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Model-Driven Development of Integrated Support Architectures
Stan OfsthunAssociate Technical Fellow
The Boeing Company(314) 233-2300
October 13, 2004
Agenda
• Introduction• Health Management Framework
Process FrameworksModel-Based Design/Analysis FrameworksModel-Based Software FrameworksModel-Based Reasoning Frameworks
• Case StudyEngineering Models (Boeing ADVISE)Run Time Diagnostic Models (ISIS GME/TFPG)Practical Experiences and Issues
• Summary
The Need for Managing Complexity
Built In TestComponent
ModuleSubsystem
Integrated SoSHealth Management
Integrated DiagnosticsPrognostics
Vehicle Health Management
Given marginal historical diagnostic performance and increasing vehicle complexity/integration requirements, how do we produce a state of the art
Health Management (HM) capability within the target support system?
Diagnostic Development Evolution
Traditional barriers have hindered the development of cost effective and robust Health Management (HM) applications…
System Engineering Software Engineering
R&M
ID
Safety
Design
Vehicle
Orgs
Mission
Pgms
Diagnostic Development Evolution
…which can be addressed by having a more integrated approach to Health Management (HM) processes and tools.
System Engineering Software Engineering
CMMI SEI
MBHM Process Frameworks
Requirements Analysis
Requirements Validation
Functional Analysis
Functional Verification
Synthesis
Verification/Validation
Systems Integration& Control
Functional Trade Studiesand Assessments
Design Trade StudiesAnd Assessments
Requirements Baseline
Validated Baseline
Functional Architecture
Verified Functional Architecture
Physical Architecture
Verified Physical Architecture
Systems Analysis(Modeling and Simulation)
PROCESS INPUTS
Process Artifacts
Requirement & Constraint Conflicts
Decomposition & RequirementAllocation Alternatives
Design SolutionTrades & Impacts
Design Solution Requirements &Alternative Architecture Concepts
Requirements TradeStudies and Assessments
Requirement Trades & Impacts
Decomposition/AllocationTrades & Impacts
System “Best Value” Design Architecture
Derived Item Requirements for theNext Level of Decomposition
D950-10446-1 (IEEE-1220)
Requirements Analysis
Requirements Validation
Functional Analysis
Functional Verification
Synthesis
Verification/Validation
Systems Integration& Control
Functional Trade Studiesand Assessments
Design Trade StudiesAnd Assessments
Requirements Baseline
Validated Baseline
Functional Architecture
Verified Functional Architecture
Physical Architecture
Verified Physical Architecture
Systems Analysis(Modeling and Simulation)
PROCESS INPUTS
Process Artifacts
Requirement & Constraint Conflicts
Decomposition & RequirementAllocation Alternatives
Design SolutionTrades & Impacts
Design Solution Requirements &Alternative Architecture Concepts
Requirements TradeStudies and Assessments
Requirements TradeStudies and Assessments
Requirement Trades & Impacts
Decomposition/AllocationTrades & Impacts
System “Best Value” Design Architecture
Derived Item Requirements for theNext Level of Decomposition
D950-10446-1 (IEEE-1220)
SEI / CMMI
Because the HM function inherently touches every aspect of a system, decisions regarding HM requirements and design must be integral to the
overall Systems Engineering (SE) process.
MBHM Design/Analysis Frameworks
Model-Based design/analysis tools support the integration of HM and SEprocesses by providing an integrated assessment of many traditionally
disparate aspects of failure propagation.
Propulsion
Payload
Airframe
Crew Station
Fuel DeliveryFlight Control
f2
Comm
C1U42
f1
L3
UHF
Xmit
Avionics
f3
WrapTest
A/G CommTest
Weight
Reconfiguration
Redundancy
Cost
DiagnosticsReliability
Power
Weight
Reconfiguration
Redundancy
Cost
DiagnosticsReliability
Power
Weight
Reconfiguration
Redundancy
Cost
DiagnosticsReliability
Power
Top Down Decomposition
Bottom UpDesign
DesignInfluence
Specification Compliance
MaturationModel
ATECompatibility
Analysis
FaultCoverage (BIT)
AnalysisMission Reliability,
FMEA/FMECAFailurePropagation
Analysis
DesignInfluence
Specification Compliance
MaturationModel
DesignInfluence
Specification Compliance
MaturationModel
ATECompatibility
Analysis
FaultCoverage (BIT)
AnalysisMission Reliability,
FMEA/FMECAFailurePropagation
Analysis
Propulsion
Payload
Airframe
Crew Station
Fuel DeliveryFlight Control
f2
Comm
C1U42
f1
L3
UHF
Xmit
Avionics
f3
WrapTest
A/G CommTest
Weight
Reconfiguration
Redundancy
Cost
DiagnosticsReliability
Power
Weight
Reconfiguration
Redundancy
Cost
DiagnosticsReliability
Power
Weight
Reconfiguration
Redundancy
Cost
DiagnosticsReliability
Power
Top Down Decomposition
Bottom UpDesign
Top Down Decomposition
Bottom UpDesign
DesignInfluence
Specification Compliance
MaturationModel
ATECompatibility
Analysis
FaultCoverage (BIT)
AnalysisMission Reliability,
FMEA/FMECAFailurePropagation
Analysis
DesignInfluence
Specification Compliance
MaturationModel
DesignInfluence
Specification Compliance
MaturationModel
ATECompatibility
Analysis
FaultCoverage (BIT)
AnalysisMission Reliability,
FMEA/FMECAFailurePropagation
Analysis
MBHM Software Frameworks
State of the art software frameworks support the integration of model-based diagnostics and prognostics into aerospace vehicles by providing a
layered, “unbundled” architecture.
System Failure Propagation Models• ISIS Timed Failure Propagation Graphs
Localized Performance Models• Electronics Built In Test• Motor Efficiency Monitors• Valve Transition Time Monitors • Etc.
Open System Architecture for Condition Based Maintenance
Sensor (Hardware Control)
Data Acquisition (Sampling, Scaling, Smoothing)
Interface Standards
Signal Processing Algorithms(Feature Extraction)
Interface Standards
State Detector Algorithms(Tests & Monitors)
Interface Standards
Diagnostic Algorithms(Data Fusion & Failure Isolation)
Interface Standards
Prognostic Algorithms(Prediction & Trending)
Interface Standards
Sensor (Hardware Control)
Data Acquisition (Sampling, Scaling, Smoothing)
Interface Standards
Signal Processing Algorithms(Feature Extraction)
Interface Standards
State Detector Algorithms(Fault Detection Tests & Monitors)
Interface Standards
Diagnostic Algorithms(Data Fusion & Fault Isolation)
Interface Standards
Prognostic Algorithms(Prediction & Trending)
Interface Standards
MBHM Reasoning Frameworks
Off the shelf reasoning tools provide standardized run time engines for executing failure propagation models and/or performance models within
an aerospace platform.
Plant(Aircraft Subsystem)
Regulator
Supervisory ControllerFault Adaptive Control Unit
HybridObserver
FaultDetector
Hybrid Diagn.
DiscreteDiagn.
Param.Estim.
Fusion
Predicted vs. Measured output
Symbolic Failure Modes
Fault Magnitude Parameters
Updated Physical Parameters
ControllerSelector
ReconfigurationManager
TransientManager
Plant ModelsActive State
Model
Case Study – Generic Fuel System
PP
PP
LXTank
RXTank
PwrCtl
PwrCtl
PwrCtl
PwrCtl
V
V
V
P
P
P P
Manifold
LWTank
RWTank
P P
V
LFTank
RFTank
LEngine
REngine
V
V
FM
FM
PT
PT
PT
PwrCtl
PwrCtl
PwrCtl
PwrCtl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
PwrCtl
PwrCtl
PPPP
PPPP
LXTank
RXTank
PwrCtl PwrCtl
PwrCtl PwrCtl
PwrCtl PwrCtl
PwrCtl PwrCtl
VV
V
V
P
P
P PP P
Manifold
LWTank
RWTank
P PP P
VV
LFTank
RFTank
LEngineLEngine
REngineREngine
V
V
FM
FM
PT
PT
PT
PwrCtl PwrCtl
PwrCtl PwrCtl
PwrCtl PwrCtl
PwrCtl PwrCtl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
Pwr
Ctl
PwrCtl PwrCtl
PwrCtl PwrCtl
A generic fuel system (GFS) was chosen as a representative aerospace subsystem because it requires vehicle power, electronic controls, and
mechanical pumps, valves, etc.
MBHM Case Study - Notional Architecture
Robust GFS health assessment requires the assimilation of data from existing vehicle/subsystem monitors (e.g., BIT) as well as the outputs of
dedicated IVHM algorithms.
Hist
ory
FI/FA
Rule Based Reasoner
XXX Failed, YYY Degraded
“Filtered”Health
PresentationLayer
“Reported”Health
FD
“Local”Health
GrayScale
Fail
XXX Failed, YYY Degraded, ZZZ failedFI
ISIS TFPG Reasoner (HA)(Fuel Subsystem)
“System”Health
Vehicle Contingency MgmtSubsystem Built In Test
Vehicle Data Acquisition(States, Modes, Commands, etc.)
IVHM Algorithms(Pump/Valve Response, etc.)
Subsystem Data Acquisition(Fuel Subsystem)
“Raw”Data
110101000001…
MetricsMaturation
Case Study – ADVISE Model
During the HW design, an ADVISE model is built to identify the sensors/tests/monitors and fault reporting logic necessary to provide the
required levels of fault detection and isolation.
TestFunction
FailureComponent
Case Study – TFPG Development Model
Model
TestCases
During the SW design, the ADVISE model is translated into a TFPG model using ISIS GME/FACT tools and ADVISE outputs are used for engineering
desktop validation of proper diagnosis.
Case Study – TFPG Run Time Model
During the SW design, the OSA-CBM compliant TFPG run time code is automatically generated and the test cases are reused for SW desktop
validation of proper run time diagnosis.
TFPG Domain Model
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Fixed TFPG Engine
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TFPG Domain Model
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Fixed TFPG Engine
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Model
TestCases
Practical Experiences & IssuesCase Study Statistics• 244 unique ambiguity groups identified by ADVISE
• Static diagnosis using all defined tests, single fault assumption.
• 320 test cases used to verify run time diagnosis• Dynamic diagnosis using currently reported failures.
(e.g., some tests can only be run in certain modes or at certain rates)• Account for failure mode dependencies.
(e.g., a valve can’t be stuck open and stuck closed at the same time)• Account for multiple failure scenarios.
• ADVISE to TFPG Translation• Manual TFPG model required several weeks of labor and was 82% “accurate” on first try. • Translated model will require a few hours of labor and should be 100% accurate on first try. • Translated model was slightly smaller and faster.• Batch scripts automatically generate the necessary data sets for TFPG model/code testing from the ADVISE ambiguity group report
• Run Time Performance (target PPC processor / VxWorks / C++)• Real time diagnoses will be run in an event driven manner
• Event = mode change or monitor status change • Test cases averaged < 0.5 seconds of CPU time per event (max 1Hz rate anticipated) • Four large models run simultaneously with nearly linear memory & throughput demands
Summary• IVHM requires rigorous systems engineering to manage complexityand assure integrity.
• A model-based approach provides a disciplined methodology for supportingthe SE process:
• Successive refinement of diagnostic concepts and implementation.• Incremental transition from conceptual design to detailed design to validation.• Reuse of engineering data/models across design cycles.
• The Boeing Company is currently implementing a process-based, model-driven approach by employing tools from Boeing and ISIS, while evaluating other reasoners.
• The GFS case study is being used to document and benchmark the basic steps in the modeling process.
• Integration of the run time reasoners and models into Boeing’s desktop software development environment is on-going.