the data science behind predictive maintenance for connected vehicles
TRANSCRIPT
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The Data Science behind Predictive Maintenance in Connected Vehicles
Esther Vasiete Srivatsan Ramanujam Pivotal Data Science
Data Engineers Guild - Meetup June-21, 2016
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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/htm http://www.thehindubusinessline.com/info-tech/other-gadgets/care-for-a-connected-car/article5777444.ece
Devices are Increasingly Connected
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How can these connected devices in our home be smart enough to make daily life easier?
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How does this… …become this?
By recognizing this
And by processing this
Sensors + Other Unstructured Data
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How can we know a tree has fallen on a power line before
the residents complain?
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How can we use data to help prevent
accidents like the Macondo Disaster ?
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Gene Sequencing
Smart Grids
COST TO SEQUENCE ONE GENOME HAS FALLEN FROM $100M IN 2001 TO $10K IN 2011 TO $1K IN 2014
READING SMART METERS EVERY 15 MINUTES IS
3000X MORE DATA INTENSIVE
Stock Market
Social Media
FACEBOOK UPLOADS 250 MILLION
PHOTOS EACH DAY
In all industries billions of data points represent opportunities for the Internet of Things
Oil Exploration
Video Surveillance
OIL RIGS GENERATE
25000 DATA POINTS PER SECOND
Medical Imaging
Mobile Sensors
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To realize this opportunity requires the right tools and techniques
Problem Formulation
Modeling Step
Data Step Apps Step
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Data Lake
Ingest
Business Levers
Dashboard/App
PL/X
Modeling • Data cleaning • Data Exploration • Feature
Engineering Model Validation
Feedback loop for continuous
model improvement
Driver and Vehicle Meta
Data
Data Ingestion Platform
✔
✔ ✔ ✔ ✔ ✔ ✔ ✔
Data to Apps
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Data Science Use-cases for connected cars
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Data Science Use-Cases
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● 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
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Preventive Maintenance for Connected Vehicles
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On-Board Diagnostics
Diagnostic Trouble Codes (DTC)
Unscheduled repairs
AB1029 – Power steering pump replacementCT3408 – Wheel alignment
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Solving the preventive maintenance problem
Automakers
Customer Satisfaction
Auto Repairs
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Data Sources for Predictive Maintenance
VIN Timestamp DTC Code Odometer
Speed Acceleration
Engine Temperature Engine Torque GPS
Coordinates etc.
VIN Date vehicle in
Date vehicle out Repair code
Parts replaced Warranty claims
Repair Comments
Vehicle Sensor Data Vehicle Repairs Data
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Predicting Job Type from Diagnostic Trouble Codes (DTCs)
Time
Job Type: Transmission
Job Type: Transmission
Engine Job 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?
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Predicting Job Type: a multi-class classification problem
DF 12 10
DF 12 15
DF 29 80
AB 10 29
AB 16 22
AB 16 25
AB 86 22
CT34 02
CT3408
CT 35 60
CT 24 09
Vehicle Features
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Two-stage Hierarchical Classification Framework
Vehicle Features
DF 12 10
DF 12 15
DF 29 80
AB 10 29
AB 16 22
AB 16 25
AB 86 22
CT34 02
CT3408
CT 35 60
CT 24 09
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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
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• 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
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Reference Architecture
%%publish model 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
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