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Machine Vision Analysis of the Energy Efficiency of Intermodal Freight Trains: Sibley Site Update

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Machine Vision Analysis of the Energy Efficiency of Intermodal Freight Trains: Sibley Site Update

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    2. Wayside Machine Vision System

    3. Sibley Site Equipment Layout

    4. Machine Vision Site Equipment Camera Enclosure on Tower to House CCD Camera Bungalow Containing Computers and Train Detection Circuitry 30ft Tower for Set of Halogen Lights and Wireless Antennas

    5. Train Detection System

    6. Machine Vision Site Equipment 19" Equipment Rack In Bungalow

    7. Site Activation Scenario System in Wait State and Continuously Monitoring Train Detectors Train Approaches Machine Vision Site (West/East Bound) Locomotive Triggers (East/West) Presence Detector Lights are Turned On If Photocell Does Not Detect Daylight Camera Turns On and Focuses Region of Interest on Target Exposure Adjustment is Made Based on Target Only Camera Region of Interest is Returned to Entire Scene Locomotive Triggers (East/West) Wheel Detector Video Recording is Started to Capture Background Appearance Train Passes by Camera and is Recorded Against Background Video is Taken at 30f/s and Buffered to Memory Video Recording is Ended and Video Frames are Stored If Lighting was Used, Lights are Turned Off System Returns to Wait State for Next Train Arrival

    8. Details of Activation Scenario System in Wait State Continuously Monitoring Train Detectors The Train Status Monitor (TSM) checks the state of the detectors Uses data acquisition board inside the pc connected to all detectors through the Programmable Logic Controller (PLC) West Presence Detector West Wheel Detector West Loop Detector East Loop Detector East Wheel Detector East Presence Detector Controlled by a custom program - DIControl Frequency Rate of monitoring is admustable (currently 1/10 sec) When not looking at detectors, control of processor is returned to OS

    9. Details of Activation Scenario Train Approaches Machine Vision Site (West/East Bound) Locomotive Triggers (East/West) Presence Detector Presence detector pulse is received by the PLC Presence detector pulse is also captured by the Train Status Monitor

    10. Details of Activation Scenario Lights are Turned On If Photocell Does Not Detect Daylight PLC enables the power to the lights If the photocell is detecting light, it inhibits the power signal to the lights Lights now staggered to more evenly distribute lighting (initial config)

    11. Details of Activation Scenario Camera Turns On and Focuses Region of Interest on Target Camera is started by custom software – pgrAperture Because video frames of the background are needed prior to the train, the camera must adjust exposure without the presence of the train The target is designed to reflect light similarly to the side of the train The camera view is then restricted the region of the target only

    12. Details of Activation Scenario Exposure Adjustment is Made Based on Target Only The camera parameters are allowed to adjust to the lighting on the target, with the exception of the shutter speed The shutter speed is set to a value (2ms) determined experimentally P:events image motion blur due to the moving train (normal camera below) Camera Region of Interest is Returned to Entire Scene Before train reaches wheel detector

    13. Details of Activation Scenario Locomotive Triggers (East/West) Wheel Detector Wheel detector pulse is captured by the Train Status Monitor Wheel detector is placed 75ft from camera to start recording prior to train Video Recording is Started to Capture Background Appearance These frames are used to create a model of the background by the TMS

    14. Details of Activation Scenario Train Passes by Camera and Is Recorded Against Background With exposure set by target, train should not appear dark even if background is bright

    15. Details of Activation Scenario Video is Taken at 30f/s and Buffered to Memory To continuous capture 30f/s, frames are buffered before converting to video Video Recording is Ended and Video Frames are Stored Videos are stored in multiple 1Gbyte segments for OS requirements If Lighting was Used, Lights are Turned Off System Returns to Wait State for Next Train Arrival

    16. Now Testing System Automation

    17. Demo In Computer Vision and Robotics Lab of Duplicate Image Acquisition Computer Adjusting to Ambient Lighting Conditions and Recording Video

    19. Train Monitoring System Input : A video of an intermodal freight train Output : Length of gaps between the load Improve aerodynamic efficiency of the train Large savings on fuel costs

    20. Input: Train Video

    21. Challenge #1 Varying outdoor imaging conditions

    22. Challenge #2 Different Types of Containers

    23. Challenge #3 Computations involved need to be fast to handle railroad traffic. 1 day has 20-30 trains on both sites 1 train is completely captured in approx 5000 frames 1 frame is 640x480 pixels Need to process all frames

    24. Method: Step 1 Estimate initial train velocity in pixel shifts/frame

    25. Method: Step 1

    26. Method: Step 1

    27. Method: Step 1

    28. Method: Step 1

    29. Method: Step 1

    30. Method: Step 1

    31. Method: Step 1 Repeat for consecutive frames

    32. Juxtapose stripes from consecutive frames to generate panorama Method: Step 2

    33. Method: Step 2 Post process panorama to remove background near edges

    34. Method: Step 3 Classify each container into 3 different types

    35. Method: Step 4 Obtain gap lengths and histogram for analysis

    36. Results Tested on 110 train videos with 3 different types of containers 573 Type 1 (Double Stack) containers 515 Type 2 (Trailers) containers 10 Type 3 (Single Stack)containers Gap detection is accurate to approx 1 ft error Confusion matrix for load type detection

    37. Data Analysis System Tristan Rickett

    38. Outline Train Resistance Train Scoring System Description Inputs: AEI, TMS, UMLER AEI Current Setup at LPC and Sibley Using Available AEI Data for Sibley Videos Data Transfer Matching TMS file with AEI data Future Work

    39. Train Resistance Train Resistance considers the effects of inertia that tend to keep a body at rest and the effects of friction that cause it to lose momentum once moving The general equation for train resistance is the following: R = AW + BV + CV2 A = Journal Resistance B = Flange Resistance C = Aerodynamic Resistance

    40. Sources of Aerodynamic Drag Gap lengths Varying heights Rough surface Drag area of the lead locomotive Lack of streamlining

    41. Current practice in intermodal freight train loading Slot Utilization is metric used to measure the percentage of the spaces (a.k.a. slots) on intermodal cars that are used for loads However, this metric does not account for the size of the slot and the size of the load

    42. Slot Efficiency Methodology Slot Efficiency: comparison of the difference between the actual and ideal loading configuration This metric is similar to slot utilization except that it also considers the energy efficiency of the load-slot combination

    43. Train Scoring System (TSS) The purpose of the train scoring system is to evaluate an intermodal train’s loading efficiency and provide an aerodynamic coefficient to estimate fuel consumption The results from the TSS can aid terminal managers in creating more fuel-efficient trains

    44. Flow of TSS

    45. TSS Inputs Mini-UMLER Database has the database with all the railcars and their equipment Gap-length files contain the train’s loadings and the gap lengths AEI (Automatic Equipment Identification) data provides a list of the train’s equipment and axle timestamps

    46. Mini-UMLER Database The information contained in the database includes the following: Car Initial (e.g. DTTX) Car Number (e.g. 749452) Car’s Outside length in feet (e.g. 270 ft) Car Type (e.g. S) Car Attribute 1 (e.g. 1) Car Attribute 2 (e.g. 6) Car Attribute 3 (e.g. 2)

    47. Progress Made Improved how the code produces the output It is now embeddable so that it can run from inside another program Formatted a newer UMLER database Integrated TSS with the proposed system automation A database we A database we

    48. AEI Data Collection at LPC The TSS was originally programmed to use AEI that had axle timestamp values like the PRT AEI reader at LPC At Sibley, we have begun collecting videos since last December but the problem is that the AEI data does not have timestamps

    49. Addressing Present AEI Data Acquisition If the hot-box data is available, it would be worth calculating our own timestamps using this available data With the new AEI reader for the Sibley site, it is recommended that it provides axle timestamps

    50. Determination of Axle Timestamps Using kinematics equations and some assumptions, we can determine the timestamps. Using di = vi x ti + 0.5aiti2 Distance, di, is provided in the AEI data Assume velocity is around 20 to 25 mph (or 29.3 to 36.7 ft/sec) No acceleration

    51. Determination of Axle Timestamps Having an acceleration of zero cancels out half of the equation allowing di / vi = ti . Because axle timestamp values are cumulative, the final equation will be ti = ti – 1 + di / vi

    52. Using a Wheel Detector for Timestamps Use one wheel detector already installed at the site to measure axle timestamp values. System would be triggered by one of presence detectors The difficulty is finding a place in the automation where the AEI data can be combined

    53. Matching the Scoring Data with CAD Data All videos and AEI are named according to the date and time of when they were captured With the date of the scored train, it can also be attached to computer-aided dispatching data so terminal managers can review the efficiency of their trains loaded at their yard

    54. Matching Data This shows how the four available data types can be used to match into the TSS and help filter the results… For example… Because TMS uses the date and time for its file name from the acquisition system that dates the time and the AEI has date and time, we can match AEI and TMS files to be scored in TSS Also, the AEI car information can be compared to the UMLER database to see if the cars in the train are F,P,Q, or S-type (which are the intermodal car types). If they are something else, than the AEI data and TMS output with the non-intermodal cars will be discarded. The lead locomotive in the AEI data can then be used to match it to the CAD data where it can be attached so that the proper personnel can review the results of the scoring systemThis shows how the four available data types can be used to match into the TSS and help filter the results… For example… Because TMS uses the date and time for its file name from the acquisition system that dates the time and the AEI has date and time, we can match AEI and TMS files to be scored in TSS Also, the AEI car information can be compared to the UMLER database to see if the cars in the train are F,P,Q, or S-type (which are the intermodal car types). If they are something else, than the AEI data and TMS output with the non-intermodal cars will be discarded. The lead locomotive in the AEI data can then be used to match it to the CAD data where it can be attached so that the proper personnel can review the results of the scoring system

    55. Future Work Use the Aerodynamic Subroutine Version 4 This version is capable of inputting double stacks with differing top and bottom load lengths Evaluate intermodal trains with trailers Improve output file Integrate AEI data to the automated system Use latest UMLER database Request an AEI reader for the Sibley site that has the capability of measuring timestamps Add capability of sorting AEI and TMS data Only scores IM trains and not other train types like manifest or grain trains

    56. Acknowledgements Special Thanks to: BNSF Paul Gabler, Hank Lees, Josh McBain, Larry Milhon, Cory Pasta, and Mark Stehly LJN and Associates Leonard Nettles and Kevin Clarke

    57. Interdisciplinary Team Members From previous presentation….

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