quantitative Risk Analysis
– Develop a probabilistic RL model of the degradation
mechanism
• Step 3: Optimisation
– Such that financial benefit is maximised
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Qualitative Risk Analysis
• System Analysis
– ETA using @Risk
Precision tree for all sub
components
– System boundaries,
failure criteria specified
– Data used
• Historical (local)
• Specific data
• Generic
• Expert Opinion
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Qualitative Risk Analysis
Likelihood
Consequen
ce
Risk
Score
Elec Control
High
Medium
48
Gear Box
Low
Very High
40
Yaw System
Low
Medium
24
Entire Turbine
Low
Very High
40
Generator
Medium
High
48
Hydraulic
Low
High
32
Grid
Connection
Medium
Low
24
Blades
Medium
Medium
36
High
Medium
48
Axle/ Bearing
Medium
Medium
36
Mech Control
High
Medium
48
Entire Nacelle
Medium
Medium
36
Coupling
Medium
Medium
36
Tower
Medium
High
48
Low
Very High
40
Component
• qualitative analyses
• semi quantitative
analyses
Risk Histogram using Risk Priority Measure (RPM)
Brakes
RPM
60
40
20
Foundation
El
ec
C
on
tro
G
l
ea
rB
Ya
ox
w
S
y
st
En
em
t ir
e
Tu
rb
in
G
e
en
er
at
or
H
y
G
dr
ri d
au
C
lic
on
ne
ct
io
n
Bl
ad
es
Br
ak
Ax
es
le
/B
ea
M
rin
ec
g
h
C
o
En
nt
ro
t ir
l
e
N
ac
el
le
C
ou
pl
in
g
To
w
er
Fo
un
da
ti o
n
0
Component number
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Quantitative Risk Analysis
• Qualitative Analysis highlighted a structural
component as high risk.
Failure Mode
Effect
Consequence category
Production loss
Secondary Damage
Corrosion Uniform Band in
Splash Zone
Collapse
Personnel
Maintenance costs
Reputation
• FMEA identified
three main damage mechanisms
Fatigue Circumfrential
cracking
– Corrosion
– Scouring
– Fatigue
Find and Assess
Repair
Consequence
Maintenance duration
Regulator penalties
Other turbines
Sea vehicles
local structures
Injury
Death
Installation of new structure
Repair and recommission
Insurance premium
Technology confidence loss
Lost production
Maintenance cost
Cost (£k)
Source/comment
Keep in service
Production loss
Secondary Damage
Collapse
Personnel
Maintenance costs
Reputation
Find and Assess
Repair
Maintenance duration
Regulator penalties
Other turbines
Sea vehicles
local structures
Injury
Death
Installation of new structure
Repair and recommission
Insurance premium
Technology confidence loss
Lost production
Maintenance cost
Keep in service
Production loss
Secondary Damage
Collapse
Personnel
Scouring
Maintenance costs
Reputation
Find and Assess
Repair
Maintenance duration
Regulator penalties
Other turbines
Sea vehicles
local structures
Injury
Death
Installation of new structure
Repair and recommission
Insurance premium
Technology confidence loss
Lost production
Maintenance cost
Keep in service
• Corrosion chosen to illustrate Risk Based approach
• Probabilistic Corrosion Model gives failure rate with
respect to years of service.
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Quantitative Risk Analysis
• Main input is distribution of corrosion rate
(CR) reflecting the uncertainty involved.
• Remaining life model:
• RL= (Tstart - MAT)/ CR
Tstart= Starting wall thickness (mm)
MAT=Minimum Allowable Thickness to maintain integrity
(mm)
CR=Corrosion Rate (mm/yr)
• Probability of failure is P(RL<0)
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Quantitative Risk Analysis
• Corrosion rate estimate
• Curve fitted over historical values using
@Risk
• Expert opinion
Marine Atmosphere
Splash Zone
High Tide
Normal(0.40000, 0.1)
Trunc(0,+inf)
Low Tide
4.0
3.5
Quiet Sea Water
3.0
2.5
2.0
Mud Line
1.5
1.0
5.0%
90.0%
0.2355
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0.0
-0.1
0.5
Typical Corrosi
on Rate of Steel, mpy
>
0.5645
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Quantitative Risk Analysis
Material
Process conditions Curre nt thick ness
Proba bility
Frequency
MONTE CARLO
SIMULATION
Remaining li fe, years
Remai ning li fe, years
•Repeated sampling of values from input distributions
gives PoF based on RL for the considered DM.
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Quantitative Risk Analysis
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Quantitative Risk Analysis
Probability of Failure (PoF) Vs Time
100
60
Probability of Failure Vs
Time
40
20
0
1990
2000
2010
2020
2030
Year
% age Remaining Life consumed Vs Year of
Operation
%age Remaining Life
PoF (%)
80
100
80
60
40
20
0
1995
2000
2005
2010
2015
Years of Operation
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Risk Based Optimisation (1/3)
•
•
•
•
Risk expressed in Expected Values (EV)
EV= PoF x CoF
Consequences of failure mainly lost production
The Optimisation model finds the time of
replacement of the plant when the Net Present
Value (NPV) is the maximum over planning
period
• The optimisation weighs the EV of replacement
with the EV of not replacing to identify the
optimum year of replacement.
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Risk Based Optimisation (2/3)
90
80
70
60
50
40
30
20
10
0
10
9
8
7
6
5
4
3
2
1
0
2000
2005
2010
2015
Action NPV
Probability of Failure
(PoF) %
Probability of Failure (PoF), Action NPV Vs Years
of Operation
2020
Years of Operation
Probability of Failure (PoF) % Vs Years of Operation
Action NPV Vs Years of Operation
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Risk Based Optimisation (3/3)
Optimized Action Schedule (w/o budgetary constraints)
Description
Str 1
Str 2
Str 3
Str 4
Action Year
2010
2009
2005
2009
Capital Cost
NPV
-4.072237
-3.878321
-3.190704
-3.878321
-0.548586
-0.62253
-0.283258
0.15813
Total NPV
-1.296244
Optimized Action Schedule (with budgetary constraints)
Description
Str 1
Str 2
Str 3
Str 4
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Action Year
2010
2006
2003
2009
Capital Cost
NPV
-4.072237
-3.350239
-2.894063
-3.878321
-0.548586
-0.647286
-0.32284
0.15813
Total NPV
-1.360582
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Potential Benefits from using the Risk
Based Methodology
• Target SHE and business risks
• Maximize return on investment in O&M by
risk prioritising
• Better understand DMs
• Better control and prevent unexpected
system outages
• Identify and eliminate gaps in existing
integrity mgmt process at a site
• Provide an auditable path for integrity mgmt
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Some issues
• Limitations of the optimisation tool
 For complex systems, non-linear optimisation tools
required.
 Increasing dependencies require more computing power.
• Limitations of the methodology
 Used mainly in the Ageing phase when time dependent
damage has accumulated. Needs to be used in
conjunction with an overall strategy for plant/equipment
life management.
 More suitable to business critical systems as opposed to
safety critical systems.
• Availability and quality of data
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Questions
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