Date post: | 16-Apr-2017 |
Category: |
Technology |
Upload: | sparkcognition |
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PredictiveFuturesCognitiveAnalytics
TodaysSpeakers
StuartGillenDirector,
BusinessDevelopment
MaggiePakulaManager,
PerformanceAnalysisEngineering
JohnHensleyManager,
IndustryData&Analysis
“”
GlobalDataPowerestimatesthemaintenanceexpenditureonwindturbinesvitaltoproductivityisexpectedtorisefrom$9.25Bin2014to$17Bin2020
http://www.edie.net/news/6/Win-turbine-maintenance-costs-to-nearly-doubl/
“”
Itisestimatedthatin2011,nearly$40billionworthofwindequipmentintheU.S.willbeoutofwarranty,thrustingthefinancialriskontheownertoprovidecost-effectiveoperationandmaintenance.
http://www.renewableenergyfocus.com/view/26582/wind-getting-o-m-under-control/
CostofGearboxFailures
u Romax Studyestimatedcostofplanetarybearing failures>350k[1]
u In2014Siemenswrotedown€223Mtoreplacebearings infleet<2yrs.old[2].
u Controllingwindturbineswithdata-drivensoftwarecould,modelsshow,increaseenergyproductionbyatleast10%andgainsof14-16%arepossible[3]
u Theaveragegearboxfailurerateover10yearsisestimatedat5%[4].
1 0.75 0.5 0.25 0 2 4 6 8
ElectricalSystems
ElectronicControl
Sensors
HydraulicSystems
YawSystem
RotorBrake
MechanicalBrake
RotorHub
Gearbox
Generator
SupportingStructure/Housing
DriveTrain
UseCase
Aboutu Develops,Owns,andOperatesPowerGenerationandEnergyStorageUnitsinUSandEurope
u NorthAmerica’slargestindependentwindpowergenerationcompany
u Currentlyoperatingover4MWofwind
Headquarters
RegionalOffice
WindProject
NaturalGas
SolarProject
Storage
GearboxMonitoringApplicationTrial
u DesiredResults
u Predictgearboxfailureswith30-60dayadvancednotice
u Zeroorminimal falsepositives
u “DummyLight”output
u DataProvided
u 4yearsofhistoricaldatafromsiteof~100turbines
u 27datavariablesat10minuteresolution,novibrationvariablescollected
u Majorcomponentfailurelogs
GeneratedPredictionSignaturesforallCatastrophicGearboxFailures
RiskIndexforGearboxHealthTrialOutcomes
• Impendingfailure(redalert)predictionforcatastrophicfailure>1month
• Advanceddegradationwarning(amberalert)forfailuresis>2months
• Wehadzerofalsepositives,thatisnoalertswereraisedwhichdidnothaveafailurefollow
• Wehadzerofalsenegatives,thatisnofailuresweremissed
For100Turbines
67352
0
40
60DaysofWarning
500
1000
67Days
35Days
OutputOptions
OverallFleetHealth DetailedAssetView
OR
Connectoutputtoexistingsystems
GMSSCADA
CustomerUserInterface
Somesecondaryobservationsu Otherfailures(likelyblade),haveveryshortfailuresignatures
u Failurepredictionmadeindayscomparedtolonger signaturesforgearboxfailures
u SeasonalityofGearboxfailuresu Catastrophicgearboxfailuresshowhighcorrelationtoseasons
u Mostfailuresinsecondhalfoftheyear:Q1– 1,Q2– 1,Q3– 9,Q4–7
u Otherfailuresrelativelyindependentofseasonsu Catastrophicfailuresdistributedmoreuniformly:Q1– 3,Q2– 4,Q3– 5,Q4– 5
NextSteps
u Expandto5sites
u Pendingresultsofexpandedsites,committedtoenterprisewideroll-out
u Explorepredictivemodelsforothermajorcomponents
UsingMachineLearningandCognitiveFingerprinting™toDriveResults
Category KeyFeatures
Business Intelligence(BI)
• Centralizedanalysis• Uniformdatacollection• Averagevisualizations
RulesBasedModeling
• Fixedrulesmustaccountforalltypesoftransactionsinalltypesofconditions;leadtoruleproliferationandmanagementchallenges
• Maybegoodmeasuresforsomesimplesituations,butaverage(orevensub-par)measuresforothers
StatisticalAnalysis
• Identifiesdeviationsfrom“normal”• Moreaplatformformodelbuildinganddatascientists
thananalertgeneratingsolution• Notautomatedtoaccountforchangingconditions
PhysicsBasedModeling
• Asset-type specific• ModelbuildingIs averyhands-onprocessinvolving
laboratoryexperiments• Domainexpertsapplythesephysicalmodelsuniversallyto
assets
CommonApproaches
Enablesmachinestopenetratethecomplexityofdatatoidentifyassociations
Presentspowerfultechniquestohandleunstructured data
Continuously learnsnotonly frompreviousinsights,butalsofornewdataenteringthesystem
ProvidesNLPsupport toenablehumantomachineandmachinetomachinecommunication
Doesnot requirerules, insteadreliesonhypothesisgenerationusingmultipledatasetswhichmightnotalwaysappearconnectedorrelevant
BenefitsofCognitiveAnalytics
NLP: Natural Language Processing
CognitiveAnalyticsisinspiredbythewaythehumanbrainoperates:
ProcessesInformation
DrawsConclusions
Codifies Instincts&ExperienceintoLearning
BasicsofMachineLearning
Howdoyoulabelthese?
UnsupervisedLearning
UnsupervisedLearning
SM
MD
LG
SupervisedLearning
WH
GR
BL
Unsupervisedvs.SupervisedLearning
Unsupervised Supervised
Index Date Time AssetID Value2 5-Apr-10 7:01 750 8993 22-Mar-13 8:19 904 7927 20-Oct-14 8:26 545 745 10-Jul-12 7:38 552 8668 15-Sep-11 8:13 942 7429 1-Jun-11 8:44 900 7291 20-Jul-11 7:14 587 5054 12-Jul-10 7:36 765 9520 5-Sep-14 8:25 813 3944 30-Jun-11 7:07 983 71100 5-Oct-12 7:35 802 3466 12-Mar-10 7:39 726 4745 6-May-11 7:30 973 9884 10-Dec-12 7:17 504 6843 9-Jul-14 8:07 567 74
ActionTaken ComponentRepair Blade
Unknown BladeRepair Gearbox
Replaced GearboxReplaced Gearbox
NTF GeneratorGood GeneratorNTF BladeRepair GeneratorNTF GearboxNTF BladeRepair Gearbox
Unknown GearboxRepair BladeRepair Gearbox
TheSparkCognitionMethodology-CognitiveFingerprinting™
OurAlgorithms
Artemis• Proprietary regularization tool for
feature selection
• Automated class balancing
• Automated model selection
• Automated checks on overfitting
• Turn-key solutions with health index for industrial use cases
Iris• Proprietary clustering algorithm
• Optimal clustering of data leading to state generation
• Semantic indexing of states
• Classification from indexed states
• Turn-key solutions with health index for industrial use cases
Pythia• Proprietary regularization tool
for feature selection
• Genetic algorithms for optimizing neural networks
CognitiveAlgorithms-SparkArtemis™
Overall Vibration
MAXTemp
MinTemp
TensileForce
ShearStrength
FirstOrderFeatures SecondOrderFeatures
Wavelets
Enveloping
JointTimeFrequency
DoubleIntegration
CreatedFeaturen
ThirdOrderFeatures
CrestFactor
Integration
RunningAverage
Cauchy StressTensor
Created Feature1
CognitiveAlgorithms-SparkPythia™Artemis
ArtemisFeaturesTakeArtemisfeatures
Capturesthestateofandevolutiontofailure/eventincludingsubtleinfluencers
StartNeuralnetgeneticcomp
PredictBasedonaFunction
Significantly advancedcomparedtoexistingalgorithms
FeatureSelection
Automatically findsignificantdata
Adaptive&Self-learning
Identifymultiple topperformers
DefineRelationships
CognitiveAlgorithms-SparkPythia™
CognitiveAlgorithms-SparkIris™
WhichisBetter?
…Model1 Model1 Model1 Model1 Modeln
CognitiveAlgorithms-SparkIris™
Answer:Neither
Model1
Model2
Model3
Objectives Monitor Critical Assetsduringstartupsandcoast-downs
Predict RemainingUsefulLife
Analyzefailures, alertonimpendingfailures, optimizedesign
Client
Asset
BigUtility
Turbine Generator
BigUtility
WindTurbine
On-shoredriller
Electrical Submersible Pump
• Datacollectionfrommultipleassets
• Detectsfailures,graduatingtopredictions
• Self-learningsystemwithaccesstoin-contextadvisorypoweredbyIBMWatson
• RUL(RemainingUsefulLife)predictionandanomalydetection
• Automatedmodelbuilding,selection&management
• Insightsthroughdeeper-orderanalyses
• Failureidentificationandclassification
• Automatedfailurealerting• Criticalvariableidentification• Designandprocessoptimization
toreducespecificfailures
SolutionFeature
BusinessImpact
• Estimatedincreaseinproductivityof25%–30%
• 50XROI
• Estimatedsavingsof~40%inO&Mbudgets
• ~$2MMperyearfor100MWpowergenerationplant(wind),40XROI
• 3XincreaseinlifeofESPthroughpropermonitoringanddesign
• Savingsofupto$150,000perassetperyear,50XROI
OtherEnergySectorApplications
UseCase-ImproveSafetyandReduceRemediationCostThroughIntelligentPrognosticsu SparkCognition hasdeveloped anIBMWatson
“Advisory” application forAssetMaintenance
u SparkCognition’s poweredbyIBMWatsonwillallow
Directors ofMaintenance andtechnicians to:
§ Conductmachinetohumandialoguetotroubleshoot
faultcodes
§ Predictimpendingfailuresandfaults
§ Identifytherightfaultcodesandtroubleshootingtips
usingnaturallanguagequeries
§ Findsolutionstoproblemsandadvisetechnicians
§ Optimizeworkflowanddeliverrelevant
documentationforafasterturnaround
MachineLearning&CognitiveAnalyticscandeliverseveralbenefits
ExternalFactorsCanincorporateexternalfactors(e.g.environmentalissues suchasbirds&bats)
ScalabilityAutomatedmodel building capabilitydoesnotrequiremanualmodel buildingofeveryasset/component
In-contextRemediationAdvisor thatunderstandsnaturallanguagetohelptechnicalteams
SecurityOut-of-band, symptom-sensitive approachbeyond ITsecurity
AdaptabilityAdaptstonewandchangingconditionsautomatically
HigherAccuracyAutomatedfeatureenrichmentandextractionthatcandeliver betterinsightsandhigheraccuracy
Questions