Data Analysis| Nordex Energy GmbH | Hamburg | confidential

RELIABILITY-CENTERED MAINTENANCE: COST-EFFECTIVE TECHNIQUE TO MINIMIZE TURBINE FAILURE

PhD. Jurgita Simaityte, senior fleet data analyst

EWEA operating wind farms workshop

Bilbao 14-15 April

AGENDA

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• Nordex at a glance

• RAMS concept and O&M relation

• Reliability centered maintenance

• Life-cycle management and (un)certainty

• RCM and costs management

• Summary

EWEA operating wind farms workshop | 14-15 April 2016 | Bilbao JSI

NORDEX AT A GLANCE

Currently focused on ~20 countries – onshore market position 2015 (w/o AWP)

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PakistanNx installations 2015: 28 MW

(market data not yet disclosed)

South AfricaMarket share 2015: 18%

UruguayMarket share 2015: 17%

TurkeyMarket share 2015: 29%

Order intake: +67%

GermanyMarket share 2015: 12%

UKMarket share 2015: 32%

IrelandMarket share 2015: 25%

Chile Subsidiary established

FranceMarket share 2015: 14%

FinlandMarket share 2015: 40%

EUROPEMarket share 2015: 13%

10% 10%GLOBALMarket share 2015: 3%

ChinaSourcing only

Investor Presentation | Nordex SE | February 2016

NordexAWPNordex + AWP

RAMS: RELIABILITY, AVAILABILITY, MAINTAINABILITY AND SAFETY

JSIEWEA operating wind farms workshop | 14-15 April 2016 | Bilbao 4

Reliability

•Designed-in reliability

Operating environment

•Asset performance without operational errors

Maintenance plan

•Sustaining reliability of the asset

•Availability improvement

O&MOEM

design optimizationreliability specifications

maintenance optimizationcost reduction

OPERATION AND MAINTENANCE CHALLENGES

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• O&M market is growing!Market

• Competition

• Aging and diverse fleet requires innovative O&M approach

Challenge

• Decrease OPEXOpportunity

• Cost-effective strategies: RCMTools

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RELIABILITY CENTERED MAINTENANCE

The goal is to reduce life-cycle costs while continuing to allow the system to function as intended with required reliability and availability

RCM

Reactive Preventive Predictive Proactive

• non critical• redundant

• known failure pattern

• wear-out• consumable

• critical• not wear-

out• random

failures

• root cause analysis

• FMEA• commissioning

RELIABILITY MAINTENANCE STRATEGIES

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Function and failure analysis

Failure modes analysis

Maintenances strategies selection

Preventive tasks selection

Action time

Syste

m c

onditio

n

100%

Failure time Failure time

1

2Potential 1: Prolong lifecycle

Potential 2:Exchange component

Potential 3:Do nothing

2

1

JSIEWEA operating wind farms workshop | 14-15 April 2016 | Bilbao

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SYSTEM LIFECYCLE MANAGEMENT

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Decision making process

Input data

- Failure records

- Operational data

- Maintenance records

Analysis on component level

System reliability model

Life-time analysis

System

Component A

Component C

Component D

Component B

Component E

JSIEWEA operating wind farms workshop | 14-15 April 2016 | Bilbao

UNCERTAINTY SOURCES

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Uncertainty

Input variables

Model

Parameter

Outcome

Unknown inadequacies

Source: Roeser,S et al., Essentials of Risk Theory. Levels of Uncertainty. Springer, p:29-54.

‘‘unknown unknowns’’

JSIEWEA operating wind farms workshop | 14-15 April 2016 | Bilbao

WEIBULL RELIABILITY MODEL

η (eta)=11 years (characteristic lifetime)

JSI 10EWEA operating wind farms workshop | 14-15 April 2016 | Bilbao

All models are wrong, but some are useful. /George E. P. Box/

Note: Examples present simulated failure data

CONFIDENCE BOUNDS AND EFFECT OF SAMPLE SIZE

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*Sample size = N(n/m): N- total sample size, n – failed, m - healthy.

5

7

9

11

13

15

17

19

21

23

25

5 10 15 25 35 45 50 55 100

Eta

confidence b

ounds

Sample size

real beta

…

Real eta

There is a 95% probability that the calculated confidence interval encompasses the true value of the population parameter.

Sample size = 5(3/2)* Sample size = 10(6/4) Sample size = 50(33/17) Sample size = 100(54/36)

JSIEWEA operating wind farms workshop | 14-15 April 2016 | Bilbao

Note: Examples present simulated failure data

TOTAL OUTCOME VARIABILITYMonte Carlo Simulation

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2

3

4

5

6

7

8

9

10

11

4,5 5 5,5 6 6,5 7 7,5 8 8,5 9 9,5 10 10,5 11 11,5 12

Para

mete

r valu

e

Time line [years]

MLE beta RR beta MLE eta RR eta

real eta

real beta

JSIEWEA operating wind farms workshop | 14-15 April 2016 | Bilbao

Monte Carlo Simulation performs risk analysis by generating possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty.

Note: Examples present simulated failure data

RISK AND DECISION MAKING FOR THE FUTURE

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Output variability

Best case scenario – 22 failed Worst case scenario – 50 failed

€ 11M € 25M

Money?( €500K/gearbox)

30 gearboxes will fail in the next 2

years

80%

JSIEWEA operating wind farms workshop | 14-15 April 2016 | Bilbao

Decision

Note: Examples present simulated failure data

SUMMARY

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RCM approach offers a high potential for smart O&M

Life cycle analysis of the system - input to verify reliability and optimize maintainance

Uncertainty and risk assessment is the basic step in RCM strategies selection

RCM plays significant role in conflict between product costs saving and maintaining reliability of the product in the field

JSIEWEA operating wind farms workshop | 14-15 April 2016 | Bilbao

15JSIEWEA operating wind farms workshop | 14-15 April 2016 | Bilbao

ContactJurgita SimaityteSenior data analystRemote ServicesNordex Energy GmbH

Thank you for your attention!

Questions?

REFERENCES

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1. Gulati, R., Smith, R. Maintenance and Reliability Best Practices, Industrial Press Inc., 2009

2. "NASA Reliability Centered Maintenance (RCM) Guide for Facilities and Collateral Equipment" (PDF). NASA. FEV 2000.

3. Nowlan, F.S. and Heap, H.F. Reliability-Centered Maintenance, Dolby Access Press, San Francisco, CA 1978.