Case Study for Vehicle OBDII Data Analytics

Transcription

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

2 Asquared IoT: A Novel Approach to Analytics for Industrial IoT Strengths Machine Learning, Deep Learning and Statistical Methods Mathematical Optimization Control Theory Industrial Engineering and Operations Research Engineering Simulations High Performance Computing Key Differentiator Engineering Models & Domain Knowledge IoT Machine Learning & Analytics Expertise in finding Optimal solutions at the intersection of multiple disciplines! Application Areas (with work-in-progress examples) 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 Combination of engineering models and machine learning for new insights, better predictions and real-time systems!

3 Profiles of the Founders Aniruddha Pant, Ph.D. CEO PhD, Control Systems, University of California at Berkeley, USA years in application of advanced mathematical techniques to academic and enterprise problems. Anand Deshpande, Ph.D. CTO PhD, Mechanical Engineering, University of Colorado at Boulder, USA years of experience in worldclass research labs 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 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

4 Experiments with Vehicle Sensor Data (OBDII Data) 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 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 Image source and credit:

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 Headlight OFF Problem Statement: Using the available OBDII data (such as engine RPM, engine load, battery voltage), predict if the headlight was ON or 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 R 2 of 99.6 and RMSE of 0.9 (on the scale of km/l variations of 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 R 2 = 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 Bangalore, India Aniruddha Pant Pune, India