Systems prognostic health management april 1 2008
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Systems Engineering Program. Systems Prognostic Health Management April 1, 2008. Christopher Thompson IBM Global Business Services FCS LDMS Program.

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Systems prognostic health management april 1 2008

Systems Engineering Program

Systems Prognostic Health ManagementApril 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.


My engineering experience

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)

Lockheed Martin Missiles and Fire Control, Dallas TX

Reliability Engineer

- Army Tactical Missile System (TACMS)

SMU School of Engineering, Dallas TX

- TA for Dr. Stracener


My education

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)


My dissertation

My Dissertation

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


Sensor tradeoffs

A0

increases

Life cost

decreases

weight

increases

power

increases

MTTR

decreases

R(t)

decreases

volume

increases

AUPC

increases

cabling

increases

P(FDI)

increases

P(Prog)

increases

MTBUMA

increases

Sensor Tradeoffs

As more sensors are added to your system:

GOODBAD

TRADEOFFS


Phm optimization

PHM Optimization

Optimum

AO

Cost*

$

Operational

Availability

AO

LCC

AUPC

0 # of Sensors N

* Other metrics will include Weight/Volume, Power (K/W), Specants (Computing Power)


Phm optimization1

structural

element

x

x

x

x = mean distance between sensors

optimum

solution

0 x = mean distance between sensors X

∞ N = # of sensors 0

PHM Optimization

Probability

of

Detection

of

Crack


Systems prognostic health management april 1 2008

200% spec limit

150% spec limit

Engine Power

Engine

Internal

Temp.

125% spec limit

100% spec limit

Time

For a common LRU (such as an engine), plotting engine power against against an environmental measure (such as temperature) over time:

Severe

Damage

Moderate

Damage

Mild

Damage

No

Damage


Systems prognostic health management april 1 2008

200% specification limit

x4 (or more)

150% specification limit

x2

x1

Estimating the damage accumulated (or life consumed)

Severe

Damage

Moderate

Damage

Mild

Damage


Systems prognostic health management april 1 2008

Hypothetical engine air/oil/fuel filter performance over its life

MTBF

optimal

performance

acceptable

performance

filter performance (flow rate)

distribution of

failure times

degraded

performance

engine system

damage likely

hazardous

performance

engine system

failure likely

filter life (in miles)


Systems prognostic health management april 1 2008

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


Systems prognostic health management april 1 2008

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


Systems prognostic health management april 1 2008

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, …)


The mule program

The MULE Program

Future Combat Systems

Multifunction Utility/Logistics Equipment


Keys to the success of fcs

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


Prognostics

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.


Prognostic health management phm

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


Diagnostics

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.


Faults and failures

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.


Classes of failures

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.


The ultimate goal of prognostics

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.


What is phm

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


What is phm1

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.


Phm stakeholders

PHM Stakeholders


Phm design methodology

PHM Design Methodology


Phm design methodology1

PHM Design Methodology


Phm design methodology2

PHM Design Methodology


Phm design methodology3

PHM Design Methodology


Phm design methodology4

PHM Design Methodology


Systems prognostic health management april 1 2008

Availability Analysis

  • Availability, Achieved

    where

    MTBF = Mean Time Between Failure

    MTTR = Mean Time To Repair


Systems prognostic health management april 1 2008

Availability Analysis

  • Availability, Operational

    where

    MTBUMA = Mean Time Between Unscheduled

    Maintenance Actions

    ALDT = Administrative Logistical Down Time

    MTTR = Mean Time To Repair


Systems prognostic health management april 1 2008

Availability Analysis

  • MTBUMA = Mean Time Between Unscheduled

    Maintenance Actions

    where

    MTBM = Mean Time Between Failures

    MTBM = Mean Time Between Maintenance


Systems prognostic health management april 1 2008

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]


Systems prognostic health management april 1 2008

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!!


Systems prognostic health management april 1 2008

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!


Systems prognostic health management april 1 2008

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


Systems prognostic health management april 1 2008

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!


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