Siemens Wind Power

Copyright notice

General presentation

Siemens Wind PowerReliability Assessment and Improvement through ARM Modeling

Poul Skjærbæk, Thomas Mousten, Henrik Stiesdal, September 2009

Page 2 Date AuthorCopyright © Siemens AG 2009

Energy Sector

Offshore Challenges Lead to Questions

Offshore conditions when correcting defects are worse…• Equipment size, availability, cost and mobilization time• Magnitude of weather impact• Efficiency of man-hours

… which leads to obvious questions:• As an owner, what will be my lifecycle costs?• As a financer or insurer, what are my risks?• As a manufacturer, what will I spend during the warranty period?


Energy Sector

An ARM Model Can Provide Some of the Answers

An ARM model is a framework for a quantified analysis of failureprobabilities and consequences• Availability• Reliability• Maintainability

The failure probabilities are described by Weibull distributions• A Weibull distribution is a statistical distribution describing the

likelihood of a specific event occurring within a certain time frame• A well-known use of Weibull distributions is for the description of

naturally occurring wind speed distributions• It also turns out that failures of technical equipment will often follow

a Weibull distribution


Energy Sector

The Classical “Bathtub” Reliability Curve

Each of the three basic curves can be described with a Weibull distribution

Time

Failu

re R

ate

Infant MortalityRandom FailureWear-outBathtub Curve


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The Modified Bathtub Curve With Four Failure Types

Time

Failu

re R

ate

Infant MortalityRandom FailureWear-outPremature Serial FailureModified Bathtub Curve


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ARM Model Basics

The ARM model reviews probabilities and consequences on acomponent level• The turbine is split into about 10 main components plus a sweep-up

“Others” for minor components. • For some main components it is relevant to consider different types of

failures with different consequences. • For example, the gearbox should be modelled with at least two

entries, one for defects that can be corrected in the turbine, and another for defects that require removal of the gearbox – they will have vastly different vessel cost consequences

• It is Siemens’ experience that sufficient resolution is obtained by review of 15-20 failure types


Energy Sector

For each failure type the ARM Modelhas the same steps

1. Determination of the Weibull distribution data – shape parameter β and characteristic life η• The shape parameter β depends on the failure type. • The characteristic life η is the point in time when 1 – 1/e = 63% of

components have failed 2. Determination of the failure probability

• The probability that a failure type will occur• By definition random failure and Wear-out affect all components• The real difficulty is a realistic estimate of the probability of Infant

mortality and Premature serial failure.3. Determination of the failure consequences

• Component cost (new / refurbished)• Proportion of components that can be refurbished• Average crew size and number of working days required on site• Technician rate and day rate of any crane / vessel needed• Typical mobilization time for crane / vessel• Long-term average weather window


Energy Sector

Making the ARM Model operational

Calculation for all components combined in one Excel sheet• Using Step 1-3 data actual calculation is straightforward• A component may have more than one set of data• If more than one component of the same type results are simply

multiplied with the number used per turbine• Key results:

• NPV of cost • Downtime / availability• Spare parts needed• Resources needed


Energy Sector

The Snake in the Paradise – Data Quality

No model is better than its input data• It is notoriously difficult to make predictions – particularly about the

future… (Niels Bohr)• Estimates of failure probabilities in the wind industry are by

definition forward estimates • The critical data depend on the failure type:

• For infant mortality / premature serial failure: η • For random failure and wear-out: P(f)

• Best estimates derived from • Well-consolidated operational records• Objective assessment using FMEA analysis• Common sense


Energy Sector

A Real Life Example – Generic Project with 3.6

Distribution of NPV over Component Types

BladeBlade_minPitch bear.Main bear.GearboxGearbox minGeneratorConv.mod.Yaw ringYaw gearOthersTurbine trsf.


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A Real Life Example – Generic Project with 3.6

Distribution of NPV over Lifetime

0%

20%

40%

60%

80%

100%

120%

Bla

de

Bla

de_m

in

Pitc

h be

ar.

Mai

n be

ar.

Gea

rbox

Gea

rbox

min

Gen

erat

or

Con

v.m

od.

Yaw

ring

Yaw

gea

r

Oth

ers

Turb

ine

trsf

.

Tota

l

Year 16-20Year 11-15Year 6-10Year 1-5


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A Real Life Example – Nysted Availability

0

20

40

60

80

100

120

-1 0 1 2 3 4 5 6Time after Take Over (y)

Ava

ilabi

lity

(%)

ActualPredicted30 pr. bev. gnsn. (Actual)


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Use of ARM Model Results

Owner’s use• Basis for revenue and income calculations• Basis for qualified discussion of operational risks with financers and

insurance• Basis for long-term asset management

Manufacturer’s use• Basis for continuous design improvement programs, because the cost-

benefit ratio is easily quantified• Basis for warranty-period risk assessment• Basis for qualified pricing of LTPs (Long Term Packages)


Energy Sector

Conclusion – And a Word of Caution

An ARM Model can provide lots of answers…• Best estimates of costs, downtime, equipment and spare parts needed,

etc.• Best basis for dialogue with owners, financers, insurance…

…But one has to respect the fundamentals!• The ARM Model is a probabilistic model• Probabilistic models work for large numbers• The ARM Model does not provide accurate predictions on turbine

level, often not even on project level• A good ARM Model provides good predictions on large project level

and population level

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