1. Systems Prognostic Health Management April 1, 2008
Systems Engineering Program
Systems Prognostic Health Management April 1, 2008
Christopher Thompson
IBM Global Business Services
FCS LDMS Program
Disclaimer: This briefing is unclassified and contains no proprietary information. Any views expressed by the author are his, and in no way represent those of Lockheed Martin Corporation.

2. My Engineering Experience
IBM Global Business Services, Dallas TX
Requirements Lead/Prognostics SME
FCS Logistics Data Management Service (LDMS)
Lockheed Martin Missiles and Fire Control, Dallas TX
Senior Systems Engineer
- Multifunction Utility/Logistics Equipment (MULE)
Lockheed Martin Aeronautics, Fort Worth TX
Vehicle Systems - Prognostic Health Management
- F-35 Joint Strike Fighter (Lightning II)
Reliability Engineer
- Army Tactical Missile System (TACMS)
SMU School of Engineering, Dallas TX
- TA for Dr. Stracener

3. My Education B.S. in Electrical Engineering, SMU (1997)
M.S. in Mechanical Engineering, SMU (2001)
- Major: Fatigue/Fracture Mechanics
M.S. in Systems Engineering (2002)
- Major: Reliability, Statistical Analysis
Ph.D. in Applied Science (anticipated ~ 2009)
- Major: Systems Engineering (PHM)

4. My Dissertation
Fleet Based Analysis of Mission Equipment Sensor Configuration and Coverage Optimization for Systems Prognostic Health Management

5. Sensor Tradeoffs As more sensors are added to your system: GOOD BAD A0
increases
Life cost
decreases
weight
increases
power
increases
P(FDI)
increases
P(Prog)
increases
volume
increases
cabling
increases
MTBUMA
increases
MTTR
decreases
R(t)
decreases
AUPC
increases
TRADEOFFS

6. PHM Optimization Optimum AO Cost* Operational $ Availability AO LCC
AUPC
# of Sensors N
* Other metrics will include Weight/Volume, Power (K/W), Specants (Computing Power)

7. PHM Optimization structural element x
x = mean distance between sensors X
x = mean distance between sensors x
structural
element
∞ N = # of sensors
optimum
solution
Probability of
Detection
Crack

8. For a common LRU (such as an engine), plotting engine power against against an environmental measure (such as temperature) over time:
Engine
Internal
Temp.
Time
Engine Power
200% spec limit
150% spec limit
125% spec limit
100% spec limit
Severe
Damage
Moderate
Damage
Mild
Damage No
Damage

9. Estimating the damage accumulated (or life consumed)x1 x2
x4 (or more)
150% specification limit
200% specification limit
Severe
Damage
Moderate
Damage
Mild
Damage

10. Hypothetical engine air/oil/fuel filter performance over its life
MTBF
optimal
performance
acceptable
performance
filter performance (flow rate)
distribution of
failure times
engine system
damage likely
performance
degraded
engine system
failure likely
hazardous
performance
filter life (in miles)

11. statistically significant difference
Common LRU used on multiple vehicle types with platform specific (hidden) failure modes
Why is the Failure
Rate for the LRU
in Platform 4 higher?
What is different
about Platform 4?
statistically significant difference
Failure
Rate
Platform 1
Platform 2
Platform 3
Platform 4

12. Standard oil filter used in engines across FCS vehicles,
replaced at a scheduled time/miles
Increased engine life
consumption
Scheduled
Replacement
Time
Correct
action
Wearout – Life Consumption
wasted filter life
Vehicle 1
Vehicle 2
Vehicle 3
Actual Condition of the oil filter

13. Time (or miles, or load cycles, or on/off cycles, …)
Standard structural element across several vehicles (under cyclic loading)
fleet
based
estimate
Repair needed
before estimate
Damage
Accumulation
LRU life
histories
Time (or miles, or load cycles, or on/off cycles, …)

14. Multifunction Utility/Logistics Equipment
The MULE Program
Future Combat Systems
Multifunction Utility/Logistics Equipment

15. Keys to the Success of FCS
Reducing Logistics footprint
Increasing Availability
Reducing Total Cost of Ownership
Implementing Performance Based Logistics
Improvements in the ‘ilities’ (RAM-T)
Reliability
Availability
Maintainability
Testability
Supportability

16. Prognostics Of or relating to prediction; a sign of a future
happening; a portent.
The process of calculating an estimate of remaining
useful life for a component, within sufficient time to
repair or replace it before failure occurs.

17. Prognostic Health Management (PHM)
PHM is the integrated system of sensors which:
Monitors system health, status and performance
Tracks system consumables
oil, batteries, filters, ammunition, fuel…
Tracks system configuration
software versions, component life history…
Isolates faults/failures to their root causes
Calculates remaining life of components

18. Diagnostics The identification of a fault or failure condition of an
element, component, sub-system or system,
combined with the deduction of the lowest
measurable cause of that condition through
confirmation, localization, and isolation.
Confirmation is the process of validation that a failure/fault has occurred, the filtering of false alarms, and assessment of intermittent behavior.
Localization is the process of restricting a failure to a subset of possible causes.
Isolation is the process of identifying a specific cause of failure, down to the smallest possible ambiguity group.

19. Faults and Failures
Fault: A condition that reduces an element’s ability
to perform its required function at desired levels, or
degrades performance.
Failure: The inability of a component, sub-system
or system to perform its intended function. Failure
may be the result of one or more faults.
Failure Cascade: The result when a failure occurs
in a system where the successful operation of a
component depends on a preceding component,
which can a failure can trigger the failure of
successive parts, and amplify the result or impact.

20. Classes of Failures Design Failures: These take place due to inherent
errors or flaws in the system design.
Infant Mortality Failures: These cause newly
manufactured systems to fail, and can generally be
attributed to errors in the manufacturing process,
or poor material quality control.
Random Failures: These can occur at any time
during the entire life of a system. Electrical systems
are more likely to fail in this manner.
Wear-Out Failures: As a system ages, degradation
will cause systems to fail. Mechanical systems are
more likely to fail in this manner.

21. The Ultimate Goal of Prognostics
The aim of Prognostics is to maximize system
availability and life consumption while minimizing
Logistical Downtime and Mean Time To Repair, by
predicting failures before they occur. This is a
notional diagram indicative of a wear out failure.

22. What is PHM? Prognostic Health Management (PHM) is the integrated
hardware and software system which:
Monitors system health, status and performance
Tracks system consumables
oil, batteries, filters, ammunition, fuel…
Tracks system configuration
software versions, component life history…
Diagnoses/Isolates faults/failures to their root causes
Calculates remaining life of components
Predicts failures before they occur
Continually updates predictive models with failure data

23. What is PHM? Prognostic Health Management is a methodology for
establishing system status and health, and projecting
remaining life and future operational condition, by
comparing sensor-based operational parameters to
threshold values within knowledge base models.
These PHM models utilize predictive diagnostics, fault
isolation and corroboration algorithms, and
knowledge of the operational history of the system,
allowing users to make appropriate decisions about
maintenance actions based on system health,
logistics and supportability concerns and operational
demands, to optimize such characteristics as
availability or operational cost.

24. PHM Stakeholders SYSTEMS ENGINEERING SOFTWARE & SIMULATION
TEST ENGINEERING
MECHANICAL ENGINEERING
ELECTRICAL ENGINEERING
TRAINING & PROD. SUPP.
PHM Model
Design
Interface
Management
Requirements
Development
Sensor
Optimization
CAIV/WAIV
Analysis
Prognostic
Trending
System
Architecture
Integration
Software
Interfaces
Fault/Failure
Simulation
Continuous
BIT/PHM
Test
Planning
Criticality
Propagation
Platform
Crack Growth
Sensing
Stress/Strain
Corrosion
Vibration
Consumables
Monitoring
Acoustic
Thermal
Implementation
Data Management
Data Architecture
Reliability/
Failure Modes
Maintainability
& Testability
Logistics &
Sustainment
Training
Safety

25. PHM Design Methodology

26. PHM Design Methodology

27. PHM Design Methodology

28. PHM Design Methodology

29. PHM Design Methodology

30. Availability Analysis
Availability, Achieved
where
MTBF = Mean Time Between Failure
MTTR = Mean Time To Repair

31. Availability Analysis
Availability, Operational
where
MTBUMA = Mean Time Between Unscheduled
Maintenance Actions
ALDT = Administrative Logistical Down Time
MTTR = Mean Time To Repair

32. Availability Analysis
MTBUMA = Mean Time Between Unscheduled
Maintenance Actions
where
MTBM = Mean Time Between Failures
MTBM = Mean Time Between Maintenance

33. Availability Analysis
How can we improve AO?
- By decreasing Administrative & Logistical Down Time (ALDT)
- By increasing Mean Time Between Failures (MTBF)
- By decreasing Mean Time To Repair (MTTR)
- By increasing Mean Time Between Unscheduled Maintenance Actions (MTBUMA) – [by decreasing MTBR induced and MTBR no defect]

34. Availability Analysis
How can we decrease ALDT?
- By improving Logistics
Improve scheduling of inspections
Improve commonality of parts
Decrease time to get replacements
- By improving Prognostics
Replace parts before they fail, not after
Maximize use of component life
Improve off-board prognostics trending
More sensors!!

35. Availability Analysis
How can we increase MTBF?
- By improving Reliability
Select more rugged components
Improve life screening and testing
Improve thermal management
- By improving Quality
Better parts screening
Better manufacturing processes
- By adding Redundancy
At the cost of Size, Weight and Power!

36. Availability Analysis
How can we decrease MTTR?
- By improving Maintainability
Improve quality and efficacy training
Simplify fault isolation
Decrease number of tools and special equipment
Decrease access time (panels, connectors…)
Improve Preventative Maintenance
- By improving Diagnostics
Improve BIT and BITE
Decrease ambiguity group size
Improve maintenance manuals and training

37. Availability Analysis
How can we increase MTBM (induced/no defect)?
- By improving Safety
Limit the potential for accidental damage
- By improving Prognostics
Improve PHM models to monitor induced damage
- By improving Diagnostics
Lower the false alarm rate
Don’t repair/replace things which aren’t broken!