Intelligent Malfunction Prognostics - USAID...

Intelligent Malfunction PrognosticsFrom equipment condition monitoring to optimal asset management

EWEA Annual Conference, Brussels, Belgium, March 14-17, 2011

Copyright © 2011 by Cassantec Ltd. 1

There are many CMS for WT on the market, differing in their functional scope, WT component focus, learning capabilities and life cycle stage

Solution Profile

Scope Monitoring (Predictive) Diagnostics Prognostics

Learning Manual Automated, unit-level Automated, fleet-wide

Stage R&D Validation 100sInstallation 10s 1000s

Focus Pitch Gearbox ConverterDrive Generator Yaw

Intelligent Malfunction Prognostics


Intelligent malfunction prognostics can be provided through reliability reports, supporting critical decisions on maintenance scope and schedule

a

b c

d

e

f

g

h

Reliability Report

a View condition diagnostics

b View malfunction diagnostics

c View malfunction prognostics

d Aggregate prognostics

e Cross-check maintenance plan

f Extend condition data sources

g Extend malfunction modes

h Extend prognostic horizon



Monitor power output and technicalcondition of each unit in the fleet

Review diagnostic insights for units in marginal or critical condition

Use prognostic foresight to optimize fleet maintenance process

► Identify and avoid unnecessary preventive measures and costs ► Anticipate malfunctions before failure, damage, foregone output► Realize a commercially optimal fleet maintenance schedule

Unit View

Reliability reports aggregate to a fleet level, with navigation functions, consolidating diagnostic insight and prognostic foresight for several units

Unit vs. Fleet ViewFleet View



Crucial condition data is captured through vibration and lubricant sensors, and directly uploaded into the WT controller via standard protocols

NacelleRotor hub

Slow rotating shaftFast rotating shaftRotor bearingBearingTower

GeneratorGearbox

BladePitch

WT Server

Foundation

R2R1

E1

SCADA controllerSensor controller

T,R,E

Ethernet Switch

T2T1

T4 T5 T8T6 T7

Wind Turbine

Yaw drive

V,L

V1

V2V4 V5

V8

V7

V6

Hardware Package

V1

L1

Very low frequency accelerometer High sensitivity & accuracy Latest-generation technology Armored integral cable

V,L

Versatile Profibus terminal Easy plug-in installation Straightforward configuration Meeting OEM standards

Inline twin laser particle counter Latest-generation technology Integrated humidity sensor Stainless steel block

V8…


T3

V3

L1


• Hosting Network Mgt and PLC Controller• Consolidation of SCADA data from all WT• Consolidation of additional sensor data• Forwarding of consolidated data batches to Cassantec

Wind Park

• Gather malfunction and failure statistics

• Inform suppliers of components affected

• Improve quality of WT components affected

• Ascertain state-of-the-art prognostic solution

• Report Review • Maintenance &

Service Scheduling

• Report Review• Asset Mgt. Decisions• Spare Part Mgt.• Capacity Forecasts

Further Wind Park • Data Mgt & Archiving• Condition Monitoring• Malfunction Diagnostics• Failure Prognostics• Intelligent Reporting

Reliability reports are updated with new condition and process data in periodical intervals, and delivered to the operators on-line via reliability portal

1

2

3

4

5

6

7

8

Etc.

ALAN / Ethernet or similar

B

Router, Firewall

Internet

Cassantec Server

WT Manufacturer

WP Operator

WP Insurer

Download batches of condition and process data

(V,L,T,R,E) for all WT in regular intervals

Upload WP Reliability Reportsin corresponding intervals

WP Service Providers

ISDN, ADSL, or similar

Further Wind Park Etc.

WP Server

WT Server

Fleet Server

WP = Wind Park, WT = Wind Turbine


Data Transfer


We have calibrated and validated our reliability reporting solution with off-line and on-line data from several wind farms predominantly in the U.S.


Example

Wind Farm: Buffalo Ridge near Alta, IA, U.S.A.

WF Capacity: 150 x 750 kW = 112.5 MW

WT Models: Zond Z-46 (now GE)

Sampling period: 2006 – 2010 (on- & off-line)

Sampling intervals: continuous to 6 months

Malfunction modes: e.g. Gearbox LS wheel wear

Causes: e.g. Micro pitting, contributed by water ingress

Impact: e.g. Bearing life reduces by factor 3

Learnings: ► Upgrade sensor hardware► Monitor condition dynamics► Exploit fleet intelligence

Field Validation of SolutionIllustrative

Map source: www.google.comLogo source: www.altaiowa.com


We achieve malfunction and failure prognostics over an explicit time horizonexceeding the best “predictive diagnostic” approaches on the market so far

Prognostic Horizon

Prognostic horizon[Days after last update]

00 1 10 1.000100

Value addedby reliability report

$

$$

$$$Our currentcapability

Potentialfuture

capability

EquipmentProcurement

& Replacement

Work OrderScheduling

MaintenanceCycle

Scheduling

UnscheduledOutage

Coordination

RoutineMonitoring

Competitor capabilities



Benefits of reliability reports have been confirmed by operators beyond wind power – these benefits increase over time through machine learning

Prognostic Accuracy

► In retrospect, 99% of predictable malfunctions were accurately predicted, with a horizon of up to 5 years (!)

► Operator knowledge was exceeded by 20%, with several surprises (e.g. cartridge sealing)

► Diagnostics und prognostics are enhanced over time through machine learning

June 2010 July 2010March 2009August 2008April 2007Cartridge seals

Mech. sealsCartridge seals

Mech. sealsCartridge seals Coupling

AlignmentCouplingAlignmentMech. seal

NoOK



Example for learning value bands

Machine Learning

Value bands must be continuously „learned“ from the empirical condition data: Even with constant equipment utilization, value bands may shift over time!

Collective learning process for equipment of same type

(flagging before adjusting)


„Normal“ value bands

shift and evolve

Static value

bands not useful


This learning process is initialized at different parameter value levels – gearbox oil has fluctuating initial levels of cleanliness, mostly within tolerance intervals

Gearbox oil is rarely “clean” to begin with:units start

up with varying levels of

initial contami-

nation

Learning Process Initialization

Learning process initialization for equipment of same type


21

18

16

Example for flexibleinitialization


In summary, we are targeting new features allowing commercially optimal fleet maintenance schedules, cutting costs of failure, damage and lost power output

State-of-the-art sensor hardware► High-end specialized sensors for wind power applications► Integration of latest technologies (e.g. twin laser particle counters)► Full utilization (and no duplication) of existing data and infrastructure (SCADA)

Intelligent diagnostics► Comprehensive expertise on model-specific malfunction and failure sources and risk► Automated learning from ongoing monitoring of the entire fleet► Reference data from other WT, fleets, applications

Advanced prognostics► Extended prognostic horizon through computational stochastic model ► Full utilization of recorded and archived condition and process data histories per WT► Prognostic accuracy exceeding capabilities of any competing product on the market

Cost-effective advice on optimal fleet asset management► Reduction of risk and costs for WT malfunction, failure, damage and foregone power output► Reduction of risk and costs of unnecessary preventive measures and foregone power output► Realization of a commercially optimal condition-based fleet maintenance schedule

State-of-the-art

Enhanced

Enhanced

New

Technical & Commercial Target Benefits



Further Information

For further information, please review our brochure on-line, and contact us by e-mail or telephone

Cassantec team behind this presentation

Frank KirschnickZurich, Switzerland

Heinz GiovanelliMunich & Zurich

Gary EllisCleveland, Ohio

Shuang YuanZurich, Switzerland

Mart GrasmederCleveland, Ohio

Katerina StamouZurich, Switzerland

Mila VodovozovaZurich, Switzerland

► To obtain more information, please download our brochure at

www.cassantec.com/wind.pdf► Or send an e-mail to

[email protected]




Appendix


Cassantec is an independent provider of integrated, automated prognostic services for critical power plant equipment with a unique, protected technology

Meaning: Cassantec = Cassandra Technologies

Position: Independent provider of integrated, automated equipment condition diagnostics and malfunction prognostics

Technology: Novel combination of best practice techniques from Operations Research, Artificial Intelligence and Data Mining

Comprehensive condition data reference base (since 1993): 500k data sets of 20 equipment types, 2000 models

Offering: Online Condition Monitoring Systems and Reliability Reportson a subscription basis for equipment operators worldwide

References: Chemical and Power industries (U.S.A. and Europe) including nuclear and fossil-fired power plants and wind farms

Promoters: Power corporations, private investors, Swiss government (CTI)

Industry Partner: Leading independent U.S. lubricant lab (Insight Services)

Academic Partner: EPFL, ETHZ, Stanford UniversityCassandra

prophet of critical future events in the Greek mythology

Profile of Cassantec Ltd.



Our prognostic services have been successfully applied to a wide range of power equipment, with operators in different regions and industry segments

Cassantec References (Excerpt)

Wind Fossil Nuclear Chemical Steel


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