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Intelligent Telemetry for Freight Trains using Wireless Sensor Networks

Learn about the application of wireless sensor networks in the North American freight railroad industry, the results achieved, and the next steps for implementing intelligent telemetry.

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Intelligent Telemetry for Freight Trains using Wireless Sensor Networks

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  1. Intelligent Telemetry for Freight Trains using Wireless Sensor Networks What we learned and next steps

  2. Outline • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

  3. The North America Railroad Industry • 40% of U.S. freight travels by rail • Major contributors are coal, chemicals, food, and machinery • Intermodal rev. has been consistently growing • Railroads are three times as fuel-efficient as trucks • 7 Class 1 railroads represent 90% of total freight revenue (each with over $320M in annual sales) • Burlington Northern, Union Pacific, Canadian National Railway, Norfolk Southern, CSX, Kansas City Southern, Canadian Pacific Railway • 30 Regional railroads • e.g Florida East Coast Industries, … • Hundreds of locals (short line operators)

  4. Union Pacific Railroad Fast Facts (2007 data) • Largest railroad in NA • Op. Revenue $15.5B • Industrial, energy, intermodal, agricultural, chemicals, auto, etc. • Route Miles 32,300 • Employees 50,000 • Annual Payroll $3.7 billion • Purchases Made $6.9 billion • Locomotives 8,500 • Freight Cars 104,700 • Fuel efficiency 780 ton-mile/g • More than 70% of IT budget is spent on supporting the operations

  5. Hot-box Detector AEI Reader AEI tag affixed to the side of a freight car. Acoustic Sensor Wheel Impact Load Detector Railroad track-side sensors: railcar identification and fault prevention • AEI: Automatic Equipment Identification • NA railroad standard: identify railroad equipment while enroute • passive UHF RFID tags mounted on each side of rolling stock • trackside readers • Adopted since early 1990’s • As of 2000, over 95% railcars were tagged with 3000+ trackside readers • In addition to AEI readers, additional sensors are deployed along the track, including • Hot Box Detectors (bad bearings) • WILDs or Wheel Impact Load Detectors (bad wheels) • TADs or Trackside Acoustical Detectors (cracked or flat wheels)

  6. Outline Progress • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

  7. Railcar Tracking Brake control Bearing temperature Weight distribution Problem Summary • Data from trackside sensors are sparse • Does not provide timely information to prevent or mitigate all problems (sample every 45 min, on avg.) • Each technology is one-dimensional; not capable of supporting all the operational needs • Does not scale well for multiple sensor modalities • Proposed next-generation infrastructure requires • On-board telemetry for real-time visibility, using wireless sensor nodes or motes • One infrastructure supporting multiple sensor modalities • One infrastructure for communicating data, control, and events • Localized analytics • Demonstrable ROI • Large-scale deployment

  8. Capabilities of a Wireless Sensor Node (or mote)? • Computation: • Low-power microProcessor (e.g. TI MSP430) • Small amount of memory (e.g. 10KB RAM, 48KB ROM) • Sensing: • Temperature and light onboard • Embedded A/D converter • SPI bus for expansion • Communication: • low-power energy efficient radio (e.g. 802.15.4) Sensing Computation Communication Telosb from Crossbow • Design Tradeoffs: • Energy Vs Performance • Cost Vs Computational power and reliability

  9. Outline Progress • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

  10. SEAIT: Sensor-Enabled Ambient-Intelligent Telemetry for Trains • SEAIT is a WSN-based architecture and framework for building advanced railroad applications. • The framework provides a collection of protocols, services, and a data model that serve as the building blocks to enable intelligent telemetry through • timelier sensing, • localized analytics, and • robust communications. • The architecture specifies an onboard infrastructure to facilitate real-time data capture and analysis for better visibility and in-field management of the rolling stock. • At the heart of the architecture are intelligent wireless sensing nodes that form the on-board WSN and continuously monitor the health of critical components (e.g., wheel bearings).

  11. Goals, Applications, and Benefits of SEAIT • Goal • Improve operational business objectives by providing real-time visibility into the rolling stock • Some Enabled Applications • Real-time Fault Detection with Closed-loop Notification • Train Configuration Monitoring • Asset Tracking • Predicative Maintenance • Continuous Health Monitoring • Some Key Business Benefits • Schedule Optimization • Accident Prevention • Asset Utilization • Customer Satisfaction

  12. Basic Approach of SEAIT Illustrated through a Hot Bearing Detection Solution • WSN nodes • perform timelier sensing of wheel bearing temperature, • local analytics to detect overheated bearings, and • robust communications to relay “hot” bearing events to the gateway • Gateways • aggregate hot bearing events with other situational awareness data, • perform train-wide analytics, and then • provide closed-loop event notification directly to the engineer • WSN nodes communicate to gateways on locomotives or trackside gateways • Locomotive gateways communicate to the enterprise via an uplink (Cellular, WiFi, Satellite, proprietary RF bands, etc.)

  13. Key Technology Components • Gateway Software • Information model called the Railroad Business Object Model (RRBOM) • RRBOM is the meta-model for all railroad objects (trains, cars, axles, wheels, bearings, motes, sensors, etc.) • Uniform information model for enterprise applications to configure, query, and control the mote network • Performs onboard, train-specific analytics (enables closed-loop control) • Supervise railroad communication protocols and services • WSN Node Software • Uniform information and messaging model for managing and reporting sensor, configuration, and application data; provides hooks to gateway to map into RRBOM • All communication protocols and services to realize railroad applications and support application requirements • Low Latency • High Reliability • Long Life

  14. Key Technical Challenges to Realizing the Benefits of WSN for Railroads • Detection and Measurement Accuracy • Reliable detection and prediction of catastrophic faults (e.g., over heated bearing) with low false positive rate • Accurate reporting of train consist and parameters for operational optimization • Alert Latency • Predictable, low end-to-end latency from detection of a fault to alerting the engineer of such an event over many hops • End-to-end Data Reliability • End-to-end reliability over many communication hops under various conditions (weather, speed, terrains, ...) • Service Lifetime • The energy source for each mote must last at least the maintenance cycle of its associated car (> 5 years)

  15. Gateway-to/from-Railroad WSN Architecture • Applications and services send and receive messages through the interface to the communication stack • The information and reporting services realize the execution a uniform information model for managing and reporting sensor, configuration, and application data • The synchronization service realizes simple and robust management of a software RTC • Network features time-scheduled queues and cross-layer optimized routing • Link features semantic-based wakeups and delay measurements

  16. Car D’s motes leave the network as car D is disjoined from the train and the train in no longer in range E D Consist Identification: Car Disjoining from the Train • Problem: Dynamic join/disjoin of rail cars • No real-time or near real-time visibility of what cars are actually on the train • Possible Solution: Periodic car ID reportingviaaMote network • If one or more motes are uniquely associated with each car, then dynamic join/disjoin is a simply application that detects the presence/absence of acar-specific mote in the network • Motes can detect the status of their car and change their mode of operation: join => active reporting, disjoin => hibernation gateway motes Wayside A C B

  17. Consist Identification: Basic Operation • Iterative application that has four major phases: • Associate cars to the train • Measure closeness between each neighboring car (or pair of nodes) • Report closeness measurements • Apply the ordering algorithm • Ordering Algorithm Considering n cars in a train {N | i = 1,...,n}, the ordering algorithm operates in three steps: • Compute a car closeness metric {dij} from the node measurements • Refine the car closeness metric using a correlation based operator • Construct a weighted digraph, G= (N,E), where each edge has a weight of dij. The closeness metric reflects the closeness between two cars Ni & Nj. The closer the two cars are, the greater the value for dij. Consist ID is equivalent to finding the max. Hamiltonian path for graph G. We use a greedy algorithm to construct this path. The gateway in the locomotive serves as an anchor node.

  18. Outline Progress • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

  19. Proof-of-Concept (PoC) Testbed Deployed on the Roof our Yorktown Facility Segment of Deployment • Deployed 32 WSN platforms along the front metal railing of the roof to emulate a 16-car train • WSN platform: • TmoteSky node, a sensor board, batteries, an embedded antenna, an input/output connection board, and a weatherproof enclosure. The sensor board included temperature, light, and accelerometer sensors. • On average, freight railroad cars are about 60 feet long, ranging from as little as 40 feet up to 90 feet • Two WSN platforms per car (one at each end), each car 60 feet long and an inter-car node spacing of 10 feet • Sample segment of the deployment showing four cars. The entire deployment spans about one fourth of a mile. • The curvature of the front face of the building is such that, from any point along the front edge, no more than 300 feet are visible via line-of-sight. WSN Platform

  20. PoC Results for Consist Identification Setup: • Used periodic reporting with hop-based routing. Period was every 2 minutes • During slot time, each node measured closeness to its neighbors and reported these measurements to the gateway • Closeness measurements consume most of the time during each slot • Gateway runs Consist Identification algorithm • Error = # of cars that need to be moved to match the actual consist Key Observations: • Algorithm is robust within 1-car transpositions or flips • A flip is equivalent to a 2-car error • Ignoring flips, the algorithm is 100% accurate

  21. Graphical view of a consist being constructed(a screenshot of the research prototype)

  22. Outline Progress • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

  23. Application Layer Presentation Layer Transport Layer Network Layer Link Layer Physical Layer Next Steps: Continue the conversation about industry standardization Timeline: North America RailroadsAEI Deployment • PoC was a good starting point • PoC touched many areas requiring standardization • Communication (mote-mote, mote-gateway) • Message/Query • Industry semantic model/ontology • Power • SW life-cycle management • Like RFID, broad adoption of WSN will be driven by industry applications and require industry collaboration

  24. Next Steps: Some Possibilities • Continue PoC investigation by conducting field tests on real trains • Quantify the value proposition of real-time visibility with research study • Does more timely data really yield greater efficiencies in operations? • If so, how much? • What localized analytics are needed? • Explore how WSN technology can complement positive train control • As the PTC industry standard develops, what conversation should the industry be having about a path to on-board sensing and actuation?

  25. Acknowledgements • Union Pacific Railroad • Lynden Tennison, Dan Rubin • IBM • Co-authors: Han Chen and Sastry Duri (IBM Research), Riccardo Crepaldi (Intern) • Contributors: Maria Ebling and Paul Chou (IBM Research), Xianjin Zhu (Intern), Keith Dierkx (GRIC)

  26. Thanks for your attention. Questions?

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