ISHM/ NASA Session - Work and Technology at NASA · The ITP Project can be broken into three basic...
Transcript of ISHM/ NASA Session - Work and Technology at NASA · The ITP Project can be broken into three basic...
ISHM/ NASA Session - Work and Technology at NASA
Algorithms for Intelligent Elements William Maul
Analex CorporationInstrumentation & Controls Division
NASA Glenn Research Center
IEEE Sensors for Industry Conference 2005
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Presentation Outline
• ISHM Testbed and Prototypes (ITP) Project
• Outline Development/Implementation Issues
• Highlight Intelligent Element Areas
• Layout ITP Project Relative to Intelligent Element Area
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ISHM Testbed and Prototypes Project
The ITP Project can be broken into three basic parts:
• Testbed architecture, framework and components
• Implementation of the Testbed at Stennis Space Center’s Rocket Engine Test Stand (RETS)
• International Space Station (ISS) implementation of the Testbed at Johnson Space Center
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ISHM Testbed and Prototypes Project
The products of this Project are:• A Testbed architecture/framework/components which are
portable to other Programs, such as Constellation Systems
• A prototype ISHM implemented and validated on a Rocket Engine Test Stand Subsystem, and an International Space Station Subsystem.
• Standards for interoperability and teaming of diverse software systems
• Smart Sensor technology advancements
• Software for diagnosis, prognosis and remediation of system anomalies
• Knowledge mining software advancements
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Implementation/Development Issues
From a perspective of intelligence or autonomy, the ISHM system shall provide the following functions:
• System Monitoring
• Data Qualification
• Feature/Information Extraction
• Classification/Isolation/Diagnosis
• Mission Projection/Prognosis
• Communication/Information Transfer
• System Recovery/Response
Intelligent software elements will be required to satisfy the anticipated system-level requirements of safety, reliability and sustainability
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Implementation/Development Issues
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Implementation/Development Issues
Verification And Validation of Diagnostic Algorithms Requires not only operational systems, but the ability to test systems to
failure to demonstrate the ability of the diagnostic algorithms to identify nominal conditions and failure conditions.
Integrated Testing Series
• Software Simulations� Provide an accurate, physics-based model of the system� Account for realistic system conditions� Nominal and Failure scenarios
• Hardware-in-the-loop Simulations � flight or prototype hardware executes the diagnosis software
interactively with the software simulation of the system being monitored� further define and qualify the underlying hardware and software
diagnostic technologies
• Hardware Testing� diagnostic system performance in nominal operational scenarios as well
as selected failure modes
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Intelligent Element Areas
BEAM - SHINE Technology• Provides accurate system health monitoring:
– Detects anomalies at both the system and individual signal level.– Uses sensors, discrete states, model data, and subsystem data.– Reports detected faults, health, and prognostic assessments.
• Concepts:– Uses both statistical (black box) and combined
deterministic/statistical (grey box) anomaly detection methods.– Uses inferencing techniques (SHINE – Spacecraft Health
INferencing Engine) to isolate faults
• Characteristics:– Robust predictions at very low thresholds of detection.– Degradation monitoring– Forecasting capabilities
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Sample BEAM Applications
Intelligent Element Areas
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State Diagnosis Technology: A Fast Diagnosis Engine
• Concepts:
– Solving an Integer Programming problem instead of a logical task of finding faulty components.� Finds a priori lower and upper bounds on the size of diagnosis.� Uses these bounds for a new branch-and-bound method for solving
Integer Programming.� Uses an improved algorithm for conflict generating process based on
efficient path finding algorithm on graphs.
• Characteristics:
– Avoids inefficient methods of searching a large space of possible combinations of faulty components.
– Capable of handling large systems with hundreds components.
Intelligent Element Areas
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Feature/Event Extraction - a procedure that transforms the measurement space into information with fewer dimensions.
- requires prior knowledge of the problem
Basic Assumption - the features cluster better in the feature space therefore improving classification or discrimination.
Decision Space
Feature SpaceFeature Space
Feature SpaceFeature Space
Feature Space
Measurement SpaceMeasurement
SpaceMeasurement SpaceMeasurement
SpaceMeasurement SpaceMeasurement
SpaceMeasurement SpaceMeasurement
Space
Event Detection Algorithms
Intelligent Element Areas
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• Event detection routines search for:Drifts Level Shifts SpikesNoise Iced Sensors Peaks
• Event detection routines have been used in both post-test and real-time applications.Post Test Real-Time
SSME PCCSX-33 X-34Atlas Centaur Jet Engine Tests at AEDC
Event Detection Applications
Intelligent Element Areas
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Data Mining TechnologyGoals• Apply data mining algorithms in support of fault detection,
diagnosis, and prognosis, using two testbeds (RETS and ISS)• Test existing algorithms, and improve them and/or develop new
algorithms as necessary
Application• Use anomaly detection algorithms for real-time fault detection
• Use supervised classification algorithms to diagnose faults
• Apply anomaly detection algorithms to historical data to discover previously unknown patterns and direct engineers’ attention to them.
Intelligent Element Areas
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Combining Model-based Approach With Data Mining Approach
• The data mining approach can be used to help build monitors for a model-based diagnosis system such as Livingstone.
• The monitors extract discrete features from numerical sensor data.
• A supervised learning algorithm is trained using examples of the desired features.
Intelligent Element Areas
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ITP Project
System-LevelIVHM
OtherSSHMs
PropulsionSSHM
StructuresSSHM
Subsystem-LevelIVHM
OtherSSHMs
PropulsionSSHM
StructuresSSHM
Subsystem-LevelIVHM
OtherSSHMs
PropulsionSSHM
StructuresSSHM
System-LevelIVHM
• Scalability• Create benchmarks (MBR, BEAM, etc.)• Make algorithms scale
• MBR speed-up (State Diagnosis)• Create efficient scalable architecture
• BEAM Monitoring• JSC ISS subsystem monitoring• SSC Rocket Engine Teststand (RET) monitoring
HydraulicTemperatures
LVDT Positions
Stabilator Ac t Inlet Pressure
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ITP Project
• Feature Extraction• Transfer FE algorithms developed for Atlas Centaur, X33 and
X34 applications to RETS testbed • Develop specialized FE algorithms identified through analysis of
the selected subsystem on RETS, as required
• Data Mining• Run existing unsupervised anomaly detection algorithms on
historical data from RETS and/or ISS• Work with domain experts to evaluate the results.