2015 ars eu_red_s9_schutter

2015 ARS, Europe: Amsterdam, The Netherlands
Red Room, Session 9
Uncertainty in RAM Analysis
George de Schutter
Begins at 2:15 PM, Wednesday, April 22nd

PRESENTATION SLIDES
The following presentation was delivered at the:
International Applied Reliability Symposium, Europe
April 21 - 23, 2015: Amsterdam, Netherlands
http://www.ARSymposium.org/europe/2015/
The International Applied Reliability Symposium (ARS) is intended to be a forum for reliability and maintainability practitioners
within industry and government to discuss their success stories and lessons learned regarding
the application of reliability techniques to meet real world challenges. Each year, the ARS issues an open
"Call for Presentations" at http://www.ARSymposium.org/europe/presenters/index.htm and the presentations
delivered at the Symposium are selected on the basis of the presentation proposals received.
Although the ARS may edit the presentation materials as needed to make them ready to print, the content of the
presentation is solely the responsibility of the author. Publication of these presentation materials in the
ARS Proceedings does not imply that the information and methods described in the presentation have been
verified or endorsed by the ARS and/or its organizers.
The publication of these materials in the ARS presentation format is
Copyright © 2015 by the ARS, All Rights Reserved.

George de Schutter, Royal HaskoningDHV Slide Number: 2Session 9Red Room
AppliedReliabilitySymposium,Europe2015
Introduction
“All models are wrong, but some are useful.”
George Edward Pelham Box (October 18, 1919 – March 28, 2013),
British mathematician and Professor of Statistics at the University of Wisconsin

Introduction (2)
Royal HaskoningDHV
independent, international engineering, project
management and consultancy company
asset management, aviation, buildings, energy, industry,
infrastructure, maritime, mining, strategy, transport, urban
and rural planning, water management and water
technology
7,000 colleagues
100 offices
35 countries

Introduction (3)
 Royal HaskoningDHV performs RAM (Reliability,
Availability, Maintainability) studies for:
 Oil and Gas facilities
 Infrastructure: Locks, Bridges, Tunnels
 Water facilities
 Other
 RAM analysis is used for:
 Design optimization
 Verification of reliability / availability requirements
 Forecasting production / availability
 Maintenance optimization
 Sparing strategy
 Cost reduction

Introduction (4)
Asset Management Perspective:
Asset Management:
 coordinated activity of an organization to realize value from assets
 balancing of costs, opportunities and risks against the desired
performance of assets, to achieve the organizational objectives
Asset owners need reliable production or availability
forecasts
New ISO 55000 sets standard for asset management
 Risk management is essential

Risk Management (ISO 31000)
Organization objective: required performance
Awareness of probability of not reaching required
performance: probabilistic approach
Risk Assessment
Introduction (5)
Risk Identification
Risk Analysis
Risk Evaluation
Risk Mitigation
Monitoring&review
Communication&
consultation
Establishing context

Introduction (6)
Probabilistic Project Risk Management
In project risk management, it is more common to use a probabilistic
approach: probability of exceeding project milestones.
Probabilistic planning analysis is used for large infrastructural
projects (e.g., new subway “North-South Line” in Amsterdam).
This information is crucial for management and politics (all
stakeholders). Probabilityofexceedance
Time ->
Frequency

Introduction (7)
Observations:
Clients are often unaware of the uncertainty of the outcome
of a RAM study
In other words: probability that actual performance will be
below calculated performance
Most RAM studies do not report uncertainty (“confidence”)

Agenda
 Introduction 10 min
 Traditional RAM Analysis 10 min
 Probabilistic RAM Analysis 10 min
 Project Example Results 10 min
 Summary & Conclusions 5 min
 Questions & Discussion 15 min

Vocabulary
 CMMS – Computerized Maintenance Management
System
 FMECA – Failure Mode, Effect and Criticality Analysis
 FTA – Fault Tree Analysis
 MTBF – Mean Time Between Failure
 MTTR – Mean Time to Repair
 RAM – Reliability, Availability, Maintainability
 RBD – Reliability Block Diagram
 SD – Standard Deviation

RAM(S)
 Reliability, Availability, Maintainability (and Safety)
 RAM Analysis is used for:
 Design optimization
 Verification of reliability / availability requirements
 Forecasting production / availability
 Maintenance optimization
 Sparing strategy
 Cost reduction

The outcome of RAM analysis should serve the boardroom in risk-
based decision making:
Risk-based production targets
Support business plans
Focus for investments
Design optimization
Maintenance optimization
Cost reduction
Need for accurate performance prediction

Traditional RAM analysis stepwise
1. Client: draft design
2. Define system functions, define failure and performance
requirements for system
3. Choose method of analysis
4. Build model
5. Data collection
6. Calculations
7. Results and reporting

Step 1: Client: draft design
 Components and equipment types
 Redundancy
 Instrumentation: alarms and trips
 Design criteria

Step 2: System functions, failure definition and
performance requirements
 System function (e.g., producing gas, guiding traffic, etc.)
 Clear definition of system failure
 When does system fail? (e.g., production volume below xx m3/h,
product off-spec, throughput below xx vehicles/h)
 Define required performance (e.g., availability > 99%,
number of outages per year < 10)

Step 3: Choose method of analysis
 Depending on requirements, select a modelling method:
 FMECA
 Count Parts
 Fault Tree Analysis
 Reliability Block Diagram
 Etc.

Step 4: Build Model
 Model components, redundancy, failure behaviour
 Data needed:
 MTTF / failure rate
 Intervention / repair time

Step 5: Data collection (1)
1. Client- or vendor-specific data
2. Generic sources (e.g., Oreda, RiAC)
3. Expert judgment
Failure data Uncertainty !
 First reason for uncertainty: sampling.
 Failure data is based on a certain population (“sample”)
of components that is a sample of the total population.
 Smaller samples result in higher uncertainty.

Other reasons for uncertainty
Is the data used applicable for the specific application?
Different branch of industry
Different environment
Different vendor
Different maintenance strategy
In general, more specific data is favourable, but be careful!

Data from generic source, example:
Reference: Offshore Reliability Data, 5th ed. – Topside equipment

 Perform calculation based on model and
failure data
 Using RAM software (e.g., Isograph
Reliability Workbench®, ReliaSoft®)
Step 6: Calculations

 Obtain results from calculations
 If needed, modify design or maintenance
 Report results to client
 Often single figure (e.g., “Availability = 97.1%”)
Step 7: Results and reporting

Probabilistic RAM analysis stepwise
1. Client: draft design
2. * Define system functions, define failure and
performance requirements for system
3. Choose method of analysis
4. * Build model
5. * Data collection
6. * Calculations
7. * Results and reporting

Step 2*: System functions, failure definition
and performance requirements
 System function (e.g., producing gas, guiding traffic, etc.)
 Clear definition of system failure
 When does system fail? (e.g., production volume below xx m3/h,
product off-spec, throughput below xx vehicles/h)
 Define required performance, for example:
 Probability of production volume > 100 m3/h is 95%
Target value
100m3/h
95%
Expected value

Step 4*: Build Model
 Model components, redundancy, failure behaviour
 Data needed:
 MTTF / failure rate with Distribution (e.g., Standard Deviation, Distribution)
 Intervention / repair time with Distribution (e.g., Standard Deviation, Distribution)

Step 5*: Data collection (1)
Still possibility of using different sources:
1.Client- or vendor-specific data
2.Generic sources (e.g., Oreda, RiAC)
3.Expert judgment
But information on spread in data is needed or needs to
be estimated!

Vendor data:
Sometimes not available
If available, most of the time only MTBF values are given and no
SD
Difficult to get information on uncertainty
Vendors should start to provide information on confidence of
MTBF/MTTR values.
If no information is available, an estimation can be made of the
uncertainty.
Plant-specific failure data from CMMS:
Both MTBF and SD can be derived if individual failure data is
available

Example of plant-specific data:
Component 2012 2013 2014 Total Failure
Rate (/yr)
Pump 1 2 0 1 3 1
Pump 2 2 1 3 6 2
Pump 3 6 2 2 10 3,33
Total 10 3 6 19 2,11
No. of component years 9
Standard Deviation 0,96

Data from generic databooks
Reference: Offshore Reliability Data, 5th ed. – Topside equipment

 Perform calculation based on model and
failure data + confidence data
 Using specific RAM software (Isograph
Reliability Workbench®)
 Use confidence analysis options
Step 6*: Calculations

 Obtain results from calculations
 If needed, modify design or maintenance
 Report results to client
 Report probability that required performance
will be achieved
Step 7*: Results and reporting (1)

~ 15%
Required availability
~ 85%
Expected value
(calculated)
15% probability that
target availability is
not achieved
100 m3/h 150 m3/h

~ 50%
Required availability
~ 50%
Expected value (calculated)
50% probability that
target availability is
not achieved!
100 m3/h

Project Example (1)
 Gas landfall station (screening study)
 Fault Tree Analysis
 Information on data uncertainty was included in the model
for each component:
 Failure rate
 Failure rate standard deviation (from Oreda)
 Failure rate distribution: Normal
 MTTR
 MTTR standard deviation (rule of thumb)
 MTTR distribution: Normal

Project Example (2)
 Resulting unavailability distribution

Project Example (3)
Availability Results with 95% Confidence Interval
Note: numbers are examples to show principle
Probability that
availability is achieved
Availability
Mean Value 50 % 82,7 %
Lower Bound 97,5 % 78,4 %
Upper Bound 2,5 % 86,8 %

 Asset owners are often unaware of the uncertainty in
results from RAM analysis: any calculated unavailability
point-value does not tell the whole story
 Confidence interval analysis is supported by RAM
analysis software (e.g., Isograph Reliability Workbench®)
 Proof of concept successfully implemented for an existing
study of Royal HaskoningDHV
 Proof of concept shows that spread in results can be
substantial
 Practical challenges in confidence analysis need to be
solved
Conclusions

Discussion
Awareness of uncertainty in results of RAM analysis
is important.
Probabilistic approach has added value in specific
cases:
Contractual requirements (bonus / financial penalty
contracts)
Strong corporate demands for meeting production
targets

Challenges of Probabilistic Approach
 Vendors often do not provide information on data
uncertainty
 Many databooks provide no or limited information
on data uncertainty
 Clients are not aware of the uncertainty
Although information on data uncertainty might
be difficult to acquire, estimating the spread in
failure data using expert judgment results in a
more realistic result than implementing no
spread.

Questions for Discussion
 Does your organisation use RAM analysis?
 Is your organisation sufficiently aware of the
uncertainty in RAM analysis?
 Does a probabilistic approach (confidence analysis
on the results) in RAM analysis offer added value?

Questions
Thank you for your attention.
Do you have any questions?

Contact information
 George de Schutter MSc.
 Consultant RAMS Analysis and Risk Management at
Royal HaskoningDHV
 Amersfoort, The Netherlands
 Feel free to contact george.de.schutter@rhdhv.com
 LinkedIn: nl.linkedin.com/in/georgedeschutter

2015 ars eu_red_s9_schutter

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