OBDII data generated by a vehicle sensor network can be considered as a canonical proxy for Industrial IoT. Vehicle analytics, in addition to being useful in its own right (e.g. vehicle health monitoring, diagnostics, driver behavior modeling etc.), exhibits the same data characteristics (e.g. highly nonlinear data that varies rapidly in real-time, time delay effects in the data etc.) as an Industrial IoT application. In this case study, we demonstrate our analytics capabilities on a passenger car OBDII data. In particular, we demonstrate how one can use an "AI sensor" - a prediction system in places where no direct sensor measurement is available. Anand Deshpande Aniruddha Pant

1. Case Study for Vehicle
OBDII Data Analytics
(Canonical Problem for Industrial IoT)
Asquared IoT Pvt. Ltd.
www.a2iot.com
March 2017

2. Combination of engineering models and machine learning for
new insights, better predictions and real-time systems!
Preventive Maintenance
- e.g. Press shop, vehicle
analytics
Real-time analytics
-e.g.- Press shop, vehicle
analytics, welding process
Process & Efficiency
Improvements
-e.g.- Press shop,
Edge Computing
- e.g. Acoustic Analytics
IoT
Machine
Learning &
Analytics
Engineering
Models &
Domain
Knowledge
Expertise in finding Optimal solutions at
the intersection of multiple disciplines!
Asquared IoT: A Novel Approach to Analytics for Industrial IoT
Key Differentiator
• Machine Learning, Deep Learning
and Statistical Methods
• Mathematical Optimization
• Control Theory
• Industrial Engineering and
Operations Research
• Engineering Simulations
• High Performance Computing
Strengths
Application Areas
(with work-in-progress examples)

3. Profiles of the Founders
Anand Deshpande, Ph.D.
CTO
• PhD, Mechanical Engineering,
University of Colorado at Boulder,
USA 1999
• 17+ years of experience in world-
class research labs
Aniruddha Pant, Ph.D.
CEO
• PhD, Control Systems, University of
California at Berkeley, USA 2001
• 20+ years in application of advanced
mathematical techniques to academic and
enterprise problems.
• Expertise in complex multidisciplinary problems
• Specialization: Numerical Methods and Mathematical
Optimization, Engineering Simulations (FEM/CFD), Engineering
Design Optimization, Multi-Disciplinary Optimization, Parallel
Computing, High Performance Computing, Industrial
Engineering and Operations Research
• Several publications in tier-1 journals and conferences
• Global collaborations with top universities and national labs
• Past Experience:
➢ Research Scientist, Intel Labs, Bangalore
➢ Senior scientist and group leader, TRDDC/TCS
➢ Staff Engineer, Motorola, Chicago, USA
• Aniruddha is also Founder and CEO of AlgoAnalytics – a company
with strong expertise in AI
• Experience in application of machine learning to various business
problems; Cross-domain application of basic scientific process.
• Research in areas ranging from biology to financial markets to
military applications; Experience in financial markets trading; Indian
as well as global markets
• Close collaboration with premier educational institutes in India, USA
& Europe.
• Past Experience:
➢ Vice President, Capital Metrics and Risk Solutions
➢ Head of Analytics Competency Center, Persistent Systems
➢ Scientist and Group Leader, Tata Consultancy Services

4. Experiments with Vehicle Sensor Data (OBDII Data)
• Canonical proxy and “Controlled System” for Industrial IoT experiments
➢ Car with multiple sensors an example of a machine with sensors
➢ OBD reader an example of IoT platform that gathers and transmits data
➢ Data logging apps are examples of end systems, with data being used for analytics
• Data collected: About 150,000 data points with 63 features in each data point
Car with an array of sensors sending data to ECU
OBD Reader plugs into the OBDII
port of the car; Reads and transmits
(Bluetooth) OBD data
Android apps read and display the data; also
logged for offline analysis
Image source and credit: http://blog.asautoparts.com/5-common-symptoms-of-faulty-car-sensors/

5. No Direct Sensor available for Headlight State (ON/OFF)
• Canonical problem that represents predictions of
machine state in an Industrial IoT system
• Headlights the biggest load on the electrical system
• Reflected in the battery voltage
➢ The battery voltage also varies with engine RPM, as well
as the load on the engine and the alternator system
• Supervised Learning problem: Collected data with
headlight ON and OFF, and labelled the data
accordingly
• Problem Statement: Using the available OBDII data
(such as engine RPM, engine load, battery voltage),
predict if the headlight was ON or OFF
Headlight OFF
Headlight ON

6. Headlight Status Prediction: Training and Results
• Headlight State prediction solved as a classification problem using “Random Forest”
• Choosing the input parameters: Full set (but without any redundant parameters) of 26 features:
➢ Prediction accuracy = 84%
• Selected subset: By using the knowledge of the system, we choose 6 key parameters: Throttle
position, Engine RPM, Load on the engine, Fuel flow rate, Timing advance, Battery Voltage
➢ Prediction accuracy = 90% (kappa = 0.82; specificity = 98%; AUC = 91.6%; PPV = 98.3%)
➢ Demonstrates the importance of choosing a good feature set based on the problem understanding!
• Calculated feature importance, based on their correlation to the output (headlight state)
➢ Highest to lowest correlation: Battery Voltage, Engine RPM, Throttle Position, Timing Advance, Engine Load,
and Fuel Flow Rate
➢ Matches with the intuition!
The headlight status predicted with high accuracy (90%), and the feature
importance captured correctly!

7. Instantaneous Fuel Consumption
• Canonical problem for system efficiency metric that varies significantly and
non-linearly in real-time
• Instantaneous Fuel Consumption (km/l) varies significantly (from 0 when
idling to ~120 when “coasting” with no throttle input)
➢Varies with throttle input, engine RPM, engine load, vehicle speed, gradient of the road,
and many other parameters
• Challenges in this case study:
➢Highly non-linear time-series data with complex interactions within feature set
➢Primary Data Analysis based on the “physics of the system” showed time delay effects –
throttle input in one data instance takes effect at the next instance!
➢Dataset with many different “regimes” – different behaviour in idling, low speed
“crawling”, city traffic, highway cruising and “coasting” etc.

8. Prediction of Instantaneous Fuel Consumption: Results
• We used a Neural Network trained on
the ~150,000 data points and used to
predict on a randomly selected 1000 test
points
➢Our predictions has a R2 of 99.6 and RMSE
of 0.9 (on the scale of km/l variations of 0-
120 km/l)
➢This represents a very accurate prediction
system!
➢A single network that works on all regimes
and handled time delay effects
Accurate predictions for highly non-linear instantaneous parameter with time delay
effects – Key capability for Industrial IoT

9. Fuel Trim Prediction
• Engine Control unit (ECU) uses Fuel Trim to
maintain the stoichiometric air-fuel ratio in a
feedback loop
➢The O2 sensor values are used to estimate how
“rich” or “lean” the mixture was, and the fuel trim is
the “corrective” step used for next cycle
➢Short-term fuel trim is the immediate correction to
the air fuel ratio
➢Long-term fuel trim is the longer term correction
• Similar to instantaneous fuel consumption, fuel
trim is a highly nonlinear parameter with
complex relationships within the feature set
• In this case study, the objective was to predict
the short-term fuel trim

10. Fuel Trim Prediction: Results
• Dataset Used:
➢ Train – 1,37,889 observations 63 features
➢ Test – Blind 2000 observations 63 features
• Target variable was Short term Fuel trim
• The ECU computes only 49 discrete values of fuel trims
➢ This this can also be treated as a classification problem,
although in theory this is a regression problem
• Regression: Using Random Forest, we got R2 = 84.0%
• Classification: Using Random Forest Classifier got
accuracy of 97.7% and kappa score of 96.6%
➢ Kappa is a more robust statistical measure of the
classification performance
➢ 49 class classification is a challenge – successfully
handled by our models!
Accurate predictions for highly non-linear
instantaneous parameter – Key capability
for Industrial IoT

11. Conclusions and Summary
• Vehicle Analytics is an interesting problem in itself with many applications
➢ Vehicle health monitoring, diagnostics, driver behaviour model etc.
• However, it is also a canonical problem for Industrial IoT Applications
• “AI sensors” is a very important application for Industrial IoT
➢ Sometimes placing a direct sensor is infeasible due to the challenging operating environment and/or
costs involved
➢ A pre-trained AI model can simulate that sensor by predicting its value in real-time
• Our work here represents canonical case studies for:
➢ AI Sensors – Predicting values when no direct sensors are available (e.g. headlight status prediction,
instantaneous fuel consumption prediction etc.)
➢ Real-time prediction of highly dynamic parameters – e.g. fuel trim prediction
• Our models with consistent high prediction accuracy show our capabilities to develop AI models
on highly nonlinear, dynamic data sets with time-delay effect
➢ These characteristics make these models applicable to a wide variety of Industrial IoT problems!

12. Contact Us
For more information, please feel free to contact us:
• Anand Deshpande (anand.deshpande@a2iot.com), Bangalore, India
• Aniruddha Pant (aniruddha.pant@a2iot.com), Pune, India