Intelligent Telemetry for Freight Trains using Wireless Sensor Networks

IBM RESEARCH

© 2008 IBM CorporationJohnathan M. Reason

Intelligent Telemetry for Freight Trains using Wireless Sensor Networks

What we learned and next steps

IBM RESEARCH

© 2008 IBM Corporation 2Johnathan M. Reason

Outline

Background on N.A. Freight Railroads

Why wireless sensor networks for railroads

Railroad sensor network solution

Some Results

Next Steps

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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)

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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

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Railroad track-side sensors: railcar identification and fault prevention

AEI Reader

Acoustic Sensor

Hot-box Detector

Wheel Impact Load Detector

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)

AEI tag affixed to the side of a freight car.

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Outline Progress




Some Results

Next Steps

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Problem SummaryData 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

Railcar Tracking

Bearing temperature

Brake control

Weight distribution

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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)

Iris mote from Crossbow

Mica2 from Crossbow

Design Tradeoffs: Energy Vs Performance Cost Vs Computational power and reliability

Communication

SensingComputation

Telosb from Crossbow

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Outline Progress




Some Results

Next Steps

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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).

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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

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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 gatewaysLocomotive gateways communicate to the enterprise via an uplink (Cellular, WiFi,

Satellite, proprietary RF bands, etc.)

...

a) hopper carf) locomotiveb) WSN node

c) thermocoupled) bearing adapter

e) gateway

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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

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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)

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Gateway-to/from-Railroad WSN Architecture

Node ServicesRailroad ApplicationsHot Bearing

DetectionConsist

Identification

Car-to-Train Association

Dark Car Detection

Synchronization

ReportingInformation

IEEE 802.15.4

Rx/Tx Queues

Neighbor List

Router Message ManagerReceiverSender

WakeupsARQ Multisend Delay LQIPacket Delivery Packet Measurements

RSSI PSR

...

SchedulerDispatcherNext -hop List

Proto N

...P

hyL

ink

Net

wor

kA

pps

& S

ervi

ces

nodeId Addr Position Hops

Proto 1

Cost

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

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Car D’s motes leave the network as car D is disjoined from the train and the train in no longer in range

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 reporting via a Mote 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 a car-specific mote in the network

•Motes can detect the status of their car and change their mode of operation: join => active reporting, disjoin => hibernation

WaysideABCE D

motesgateway

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Consist Identification: Basic Operation

Iterative application that has four major phases: 1. Associate cars to the train2. Measure closeness between each neighboring car (or pair of nodes)3. Report closeness measurements4. Apply the ordering algorithm

Ordering AlgorithmConsidering n cars in a train {N | i = 1,...,n}, the ordering algorithm operates in three steps:

1. Compute a car closeness metric {dij} from the node measurements2. Refine the car closeness metric using a correlation based operator3. 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.

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Outline Progress




Some Results

Next Steps

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Proof-of-Concept (PoC) Testbed Deployed on the Roof our Yorktown Facility

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

Segment of Deployment

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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

0

1

2

3

4

1 6 11 16 21 26 31 36 41 46Report Cycle

Error

including flips

ignoring flips

Flips ignored 0 1 2

Accuracy (%) 93.0 99.0 100.0

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

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Outline Progress




Some Results

Next Steps

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Next Steps: Continue the conversation about industry standardization

PoC was a good starting pointPoC 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

Network Layer

Link Layer

Physical Layer

Presentation Layer

Transport Layer

Application Layer

Burlington Northern started atesting program and selected two

vendors for full-scale testing

1/88 1500 cars were tagged8/88 reported 99.99% accuracy

AAR formed std. committeeMore RRs started testing

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

8/89 Amtech selectedData format defined

10/90 AEI std approved

8/91 AEI mandaterectified by AAR

3/92 - 12/94Mandatory rollout

1.4 millionrailcars tagged

Timeline: North America Railroads AEI Deployment

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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?

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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)

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Thanks for your attention.

Questions?

Intelligent Telemetry for Freight Trains using Wireless Sensor Networks

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