Transcript Locomotives, Tanks, and Predictive Telemaintenance

by
John A. Fowler
&
Lawrence B. Jordan
SD70MAC, 4000 HP, 1998, FIRE Gen 1
SD90MAC, 6000 HP, 2000
SD70MAC, 4000 HP, 1998, FIRE Gen 1
MetroLink, F59PHI, 3000 HP
The Need for Failure Prediction
• The Armed Services have moved from preventive
maintenance initiated in the 1960’s to conditionbased maintenance (CBM) started in the 1990’s. The
Army’s ADIP program includes failure prediction and
telemaintenance.
• Electronics have been on-board locomotives since
the mid-1980’s. After years of scheduled
maintenance practice, a movement to CBM began in
the early 1990’s with ICE followed by FIRE Gen1.
FIRE Gen2 began IntelliTrain and predictive CBM
conceptualization.
• The IntelliTrain predictive CBM is unique in that the
earliest warning is provided of an impending failure.
• The IntelliTrain approach validates the PM-TMDE
maintenance initiatives and inquiries are welcomed.
. . . by the Earliest Warning!
Maintenance Goal:
Reduce Cost + Increase Availability
Maintenance Approaches:
• Reactive – Fix when broken
• Preventive – Scheduled Maintenance
• Condition-Based – Fix based on current conditions
• Predictive – Forecast Failure based on CBM
& Experience
• IntelliTrain Predictive
Military CBM Initiatives
• PM-TMDE: EMS/IETM, ADIP
• LIA: EDAPS
Gives Earliest Warning!
Evolution of Predictive Maintenance
Reactive
It Broke
Come Fix It
High cost
Preventative
I know it
will break
someday
Recommend when I
should perform
maintenance
Predictive
It’s running and
it’s making money
Help me see what
needs fixing so I can
plan when I will fix it
Low cost
Remote Predictive Telemaintenance
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Data available, but how to gather?
Sending personnel to monitor is costly
Storing data delays prediction
Remote access is needed with security
CBM concepts in place of Preventive
To be predictive, need advanced warning
IntelliTrain Critical Success Factors (CSF):
1. Embedded Sensors
2. Secure Multi-mode Communications
3. Model Based Prediction
4. Locomotive Management Center
5. Maintenance Action Management
F 103
Predictive Telemaintenance Enterprise
Enterprise Features & Benefits:
•High Reliability Maintenance
•Reduced Maintenance Cost
•Limit Mean Miles Between Failure (MMBF) degradation with age
•Eliminate time based overhaul
•Provide 7+ days advanced warning of failure
•Reduce No Defect Found (NDF)
•Location of any locomotive at any time
•Locomotive Control
•Homeland Security Initiatives
Unique feature is the creation of a personalized
empirical model – each locomotive has a custom
software profile.
Predictive Telemaintenance Enterprise
Engineering
Predictive Overhauls
Improved Equipment Design
System Monitoring
Monitoring Center(s)
Operations Data
Health Monitoring
Alerting (Health, Fuel)
Real-Time Interactive Diagnostics
Mobile Node
Locomotive
Mobile Node
Line of Road
Track Clearance
Incident Reporting
Asset Tracking
Time Cards
Rail Condition Monitoring
Biometric Operator Auth.
Predictive Health Monitoring
Dynamic Brake Health
APU
CBTM
Work Orders
Mobile Node
Mechanical & Maintainers
Transportation
Interchange Data
Asset Tracking
Yard Mangement
Track Quality
Interactive Troubleshooting Support
Problem Notification/Verification
Recommended Repairs
Parts Availability
Fleet Trends
Operations
Dispatch Readiness
Crew Performance
Crew Planning
Rules Infraction
Time Cards
Receipt of Shipment
Third Party Systems
RR Suppliers
Fuel Reconciliation
Crew Taxi
RR Customers
Shipment Status/Location
Condition of Shipment (spoilage)
Logistics
Warranty
Parts Ordering
Work Orders
Fuel Management
InterCarrier Data Exchange
Remote Monitoring
– Engine System
–
–
–
–
Fuel Monitor
Oil/Engine Temperature
Oil/Water Pressure
Fuel Injector Performance
– Traction System
– Voltage and Current
– Ground Current
– Vehicle Speed
– Auxiliary Systems
– Electronic Air Brake System
– Distributed Power
– Head of Train Device
Failure Prediction Using CBM
• CBM enabled via monitoring of key feedback
signals
– Detect subtle system changes prior to logging
faults
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Water leaks
Short brush detection
Fuel oil dilution of lubricating oil
Engine performance problems
Fuel and lube oil filter plugging
– Learning continues
• Prediction of electrical failures
– Ground current monitoring under test
– Predictive Model
But where, on the battlefield, are they?
• The location of each and every locomotive is
identified by our embedded GPS.
• IntelliTrain’s secure IP-Centricity provides a
map with the locomotive location at any
internet site.
• With thousands of miles of rail, GPS location
via the internet provides instant location.
• Predicted failures can be planned for repair.
• The same would be true to identify military
vehicle location in training or on the
battlefield.
GPS Provides Location of Locomotives
Over the Internet
Locomotive Cab Interior View
Computer/Display
Computer
Front View
Rear View
Total Solution in a Single COTS Unit
Computer: A COTS Solution
Significant Features & Benefits
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System Integration (Hardware & Software)
Host for all Software (Application, drivers & OS)
Embedded BIT
Wireless control (Cellular, WLAN & GPS)
Sensor interface
Environmental protection (thermal, vibration, dust,
EMI)
• Thermal management (-40o C to +85o C, internal)
• Fault tolerant
• Future growth including homeland security
Computer: Remote Access Data Acquisition
GPS
CELLULAR
Packet Data
CELLULAR
Circuit Switched
Data
DIRECT NETWORK
Via WLAN
Display,
Controls
and
Power
(COTS
and
Custom
Design)
Mobile Wireless
Communications + GPS
(COTS & custom
design)
Memory
System Computer
And
Interface
Electronics
(COTS Components)
FIRE
Computer
LOCOMOTIVE
Critical
Systems and
Sensors
Legacy
System &
Sensors
M1A1
The IntelliTrain Predictive CBM can
benefit a wide range of military vehicles.
Stryker
Computer Specifications
• COMPUTER CHARACTERISTICS
– Processor type: Intel Compatible Geode GX1-300MHz
• 256 MB RAM
• 128 MB Flash disk, expandable to 1 GB
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Dual Ethernet & USB ports
Sound Blaster Compatible Audio Interface 2 Watts/Channel
Dual PC-104 Expansion slots
External Connections for Keyboard, Mouse, USB, Ethernet,
VGA video and other PC functions
– Optional Dual Channel SAE J1939 (CAN) interface
– 8 External Serial Ports
• 4 Isolated Synchronous/Asynchronous RS422 ports (SAE J1708)
• 2 Isolated and 2 non-isolated RS232 ports
Computer Specifications
• WIRELESS SUPPORT CHARACTERISTICS
– GSM/GPRS Cellular Modem with:
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Tri-band Operation (900/1800/1900 MHz)
Meets ITU Specifications
Supports GPRS class B with 56K data rates
Includes GPS
– CDMA Cellular modem with:
• IS 95 A CS (CDMA 1995)
• 1xRTT (HSPD) IS-2000
• Supports Dual Band Operation (800/1900 MHz)
– Quad Serial Interface to Single Board Computer
– WLAN (IEEE 802.11b) support via Ethernet
Computer Specifications
• ENVIRIONMENTAL CHARACTERISTICS
– Temperature:
• Operating: -40C to +65C
– Modems: -30C to +60C
– Non-operating: -50C to +71C
– Humidity:
• MIL-STD-810E, 95% non-condensing
– Altitude:
• Operating: 11,000 feet
– Shock:
• Operating: MIL-STD-810E, 30g
– Vibration:
• Random: MIL-STD-810E
– EMI/EMC:
• FCC Class B
Computer: Modem I/F Board
Network-Centric Multi-path Communication
Top Level Architecture
Mobile
Message
based
Application
Corporate
Message
based
Application
Queue
Manager
Queue
Manager
Private Packet Switched Networks
Protocol NON-IP Private Networks
IP
IP Routing,
VPN, RAS
Firewall,
Proxy
Bridge
Protocol
Bridge
IP Centric Private Networks
Commercial Circuit Switched Networks
IP Routing,
VPN, RAS
Firewall,
Proxy
IP
AMPS ( 2.4 Kb/s)
CDMA (9.6 Kb/s)
Mobile
Web
Browser
Client/
Server
Application
GSM (9.6 Kb/s)
Commercial Packet Switched Networks
CDPD (19.2 Kb/s)
GPRS (56 Kb/s)
1xRTT (144 Kb/s)
802.11WLAN (3 Mb/s)
Connection Oriented Applications
Web
Server
Application
Server
Security
• All wireless links are secured by an end-to-end
Virtual Private Network (VPN).
• The VPN is used on top of any additional over the
air security measures such as 802.11 WEP
encryption.
• The VPN currently uses 128 bit encryption and
strong authentication.
• The VPN implementation is extensible and is
capable of using 3DES, AES or other encryption
standards.
• No public IP addresses are used on board the
locomotive.
• Both ends of the connections are also fire-walled.
Model Based Prediction
How IntelliTrain’s Predictive Maintenance
Component Provides Early Warning
Traditional Condition Monitoring
Upper Threshold
Sensor Signal
Lower Threshold
Earliest!
IntelliTrain - Early Detection
Sensor A
Sensor B
Sensor C
Sensor D
Sensor E
Sensor F
• In realreal-time, the predictive
maintenance component generates a
dynamic band around each signal,
using an empirical model to generate
an estimate for each sensor based on
the value of all other sensors
• Signal excursions outside of this
dynamic band provide the earliest
possible warning of trouble – well
within the traditional thresholds
How Model-Based Prediction Works
Personalized Empirical
Model
Removes Effects of
Normal Operation
Boost
Real-time
Sensor Data
Boost
Eng.
Rack
TBU
RPM
Eng.
Rack
Statistical
Deviation
Detection
TBU
RPM
Real-Time
Alerting of Impending
Problems
Diagnosis
Advanced
Warning
Notice
Diagnostic
Rules
Engine
Associates
Deviations with
Known Problems
Determines
If Operation
Is Abnormal
Locomotive Management Center
Condition Based Maintenance Process
IntelliTrain
Wireless Link
Internet
EMD Locomotive
Management Center
IntelliTrain
Server
Offboard
CBM Tools
& Databases
Onboard
prognostic
devices
Work Order
Railroad’s
Maintenance
Control System &
Transportation
Control System
Predictive Maintenance
and Diagnostic Tools
Analyst
Work Orders:
E-mail, Pager or
Phone Advisory
and/or B2B Link
Predictive CBM Results
• No potential mission failures were
detectable prior to IntelliTrain
• 80% of potential mission failures
were detectable via IntelliTrain
prior to functional failure.
Future Plans
• L-3/IEC –
– Develop military
applications:
• System Integration
• Communication
interface
• Application software
• Equipment Locator
• GM EMD
– Expanded Suite of
Remote Monitoring
Solutions
L3/IEC Dual Plane Display
1.
2.
3.
Predicting Failures is as much a desire for military vehicles as for
locomotives
Both military and industry have attempted to find improvements in
maintenance and diagnostics.
IntelliTrain has developed a model-based predictive telemaintenance
system with the following Critical Success Factors:
a.
Embedded Sensors
b. Multi-mode Communications
c.
Model-based predictive telemaintenance
d. Locomotive Management Center
e.
Maintenance Action Management
4.
Results to date: 80% of potential mission failures are detectable with the
earliest warning.
5.
L3/IEC is prepared to help any military program integrate this new form
of Predictive Telemaintenance into their system.