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Systems Prognostic Health Management EMIS 7305 March 28, 2006

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Title: Systems Prognostic Health Management EMIS 7305 March 28, 2006


1
Systems Prognostic Health ManagementEMIS
7305March 28, 2006
Systems Engineering Program
  • Christopher Thompson
  • Senior Research Engineer
  • Lockheed Martin Missiles and Fire Control

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
Topic Outline
  • Introduction
  • Definitions
  • The Goal of Prognostic Health Management
  • PHM Stakeholders
  • PHM Modeling
  • Sensors
  • Prognostics Analysis Tools
  • Availability
  • Examples

3
Introduction
  • Education
  • B.S. in Electrical Engineering, SMU (1997)
  • M.S. in Mechanical Engineering, SMU (2001)
  • - Focus Fatigue and Fracture Mechanics
  • M.S. in Systems Engineering (one class remaining)
  • - Focus Reliability, Statistical Analysis
  • Ph.D. in Applied Science (anticipated 2008)
  • - Proposed Dissertation Title
  • Sensor Optimization for Systems
    Prognostic-Diagnostic Health Management in a
    Unmanned Ground Combat Vehicle

4
Introduction
  • Experience
  • Lockheed Martin Missiles and Fire Control, Dallas
    TX
  • Systems Engineer
  • - Multifunction Utility/Logistics Equipment
    (MULE)
  • Reliability Engineer
  • - Army Tactical Missile System (TACMS)
  • Lockheed Martin Aeronautics, Fort Worth TX
  • Vehicle Systems - Prognostic Health Management
  • - F-35 Joint Strike Fighter
  • SMU School of Engineering
  • - TA for Dr. Jerrell Stracener

5
Introduction
  • Future Combat Systems
  • MULE Program

6
Introduction
  • Some keys to the successful fielding of the U.S.
    Armys Future Combat Systems are
  • Reducing the Logistics footprint
  • Increasing Availability
  • Reducing total cost of ownership
  • Implementing Performance Based Logistics
  • Improvements in the ilities (RAM-T)
  • Reliability
  • Availability
  • Maintainability
  • Testability
  • Supportability

7
Some Definitions
  • Prognostics - Of or relating to prediction a
    sign of a future happening a portent.
  • Prognostics is the process of calculating and
    reporting an estimate of remaining useful life
    for a component, within sufficient time to repair
    or replace it before failure occurs.

8
Some Definitions
  • Prognostic Health Management (PHM) The
    implementation of an integrated software and
    hardware system which monitors the health, status
    and performance of a vehicle or system, tracks
    consumables (oil, batteries, ammunition, filters,
    fuel, coolant) and configuration (software
    versions, part history), and determines
    remaining life of all safety and performance
    critical components, predicting failures before
    they occur, thereby enhancing logistics and
    maintenance activities. PHM consists of
    on-board as well as off-board components.

9
Some Definitions
  • 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.

10
Some Definitions
  • Fault A condition that renders an element
    unable to perform its required function at
    desired levels of performance, or in a degraded
    mode.
  • Failure The inability of a component, system or
    sub-system to perform its intended function as
    designed. Failure may be the result of one or
    more faults.
  • Fault Tolerance The design of a system so that
    it will continue to operate in a degraded or
    reduced level rather than failing completely,
    when some part of the system fails.

11
Some Definitions
  • Failure Cascade The result when a failure
    occurs in a system of interconnected components,
    and the successful operation of a component
    depends on the successful operation of a
    preceding component. Conversely, a failure can
    trigger the failure of successive parts, and
    potentially amplify the result or impact.
    Redundancy and fault tolerant design can reduce
    the criticality or impact of the cascade, but not
    necessarily prevent a failure.

12
Some Definitions
  • 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.

13
Some Definitions
  • One-To-One Redundancy - Each active component in
    a system has a redundant backup on standby. The
    active component is monitored at all times, and
    the standby component will activate if the
    primary component fails. Since the probability of
    both components failing at the same time is low,
    One-To-One Redundancy provides the highest level
    of availability, but at a considerable
    disadvantage of requiring double the size,
    weight, power and cost, while reducing
    reliability (more components which can fail).

14
Some Definitions
  • N X Redundancy N components are required to
    perform a function, but the system is configured
    with N X components. When any of the N
    components fail, one of the X modules activates.
    The advantage lies in reduced size, weight, power
    and cost of the system, in the case where X is
    smaller than N. In case of multiple component
    failures, this scheme provides lesser system
    availability. 

15
Some Definitions
  • Load Sharing Multiple components share a
    combined load. A higher level component manages
    load distribution, and monitors the health and
    status of the components. If one of the load
    sharing components fails, the load is
    re-distributed among the others, allowing for
    graceful performance degradation. In this scheme,
    there is almost no extra cost. The main
    disadvantage is that multiple failures, system
    performance may degrade below an acceptable
    level.

16
The Ultimate Goal of Prognostics
  • The purpose of Prognostic Health Management is to
    repair systems before they fail, while maximizing
    useful life consumption, and to have the
    necessary parts, tools and maintainers waiting
    nearby to resolve the correct problem as quickly
    and efficiently as possible.

17
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 PHM Model Integration Software Interfaces Fault/Failure Simulation Continuous BIT/PHM Test Planning Fault/Failure Criticality Fault/Failure Propagation Fault/Failure Simulation Platform Integration Crack Growth Sensing Stress/Strain Sensing Corrosion Sensing Vibration Sensing Consumables Monitoring Acoustic Sensing Thermal Sensing Sensor Implementation Sensor Integration Data Management Data Architecture Reliability/ Failure Modes Maintainability Testability Logistics Sustainment Training Safety
18
Systems Engineerings Role in PHM
  • Requirements Development
  • System Integration
  • System Architecture
  • Interface Management
  • Risk Assessment
  • Performance Measures TPMs KPPs
  • System Modeling Knowledge Integration
  • Functional Decomposition

19
PHM Requirements
  • The PHM system shall isolate X percent of all
    detected failures to a single component, within Y
    percent confidence interval.
  • The PHM system shall predict X percent of
    expected failures for the next Y hours of
    operation.
  • The PHM system shall predict all failures that
    can result in a Safety Critical Failure.
  • The PHM system shall incorporate sensors to
    assess platform health, status and performance.
  • The PHM system shall incorporate sensors to
    monitor platform consumables.
  • The PHM system shall record and store all sensor
    data in onboard memory.

20
The Ilities Product Support
  • Reliability
  • FMECA Failure Modes Effects Criticality
  • FRACAS Failure Reporting Corrective Actions
  • Measures MTBF, MTBSA, MTBEFF, MTBUMA
  • Maintainability
  • - Maintenance Ratio
  • - Preventive Maintenance Checks
  • - Condition Based Maintenance
  • - Design for Maintainability
  • Availability
  • - AO, AI, AA

21
The Ilities Product Support
  • Testability
  • - Verification and Validation
  • - Fault Insertion
  • - Simulation
  • Supportability
  • Consumables Monitoring
  • Supply Planning and Prediction
  • System Safety
  • - Single Multiple Fault Tolerant Design
  • Safety Critical Failures
  • Human/Machine Interaction

22
PHM Modeling
  • eXpress Modeling Tool
  • Model Based Reasoning
  • Case Based Reasoning
  • Knowledge Bases
  • Prognostics Analysis Tools

23
eXpress Modeling Tool
DATA MINING
DIAGNOSTIC, PROGNOSTIC PHM DESIGN
SENSOR FUSION
REQUIREMENTS ANALYSIS
Mission Assurance, Availability Success
Run-Time Prognostic Health Management
Performance Based Logistics
CONOPS, SPECS LOGISTICS
RISK ASSESSMENT
LIFE CYCLE TRADE SPACE
FRACAS FMECA DEVELOPMENT
BUSINESS CASES
24
Impact Technologies
Prognostics developed at Impact Technologies
Gas Turbine Engines and Auxiliary Systems
Avionics PHM and Reasoning Aircraft Actuators
(EMA, EHA) Switching Mode Power Supplies, GPS
Receivers and Power Electronics Generators and
Electric Drive Systems Bearings, Gears,
Shafts, Drive Trains, and Clutches Hydraulic,
Lube Oil and Fuel Systems Structures and
Components Diesel Engines
25
Impact Technologies
  • Prognostics modules have been developed and
    successfully tested on the following systems
  • Pratt Whitney F-100 engine on F-15 and F-22
  • Engine, generator, lubrication system and
    gearbox on Honeywell F124
  • Oil wetted components on GE F110-129, GE F404,
    Rolls Royce F405
  • CH-47 T-55 engine and drive-train and
  • CH-60 intermediate gearbox
  • Blackhawk Carrier Plate Prognosis System
  • JSF Clutch Wear and Lift-Fan Prognosis System
  • Fuel system and Power generation system on
    DDG-class Navy Ships

26
Impact Technologies
  • A number of different techniques have been used
    in the development of these prognostics
  • Analytical and stochastic physics of failure
    models
  • Advanced signal processing
  • Feature extraction methods
  • Health state estimation and prediction
    algorithms
  • Statistical reliability
  • Bayesian updating methods
  • Component damage accumulation models
  • Probabilistic remaining useful life estimation
  • Data driven modeling techniques

27
Model Based Reasoning
Model Based Reasoning (MBR) is a qualitative
scheme where a model of the system is combined
with an inference engine that is able to
accomplish fault detection and fault isolation.
The qualitative model is used to describe system
elements and components, interconnections, and
input/output behavior of the system being
diagnosed, or Knowledge Base and to establish
an envelope of correct behavior. To accomplish
diagnosis, the model determines what differences
exist between the actual behavior of the system
and the model of the system. The inference
engine, using this comparison information,
accomplishes the fault isolation task.
28
Case Based Reasoning
Case Based Reasoning (CBR) is the process of
solving problems based on past understanding of
similar problems. The vast majority of this type
of information is contained within the
maintainers and operators the experience and
knowledge of the person using the system in
question. CBR compares a case, forms an implicit
generalization of the case, and then identifies
commonalities between a retrieved case and the
target problem.
29
Knowledge Bases
inorganic sensor data
off-board prognostic trend analysis
organic sensor data
KNOWLEDGE BASE FMECA data fault/failure
propagation system level interactions functional
interdependencies physical interdependencies desig
n knowledge prognostic trend analysis CAD
models circuit layouts
Database Management Data Mining Feature Extract
ion
subsystem/ LRU internal sensor data
sensor fusion and signal conditioning
BIT data
consumables monitors
maintainer inputs
30
Prognostic Analysis Tools
  • Learning Systems Artificial Intelligence
  • Genetic Algorithms
  • Expert Systems
  • Fuzzy Logic
  • Neural Networks
  • Database Techniques
  • Feature Extraction
  • Data Mining
  • Mathematical Techniques
  • Kalman Filtering
  • Dempster-Schafer Method
  • Wavelets
  • Statistical Analysis
  • Chaos Math?

31
Prognostic Analysis Tools
  • Traditional Academic Solutions to PHM
  • Run-to-Failure analysis of large, expensive
    systems, such as ship or rail engines
  • Analysis involves impractical, complex math
    models that require years of training to
    understand and interpret
  • Very expensive
  • Time consuming process
  • Rarely offer concrete design guidelines or
    solutions

32
Prognostic Analysis Tools
  • Why Engineers in Industry Need More
  • We have bottom lines and schedules to meet!
  • We have customer requirements to satisfy!
  • Systems Engineers work with designers who dont
    like impractical, complex math models that
    require years of training to understand and
    interpret!
  • We have program managers who dont like very
    expensive, time consuming solutions!
  • We like concrete design guidelines and solutions!

33
Sensor Technology
  • BIT/BITE
  • Sensor Fusion and Virtual Sensors
  • Sensor Conditioning and Filtering
  • Smart Sensors

34
Availability Analysis
  • Availability, Achieved
  • where
  • MTBF Mean Time Between Failure
  • MTTR Mean Time To Repair

35
Availability Analysis
  • Availability, Operational
  • where
  • MTBUMA Mean Time Between Unscheduled
  • Maintenance Actions
  • ALDT Administrative Logistical Down Time
  • MTTR Mean Time To Repair

36
Availability Analysis
  • MTBUMA Mean Time Between Unscheduled
  • Maintenance Actions
  • where
  • MTBM Mean Time Between Failures
  • MTBM Mean Time Between Maintenance

37
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

38
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!!

39
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!

40
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

41
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
  • Dont repair/replace things which arent broken!

42
Sensor Example
Engine Health/Performance Monitoring Place an
acoustic sensor on the engine housing. Establish
nominal operating parameters. Develop library
relating fault precedents to failures odd
sounds which warn of impending failure. Monitor
for out of nominal acoustic signature.
43
PHM Example
Consider a toaster Not just any toaster, but the
toaster on the first mission to Mars. NASA could
only afford to send one, and it must work, every
time, or else the astronauts wont have toast.
The toaster must also not endanger the mission
by causing a safety hazard or waste bread.
Mission Critical Function - make
toast Safety Critical Functions - dont injure
the astronauts - dont damage the spaceship -
dont burn the toast!
44
PHM Example
  • Identify the elements of a toaster.
  • What are the failure modes?
  • What should we monitor for safety hazards?
  • What elements should we monitor for diagnostics?
  • What data should we collect for prognostics?
  • How would we optimize the sensor coverage and
    data collection?

45
Issues Related to PHM
  • Continually monitoring sensors and storing all
    that data for analysis will quickly consume
    available bandwidth and storage space.
  • Capturing profound knowledge of a complex
    engineered system and its myriad failure modes is
    very difficult, and involves integrating
    knowledge which crosses discipline boundaries
    SE, EE, ME, RAM-T, Safety, Software, Math,
    Statistics, Physics
  • Prognostic analysis of data is a very difficult
    problem, with no easy or universal solution.
  • PHM is a relatively new field.

46
Final Remarks
  • Do I have any practical PHM suggestions?
  • - Aim for the low hanging fruit
  • Use the sensors you already have in creative
    ways.
  • Only add sensors when you must.
  • You cant monitor everything, so dont try.
  • - Dont reinvent the wheel
  • Build on others work and experience.
  • Find good tools to design your system.

47
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