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© 2016 DataRPM – Proprietary and Confidential11
Cogn i t i v e P r e d i c t i v e Ma i n t e n an c e ( C PdM ) P l a t f o rm
Fo r I n d u s t r i a l I o T ( I I o T )Predictive Maintenance for Automotive Industry
© 2016 DataRPM – Proprietary and Confidential22
Predictive Maintenance - Automotive
2
Quality Control
• Assembly Line Uptime• Production Quality / Warranty• Early Detection & Prevention• Paint Shop Quality
Service & Maintenance
• Maintenance & Fault Detection Performance of Assets
• Service SLAs
Increase Productivity
• Increase uptime of assets• Increase Jobs per Hour• Lower Downstream costs of lost
productivity
• Spare Parts Availability• Lead time indicator for expensive
parts
Optimize Inventory Customer Satisfaction
• Enhanced Reliability• On time delivery• Reduced maintenance problems
Reduce Warranty Costs
• Equipment Warranty• Reduced Malfunctions• Decreased Recalls
© 2016 DataRPM – Proprietary and Confidential33
Business Value We Help Unlock From PdM For Automotive Industry Ecosystem
3
Minimize Insurance
Risks
Prevent Car Breakdowns
& Part Failures
Prevent Quality On Assembly
Line & Paint Shop
Minimize Warranty
Claims
Minimize Car Maintenance
CostsPredicting Potential
Issues With Assets Ahead
Of Time Optimize Parts
Inventory and Field
Resources
Predictive maintenance will help companies save $630 billion by 2025
McKinsey
© 2016 DataRPM – Proprietary and Confidential44
PdM Is Not New! But What Has Changed Now For $$$?
4
Sensors Everywhere And Now Connected To Internet
Big Data Platforms To Collect, Store And Process Data At Scale
Price of sensors are rapidly dropping close to $1.The number of sensors shipped has increased more than five times in 2 years from 4.2 billion in 2012 to 23.6 billion in 2014 and growing rapidly!
Advancement In Data Science Technologies
Meta Learning
Our Customers Our Partners
Us
© 2016 DataRPM – Proprietary and Confidential55
And… Data Science Is Hard For Machine Data
5
Weak Signals To NoiseThe true signals hidden in the data
scattered over millions of data points coming from various different
sensors at different time windows
Obsolete ModelsThe machine data patterns change too often. Models get obsolete by the time they are productionized
No Labeled Training DataNo labeled data for training, lack
of knowledge about machine signals and monitoring each
sensor in isolation doesn’t work
Not Human ScaleTimely and accurate insights is impossible to get by manually
analyzing data samples
Besides there is a 200k - 1M Shortfall of Data Scientists
© 2016 DataRPM – Proprietary and Confidential66
Traditional Approach Of Analysis Don’t Work Anymore
Sensors individually monitored for Spikes Manually generated alert rulesLarge Workforces required to filter signals from noisy alerts
Manual rule-based monitoring solutions don’t deliver PdM ROI & TCO is high!
Data Science & Machine Learning driven approach is the only way to efficient PdM
All sensors analyzed continuously in combination to learn machine states
Automatically Identify only the critical signals
Recommend prescriptive actions & learn from results
© 2016 DataRPM – Proprietary and Confidential77
The Only Solution: Teaching Machines to do Machine Learning
7
MLML Meta-Learning on Machine-Learning
DataRPM is one of the first Enterprise-grade applications of Meta-Learning.Massive Economic Value is thusdelivered through Cognitive PredictiveMaintenance (CPdM) for Industrial IoT &Manufacturing applications.
How Machines learns to do ML:“Algorithmic Survival-of-Fittest”
7
1Run many live automated ML experiments on datasets in parallel
2 Extract meta-data from every experiment based on
3 Train an Ensemble of Models on this meta-data repository
4 Apply models to predict the best algorithms & hyper-parameters
5 Build machine-generated and human verified ML models for PdM
Dataset Characteristics
Selected Features
Selected Algorithm
Selected Hyper-Parameters
Resultant Value Of Objective Function
© 2016 DataRPM – Proprietary and Confidential88
We deliver a Cognitive Predictive Maintenance [CPdM]software platform for the Industrial IoT [IIoT] that automates Data Science with Meta-Learning on Machine Learning [MLML]
to solve large-scale problems
AutomotivePower | Energy | Utilities
ManufacturingHealthcare
Oil | GasTransportation | Travel
Se
cto
rsR
esu
lts
Results In as quickly as
1/30th
the Time
with up to
30%in Cost
Savings
at least a
300 %increase in
Prediction Power
© 2016 DataRPM – Proprietary and Confidential99
What Makes Us Different From Other PdM Solutions?
9
§ We are designed specifically to handle the challenges of doing PdM for IIoT
§ We cognitively automate the Data Science process at mass-scale
§ We utilize Meta-Machine-Learning [MLML] to teach machines to teach themselves
§ We Operationalize the best Ensembles & continually modify in-line & real-time
§ We partner with our customers to solve real problems and deliver ROI quickly
© 2016 DataRPM – Proprietary and Confidential1010
High-Level Automated Workflow for CPdM with MLML
10
Sensors (Batch Time Series Data)
Temperature
Pressure
Accelerometer
Noise
Feature Engineering
Features EnggMeta-
Learning
ALIGN
RESAMPLE
IMPUTEMISSING VALUES
ROLL-UP
MEAN / STDEV
MIN / MAX
CHANGE RATE
FFT / DWT
Anomaly Detection
ClusteringMeta-
Learning
Labeled Training Data
USER VALIDATION
FREQUENT PATTERNS SEEN IN
PRIOR FAILURES
Classifier / Regression
Meta-Learning
ModelGen
Cross-Validate
Is Quality Good?
Tune Params NO
TestCorrectlyClassified
?
YES
Add To New Training Set
NO
Prediction Modeling
Sensor (Streaming Data)
Model Ensembles
Visualize on DataRPM
Visualize on Tableau etc.
Integrate via APIs into
ServiceCloudFeedback
Feedback
Production
CLASS BALANCEUp-sample Minority Classes
Down-sample Majority Classes
CONNECTORS FEATURE ENGG RECIPE
SEGMENTATION RECIPEINFLUENCING FACTORS
RECIPE
PREDICTION RECIPEAPI FRAMEWORK + SCORING RECIPE +
RECOMMENDATION RECIPE + DASHBOARD
MLML
MLML
MLML
Prior Maintenance / Service Action
Records
© 2016 DataRPM – Proprietary and Confidential1111
Use Case | CPdM for Connected Cars
MANUALDATA ANALYSIS
CHALLENGE
Resulted in poor prediction with lots
of false positives
Sensors were
monitored individually
Manual rules werewritten toraise alerts
Impossibleto capture
all failure scenarios
ALL sensors
in parallel
Monthsof sensor dataused to train Data Models accurately
< 2 WeeksHighly Accurate
Prediction Model
Automated building of thousands of models in parallel to deliverthe optimal model
Predictions of Breakdowns & RecommendMaintenance
USE CASE
Identify the indicators of
malfunction for Connected Cars
011101110010101
0110110
DATA OVERLOAD
300%Increase in Prediction Accuracy
ResultsResults
Delivered30 X
Faster
Each sensor records multiple
data points in millisec range
Unique Sensor Recordings in
HDP Platform
75%Reduction inBreakdowns
Identified
Accuracy SavingsSpeed
AUTOMATEDDATA SCIENCE
SOLUTION w/ MLML
A Luxury Automotive
Company
© 2016 DataRPM – Proprietary and Confidential1212
Use Case | CPdM For Assembly Line Quality
MANUALDATA ANALYSIS
CHALLENGE
AUTOMATEDDATA SCIENCE
SOLUTION
Resulted in poor prediction with lots
of false positives
Sensors were
analyzedindividually
Manual rules werewritten toraise alerts
Impossibleto capture
all failure scenarios
ALL sensors
in parallel
Monthsof sensor dataused to train Data Models accurately
< 4 WeeksHighly Accurate
Prediction Model
Automated building of thousands of models in parallel to deliverthe optimal model
Prevent defects and reduce recalls
and warranty claims
USE CASE
Identify the indicators of poor quality in
production assembly line
Each sensor records multiple
data points in millisec range
011101110010101
0110110
Unique Sensor Recordings in
HDP Platform
DATA OVERLOAD
Results30 X
Faster
328%Increase in Prediction Accuracy
ResultsSignificant
Reduction inBad Parts
A Luxury Automotive
Company
© 2016 DataRPM – Proprietary and Confidential1313
CPdM Platform For Connected Cars
13
Prediction TimeframePredicted Stage (Criticality) For All Connected Car (Vin numbers)
Current Sensor Readings For Selected Car
Available Info For Selected Car
Components Requiring Predicted Maintenance For Selected Car
Maintenance AlertsWith Recommended Time To Maintenance For Selected Car
Predicted Sensor Readings For Selected Car
© 2016 DataRPM – Proprietary and Confidential141414
T H A N K YO UF o r M o r e I n f o r m a t i o nE m a i l m a r k e t i n g @ d a t a r p m . c o mV i s i t w w w . d a t a r p m . c o m