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The Data Science behind Predictive Maintenance in Connected Vehicles
Esther VasieteSrivatsan RamanujamPivotal Data Science
Data Engineers Guild - MeetupJune-21, 2016
Picture credit (from L to R):http://www.techlicious.com/blog/ericsson-mobility-report-internet-connected-devices/http://www.mdpi.com/1424-8220/14/10/19260/htmhttp://www.thehindubusinessline.com/info-tech/other-gadgets/care-for-a-connected-car/article5777444.ece
Devices are Increasingly Connected
How can these connected devices in our home be smart enough to make daily life easier?
How does this……become this?
By recognizing this
And by processing this
Sensors + Other Unstructured Data
How can we know a tree has fallen on a power line before
the residents complain?
How can we use datato help prevent
accidents like the Macondo Disaster ?
Gene Sequencing
Smart Grids
COST TO SEQUENCE ONE GENOMEHAS FALLEN FROM $100M IN 2001 TO $10K IN 2011TO $1K IN 2014
READING SMART METERSEVERY 15 MINUTES IS
3000X MOREDATA INTENSIVE
Stock Market
Social Media
FACEBOOK UPLOADS250 MILLION
PHOTOS EACH DAY
In all industries billions of data points represent opportunities for the Internet of Things
Oil Exploration
Video Surveillance
OIL RIGS GENERATE25000DATA POINTS PER SECOND
Medical Imaging
Mobile Sensors
To realize this opportunity requires the right tools and techniques
Problem Formulation
Modeling Step
Data StepApps Step
Data Lake
Ingest
Business Levers
Dashboard/App
PL/X
Modeling• Data cleaning• Data Exploration• Feature
EngineeringModel Validation
Feedback loop for continuous
model improvement
Driver and Vehicle Meta
Data
Data Ingestion Platform
✔
✔ ✔ ✔ ✔✔ ✔ ✔
Data to Apps
Data Science Use-cases for connected cars
12
Data Science Use-Cases
13
● Predictive Car Maintenance‒ More accurately predict part failure‒ Optimize part repair and replacement schedule● Leveraging Driving Behaviour‒ Useful to differentiate insurance pricing based on driving
style‒ Optimize car design● Improving GPS Systems‒ Establish baseline for traffic congestion‒ Create more meaningful metrics for routing‒ Infer public transportation effects on traffic‒ Predict how long incidents would take to clear
● Predictive Power for Assistance Systems
‒ Optimize fuel efficiency‒ Predict the future state of a car in the next 2
minutes (starts, stops, emergency braking)● Traffic Light Assistance‒ Signal timing of traffic lights‒ Crowd sourcing of traffic signals‒ Optimize traffic light patterns to reduce
congestion
Preventive Maintenance for Connected Vehicles
14
On-Board Diagnostics
Diagnostic Trouble Codes (DTC)
Unscheduled repairs
AB1029 – Power steering pump replacementCT3408 – Wheel alignment
Solving the preventive maintenance problem
Automakers
Customer Satisfaction
Auto Repairs
Data Sources for Predictive Maintenance
VINTimestamp DTC CodeOdometer
SpeedAcceleration
Engine Temperature Engine Torque GPS
Coordinates etc.
VINDate vehicle in
Date vehicle outRepair code
Parts replacedWarranty claims
Repair Comments
Vehicle Data Car Repairs Data
Predicting Job Type from Diagnostic Trouble Codes (DTCs)
Time
Job Type: Transmission
Job Type: Transmission
EngineJob Type:
Regular check
DTC: B DTC: B,
P, C
DTC: U DTC: B DTC: B
DTC: B, P, C, U
DTC:P, B, U
DTC: P DTC: B DTC: B,P
DTC: B,P
Can the DTCs observed here predict
this Job Type?
Can the DTCs observed here predict this Job
Type?
Can the DTCs observed here predict this Job
Type?
Predicting Job Type: a multi-class classification problem
DF1210
DF1215
DF2980
AB1029
AB1622
AB1625
AB8622
CT3402
CT3408
CT3560
CT2409
Vehicle Features
Hierarchical Classification Framework
Vehicle Features
DF1210
DF1215
DF2980
AB1029
AB1622
AB1625
AB8622
CT3402
CT3408
CT3560
CT2409
Model Parallelism
One or more job on the same day
Multi-labeling problem
One-vs-rest classifiers built in parallel
1
0
0
1
0 1
0
Class 1
Class 2
Class 3
One-vs-Rest Classification
Red vs. Non Red
On Segment 1
Green vs. Non Green
On Segment 2
Blue vs. Non Blue
On Segment N
• Predictive maintenance problems are challenging because DTC signals are not always symptomatic of an ensuing repair.
• Given the hierarchical nature of repair codes, we built a two stage hierarchical classification framework comprising a top-down cascade of classifiers.
• Major system jobs can be predicted earlier to the repair date.
Key Takeaways
Reference Architecture
%%publishmodel info.
/
Microservices (Spring Boot)
/load_model/score_model
Spring Cloud Data Flow
vehicle data (streaming)
connector
exploratory data analysis & model
training
Rabbit/Kafka source
training (offline) scoring (online)
/
web or mobile app dashboard