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.