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Why are we discussing this today
v The paint shop corresponds to nearly third of the time taken in the automotive build process.
v Automation and Manual Processes: The process of paint application involves a large number of steps with a high degree of automation along with manual processes and inspections.
v As per research, 40% of the cars that exit a paint shop are likely to undergo some sort of rework (either on a part or in total).
v Environmental Impact: Paint is one of the highest waste generating process with a major environmental impact.
v Capital Intensive: Setting up and changes to the paint shop is a capital intensive process (almost a third of the total cost).
v Automation is generating large amount of data which is unused for asset analysis3
Henry Ford Once Famously Said
4
“Any customer can have a car painted any colour that he wants so long as it is black.”
Customer Preferences Have Changed Over Time
Car Colors have always reflected the mindset of the era and hence have a huge impact on the customer purchase mindset
Customer tastes have transformed over time and car manufacturers have to match up to their requirements with no tolerance in drop of quality
5
All Blacks 1920’s
Wacky 1960’s
Apple Effect of 2000’s
Observations
• Entire Paint Shop Process is a 7+ hour multi-step process with a number of manual and automated steps
• Defects in the paint process can occur due to:• ExternalInfluencersliketemperature, dirt, particles, paint quality, mix etc• ProductionLineInfluencerslikefault in the robotic arms• ManualInfluencerslikelindt particles, hair, PVC particles fromworkers
• There are multiple inspections done on the quality of the paint of the car both manually and through automated techniques to diagnose defects
• In case a defect is observed, it is recorded in the system and the car is sent back for rework
• On average, each manufacturer could spend >£76,000 in re-work costs for every 10,000 cars produced.
***Based on 63 million manufactured cars produced globally in 2012***8
Issues Are Many But Solutions Are Few
© 2016 DataRPM – Proprietary and Confidential 9
Thenumberofobserveddefectsandassociatedfactorsinthepaintshopareextremelyhigh.
Mostoftheanalysisisdoneafter thedefectisobserved.Thiscausesline disruptions and rework.
Mostofthefactorsareattributedtosomeexternal influence butthetrue root cause isnotdeterminedwithcertainty.
Changesinmultiplefactorswhichmayhavehappenedtogetherarenot takenintoconsiderationduetothemanualapproachofanalysis.
Applying Preventive Maintenance
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Man Machine Method Material
IdentifythePossibleCauseAcrosstheEntireProcess
ConductanAssetLevelAnalysisforeachRobotic
Arm
PredictWorkingStatesofallthe
assetsindependently
© 2016 DataRPM – Proprietary and Confidential 11
Change in Scale requires a Change in Approach
AnalyzeRootCauseoftheErrors
DetectAnomaliesinmachinedata
AnalyzeRootCauseofWithinMachineError
CreateaPredictionModelforEveryMachine RecommendActionables
ApplyFeatureEngineeringtoenhancemachinedata
SegregateMachineandMethod
Transform
Enhance
TimetoFrequencyDomain
Mean,Skewness,Kurtosis
UnsupervisedLearning
IntrinsicFactors
ExtrinsicFactors
Predictpossiblefailuresinadvance
ReduceDowntimeandimprovequality
ImproveSystemSettings
AdjustTemperatureRange
IncreasePaintDensity
Process Level Analysis - Root Cause Analysis
12
ApplyDataScienceTechniqueswhichworkonlargeamountofdata,usemachinelearningtolearnandcorrelatemultipledatapatternsandfinallycreateassetlevelpredictivemodelsaretheneedofthehour.
AnalyzeRootCauseoftheErrors
• SimultaneouslyobservethechangesintheVitalX’sacrosstheentireproductionlinetodifferentiate betweennormalandabnormalworkingconditions
• UsingAssociativeMiningRules,determinecorrelationsbetweendefectsandobservedbehaviorinordertodeterminecandidates of causality
• Thismaybelinkedtooccurrence ofacertainoperator,aparticulartimeofday,linetemperature,numberofunitsprocessedbeforethedefect,aparticularpartofthecar,roboticarmsettingsetc.
• Thefactorsneedtobeobservedindependently and in combination todeterminetheeffectofinteractionacrossthecandidateX’s
Asset Level Analysis – Engineering Features on Sensor Data
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• Fromthepreviousanalysis,therearelikelytobecertain machines and processes whichwillgethighlightedasVitalXcandidates.
• Owingtohighdegreeofautomation,themachinearmsarefittedwithmachine sensor data.Thesedatapointsneedtobecapturedandanalyzedforpotentialdeviations.
• BuildingaPredictiveMaintenancePipelinewillallowyoutoensurethatdeviationsinthosemachineandprocessescanbecapturedearly in the cycle anddonotleadtoqualitydisruptions.
ApplyFeatureEngineeringtoenhancemachinedata
TimetoFrequencyDomain
Mean,Skewness,Kurtosis
• Feature Engineering isessentialtounderstandtheeventsthatprecededthesensorvalueatagiventimeaswellasdeviationsinthereadings.
• Thesefeaturearecriticaltodifferentiatenormalworkingconditionstoanomalies
Choiceoffeatureswillvary fromassettoasset.Hence,itisimperativethatmultiplefeaturesarecreatedandthealgorithmsdeterminewhichfeaturesneedtobeselectedforthefinalmodel.
Asset Level Analysis – Determining the Anomaly State
14
DetectAnomaliesinMachineData
AnalyzeRootCauseofWithinMachineError
UnsupervisedLearning
IntrinsicFactors
ExtrinsicFactors
• BasedontheFeaturescreatedinthepreviousstep,weneedtodifferentiatebetweenthenormalandabnormalworkingconditionsofthemachine.
• TherightoperatingcriteriacanbeinfluencedbymanualrulesbuttheunsupervisedlearningalgorithmwilldeterminethevariousoperatingstateswithintheMachine
• AssociatedConditionsinthesestateswillhighlightvariousintrinsicandextrinsicfactorswhicharecausingthemachinetobeinthatstate
• Thiswillfurtherhelpdeterminetherootcauseofdeteriorationofthemachinehealth
Asset Level Analysis - Preparing for the Future
15
CreateaPredictionModelforEveryMachine
Predictpossiblefailuresinadvance
ReduceDowntimeandimprovequality
• Allthisanalysisculminateswithanassetlevelpredictionmodelwhichdeterminesthelikelymachinestateinthenearfuture.
• Themodelisregularlytweakedbyafeedbackloopwhichtunesitbasedonthechangingworkingconditionsofthemachine.
• Ratherthanpost-mortemmonitoring,thefloormanagerscanbebetterpreparedtotweakprocessesiftheyobserveanysignificantdeviationinthemachine.
RecommendActionables
ImproveSystemSettings
AdjustTemperatureRange
IncreasePaintDensity • Further,basisthestrengthofthemodelvariables,variousrecommendationscanbemadetothelinemanageraroundimprovingtheprocess.
• Thesetooaredrivenbymachineobservedpatternsandbasistheassetconditions
The Solution: Meta-Learning on Machine-LearningTeaching Machines to automate Machine Learning
© 2016 DataRPM – Proprietary and Confidential 16
DataRPM is the first Enterprise-Grade application of Meta-Learning for Machine Learning & Data Science Automation.
Massive Economic Value is thus delivered via our Cognitive Predictive Maintenance (CPdM)software platform for Industrial IoT & Manufacturing applications.
MLML
Meta-Learning on Machine-Learning
Automating how Machines learn to do Meta Learning:“Algorithmic Survival-of-Fittest”
© 2016 DataRPM – Proprietary and Confidential 17
1 3
4
5
2
Run many live automated ML Experiments in parallelfor each Asset
Extract Meta-Data from every Experiment:
• Dataset Characteristics• Selected Features• Selected Algorithm• Selected Hyper-Parameters• Resultant Value of Objective Function
Train an Ensembleof models via our
Meta-Data Repository
Apply Models to Predict the best Algorithms & Hyper-Parameters
for each asset
Build Machine-Generated& Human-Verified Models
for each & every Asset
DataRPM End-to-End Workflow
© 2016 DataRPM – Proprietary and Confidential 18
Consumption
APIs
Micro Apps Framework
Security
DataManagement
Data Sync
Data Lake
Metadata
Machine Learning& Analytics
Spark Engine
Workflow Builder
Data Science Recipes
Meta Learning
Natural Language
Visualization
IIoT Sensors Data
Enterprise Asset Management Systems
RDBMSDatasources
Hadoop
Insights App
Discovery App
Admin App
PdM Apps
DataRPM Key Differentiators
© 2016 DataRPM – Proprietary and Confidential 19
Natural LanguageSearch
DistributedComputing
API Driven+ + + + +Digital
TwinMeta
LearningOpen-box
SolutionCognitiveAutomation+
$250M+ in Annual Cost Savings identified
20
Average
> 80%+increase in Prediction Power
Go Live in
Days - WeeksAverage
30%Cost Savings
DataRPM End-to-End Full Tech Stack
21© 2016 DataRPM – Proprietary and Confidential
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