Cognitive Predictive Maintenance for Automotive

© 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


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


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


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


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


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


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


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


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


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


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


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


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


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