Data Analytics of High Frequency Engine Rotational Speed Using MATLAB

Transcription

1 Data Analytics of High Frequency Engine Rotational Speed Using MATLAB AINA JAIN MEENAKSHI NISHIT JAIN SANDEEP MANDAL Maruti Suzuki India Ltd.

2 AGENDA Engine Management System (EMS) and its components Acquisition of sensor signals processed by EMS and need of validation Analysis of sensor data by Conventional Approach and its limitations Analysis of sensor data using MATLAB GUIDE and its advantages

3 PROBLEM STATEMENT Big data processing and analysis to validate the signals processed by Engine controller To perform this analysis within stipulated time to meet the development schedule.

4 Engine Management System ECU Actuators Optimal control of Actuators Engine Condition Information Sensors -Ignition Coil -Injectors -Throttle Motor -Fuel Pump, relays, etc. EMS (Engine Management System) - Analog Inputs - Digital Inputs - Angular Position Sensor Inputs Engine Engine Response Control Engine Condition detection i.e. engine temperature, piston position identification etc.

5 Acquisition & Processing of Angular Sensor Input Actuators -Ignition Coil -Injectors -Throttle Motor -Fuel Pump, relays, etc. Optimal control of Actuators ECU EMS (Engine Management System) Engine Condition Information Sensors Engine ECU - Analog Inputs - Digital Inputs - Angular Position Sensor Inputs Engine Engine Response Control Engine Condition detection i.e. engine temperature, piston position identification etc.

6 Sensor Low and High Frequency X rpm Y rpm 4 teeth detected in t seconds Z rpm 9 teeth detected in t seconds 34 teeth detected in t seconds Detection points increases with increasing Engine RPM i.e. Z > Y > X

7 Data Analysis using Conventional Approach

8 CONVENTIONAL APPROACH: DATA CAPTURING Text data exceeds the worksheet limit. MULTIPLE IMPORT REQUIRED! OR

9 CONVENTIONAL APPROACH: DATA ANALYSIS Divide the text File and import into multiple worksheets.

10 CONVENTIONAL APPROACH: DATA ANALYSIS OH NO! I put the wrong formula. How can the Engine rpm be negative? Many calculations in numerous sheets. Very much prone to Manual error.

11 Data Analysis Flow using Conventional Approach OH NO! Microsoft Excel crashed again. I need to perform the calculations again. Large data processing causes the system to crash.

12 Data Analysis Flow using Conventional Approach Selected data sets are plotted to find the exceeding limits. Excel Based plots are used to check the anomaly points visually.

13 SUMMARY: Limitations of Conventional Approach

14 LIMITATIONS OF CONVENTIONAL APPROACH Capture Data every x micro-seconds Import Data in Excel Format Perform Calculations based on captured data Plot the data and Identify Anomalies

15 LIMITATIONS OF CONVENTIONAL APPROACH Capture Data every x micro-seconds Importing of all samples takes large time (samples numbers~ 50 Million) Import Data in Excel Format Perform Calculations based on captured data Plot the data and Identify Anomalies

16 LIMITATIONS OF CONVENTIONAL APPROACH Capture Data every x micro-seconds Importing of all samples takes large time (samples numbers~ 50 Million) Import Data in Excel Format Large Processing Time for manual calculation of different parameters based on this huge data Perform Calculations based on captured data Plot the data and Identify Anomalies

17 LIMITATIONS OF CONVENTIONAL APPROACH Capture Data every x micro-seconds Importing of all samples takes large time (samples numbers~ 50 Million) Import Data in Excel Format Large Processing Time for manual calculation of different parameters based on this huge data Perform Calculations based on captured data Manual intervention required to review test results Plot the data and Identify Anomalies

18 LIMITATIONS OF CONVENTIONAL APPROACH Capture Data every x micro-seconds Importing of all samples takes large time (samples numbers~ 50 Million) Import Data in Excel Format Large Processing Time for manual calculation of different parameters based on this huge data Perform Calculations based on captured data Manual intervention required to review test results Plot the data and Identify Anomalies

19 Data Analysis using MATLAB Application

20 Data Analysis using MATLAB Application MATLAB GUIDE Set of tools for creating user interface (UI) environment Generates a program file to control the UI behavior Provides a callback approach to make the GUI functional HIGH FREQUENCY DATA ANALYSIS TOOL Graphical UI to perform all calculations Plot the Data along with coordinates of anomaly points Generate the excel data (Refined Data) Flexibility to clear and re-plot or save the plot

21 Step 1: Selection of Raw data captured from Vehicle Click here!

22 Step 1: Selection of Raw data captured from Vehicle To process further, file selection is necessary Else the GUI highlights the need of selection

23 Step 2: Import Raw data to MATLAB workspace Click here!

24 Step 3: Create Plot of Test Results Data import complete. Click here! Process ahead to plot the data

25 Plot of Test Results

26 Plot of Test Results

27 Step 4: Export results to trace back the anomalies Click here!

28 Additional Utilities: Save/Clear the Plots Plots can be Saved for documentation Plots can be Cleared to re-plot new data

29 permission of MSIL. Why using MATLAB is a better Approach Data Analysis using Conventional approach

30 permission of MSIL. Why using MATLAB is a better Approach Data Analysis using Conventional approach

31 permission of MSIL. Why using MATLAB is a better Approach Data Analysis using Conventional approach

32 permission of MSIL. Why using MATLAB is a better Approach Data Analysis using Conventional approach Data Analysis using MATLAB GUI

33 permission of MSIL. Why using MATLAB is a better Approach Data Analysis using Conventional approach Data Analysis using MATLAB GUI

34 permission of MSIL. Why using MATLAB is a better Approach Data Analysis using Conventional approach Data Analysis using MATLAB GUI

35 permission of MSIL. Why using MATLAB is a better Approach Data Analysis using Conventional approach Data Analysis using MATLAB GUI

36 Advantages of MATLAB Application Very Fast Calculations * 60 minutes job done in less than 2 minutes! * 200 MB raw data refined to 1MB of useful information in just 3 clicks! Accurate Results * Fool-proofing from manual errors! Saves a lot of manual Effort * MATLAB takes the pain, User finds the gain!

37 Thank You! Q & A