nasa intelligent self-situational awareness project

Applications of Data Mining in Automated ISHM and Control for Complex Engineering Systems

Karl M. ReichardApplied Research Laboratory

Penn State UniversityVoice: 814-863-7681

Email: [email protected]

mailto:[email protected]

ISHEM Forum - 9 Nov, 2005 2

Data Mining

• Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data.

• Data mining tools are used to identify correlations and to predict behaviors and future trends from data.

• Businesses use data mining to search and analyze databases for hidden patterns and may find trends and predictive information that experts may miss because it lies outside their expectations or because they never thought to look for correlations there.


Data Mining

• Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for valuable minerals.

• For businesses, the economics of data mining have many analogies to traditional mineral mining – pay for access to public data and make money from mining it.

• A data warehouse is a repository where data located in disparate databases are consolidated. Data warehouses store large quantities of data by specific categories so users can easily retrieve, interpret and sort the contents.


Elements of Data Mining

• Data preparation• Databases and data warehouses• Machine learning

– Statistical models– Clustering

• Data Models• Knowledge Representation

– Association Rules– Classification Rules– Decision trees

• Data transformation


• Empirical Science – Scientist gathers data by direct observation– Scientist analyzes data

• Analytical Science – Scientist builds analytical model– Makes predictions.

• Computational Science – Simulate analytical model– Validate model and makes predictions

• Science - Informatics– Data captured by instruments

Or data generated by simulator– Processed by software– Placed in a database / files– Scientist analyzes database / files

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Data Mining as Scientific Evolution

Jim Gray, “Online Science the New Computational Science,” Microsoft Research.

http://es.rice.edu/ES/humsoc/Galileo/Images/Astro/Instruments/hevelius_telescope.gif


Leveraging Technology Trends

• Moore’s Law– Performance/Price

doubles every 18 months

– 100x per decade

• Metcalfe’s law– Utility of computer networks grows as the

number of possible connections: O(N2)

1E+3

1E+4

1E+5

1E+6

1E+7

1988 1991 1994 1997 2000

disk TB growth: 112%/y

Moore's Law: 58.7%/y

ExaByte

Disk TB Shipped per Year1998 Disk Trend (J im Porter)

http://www.disktrend.com/pdf/portrpkg.pdf.

• Computer storage capacity is actually beating Moore’s law



• Premise: Most data is (or could be online) so, the Internet is the world’s best telescope:– It has data on every part of the sky, in

every measured spectral band: optical, x-ray, radio..

– It is up when you are up. The “seeing” is always great (no working at night, no clouds no moons no..).

– It’s a smart telescope: links objects and data to literature on them.

http://www.us-vo.org/ http://www.ivoa.net/

Example: World Wide TelescopeVirtual Observatory


http://www.us-vo.org/

http://www.us-vo.org/

http://www.ivoa.net/




Why are Computer Scientists interested in Astronomy Data?

• It has no commercial value–No privacy concerns–Can freely share results with others–Great for experimenting with algorithms

• It is real and well documented–High-dimensional data (with confidence intervals)–Spatial data–Temporal data

• Many different instruments from many different places and many different times

• Federation is a goal• There is a lot of it (petabytes)

IRAS 100

ROSAT ~keV

DSS Optical

2MASS 2IRAS 25

NVSS 20cm

WENSS 92cm

GB 6cmJim Gray, “Online Science the New Computational Science,” Microsoft Research.


CollectCollectraw dataraw data

Perform Perform assessmentassessment

MakeMakediagnosisdiagnosis

ReduceReducedatadata

ImplementationImplementation

AnalyzeAnalyzedatadata

SensingSensing FeatureFeature ExtractionExtraction

FaultFaultDetectionDetection

DamageDamageEstimationEstimation

Feature Feature TrackingTracking

& Prognostics& Prognostics

DevelopmentDevelopment

SensorSensorSelectionSelection

DevelopDevelopPhysical Physical ModelsModels

TransitionalTransitionalFailure DataFailure Data

DiagnosticDiagnosticPrognosticPrognosticAlgorithmsAlgorithms

SystemsSystemsInterfacesInterfaces

Open Systems ArchitectureOpen Systems Architecture

DegraderDegraderAnalysisAnalysis

ISHM Development and Implementation

Where can we apply data mining techniques in ISHM development and implementation?


SensorSensorSelectionSelection

DevelopDevelopPhysical Physical ModelsModels

TransitionalTransitionalFailure DataFailure Data

DiagnosticDiagnosticPrognosticPrognosticAlgorithmsAlgorithms

SystemsSystemsInterfacesInterfaces

DegraderDegraderAnalysisAnalysis

Data Mining in ISHM Development

Parts usageRepair ordersMaint. logs

• Initial design and development • ISHM system verification and validation• Continuous ISHM system improvement (borrowing

techniques from manufacturing and quality control)

Sensor reliability

Operational dataISHM system dataMaint. logs

Biggest payoff may be in collection and analysis of transitional failure data across large numbers systems


Data Mining in Prognostics

RCM Analysis

SensorsDetection & Diagnostic Algorithms

Prognostics

PhysicalModels

Predicted Remaining Useful Life

Anticipated Demand

• Transitional failure data from seeded fault experiments

• Transitional data from analogous systems

• Operational data

Load history

TransformationModel

Data mining may allow access to much larger sets of transitional data for assessing fault severity and predicting remaining useful life


PlatformData Store

Platform Data Store

PlatformData Store

Platform Data Store

PlatformData Store

Platform Data Store

PlatformData Store

Platform Data Store

RegionalData Store

(CRIS)

RegionalAnalysis

Net-CentricCompliant

Service

OSA-EAITech-XML

GlobalData Store

(CRIS)

3rd PartyAnalysis


Service

OSA-EAI

or

COTS Replication

DepotAnalysis

Net Centric Attributes

Only handle information once

Tag and post before processing

Store publicly and advertise

Register metadata

Tag applications for discovery

PlatformData Store

Platform Data Store

PlatformData Store

Platform Data Store

PlatformData Store

Platform Data Store

PlatformData Store

Platform Data Store

RegionalData Store

(CRIS)

RegionalAnalysis


Service

OSA-EAITech-XML

OEMAnalysis

Database Flat file or XML Database Flat file or XML

PM IDEs

What types of data are we talking about?

Asset Register Management

Work Management

Diagnostic / Health / Rec (?)

Static Trends / Alarms

Dynamic Vibration / Sound

Oil /Fluid / Gas / Solid Tests

Binary Data / Thermography

Reliability Data

Managing System Health Information


SME

Supply

SmartMaintainer

Transfer

Maintenance

Life CycleMgmt

PM

MC

What is the status of all my platform Cs?

What is broken or about to break on this vehicle?

I need to report a problem.

Give me battery health data for all of

platform type As.

What are the new maintenance

requests? Do I have the capacity?

OperationsLogistician

Planner

Do I have assets required for the upcoming mission?What risks do I mitigate or

introduce?

When do I need to buy material for systems that are predicted to fail?

VehicleType ABlock 1












VehicleType BBlock 2








VehicleType CBlock 2

VehicleType BBlock 1

VehicleType CBlock 1

Operator

How can I automatically report when the remaining useful life of an LRU drops

below threshold?

IntegratedArchitecture

Leveraging Platform Asset Health Information


Message Manager Adapters

Platform Level e Book

ConfigurationConfigurationBIT/BITEBIT/BITE

SensorSensorMaintenanceMaintenanceOperationalOperational

Embeddede Data

Tech DataTech DataFault TreeFault Tree

RPSTLRPSTLe PMCSe PMCS

Serial Serial UID/AITUID/AIT

Configuration

Remote Diag.Remote Diag.Data SyncData Sync

Remote Diagnostics

AlertsAlertsTrendingTrending

AlgorithmsAlgorithms

HUMS

Data Download Manager Data Download Manager

Internal Message Manager

GCCS GCSSAL

Operator / Maintainer Presentation & Interface

EXternal Message Manager

USMC Autonomic Logistics Architecture

Platform

LRU LRU

LRULRU

LRU LRU

LRU


US Army Common Logistics Operating Environment


US Army Common Logistics Operating Environment

Maintain on-platform persistent health data store using MIMOSA OSA-EAI CRIS or Tech-XML

Bulk health data transfer using MIMOSA OSA-EAI Tech-XML

Manage off- platform persistent health data store using MIMOSA OSA-EAI CRIS

Emergency Resupply Request using JVMF K07.xx messages

Replicate to CONUS using COTS tools

Query persistent health data store using MIMOSA OSA-EAI Tech-XML


CACE - Coherent AnalyticalComputing Environment

CACE is an example of a data mining application which taps into databases of maintenance requests, repair schedules, and training schedules to optimize flight operations


Access to Data

• Need access to data for all information customers– Embedded ISHM systems– Operators– Maintainers– Planners– Program managers– Design Engineers

• Latency of information will determine value to different customers

• Who is responsible for maintaining the data warehouse?


Uncertain

Healthy

Faulty

Advisory

Generation

PrognosticAssessment

Diagnostic Health

Assessment

PresentationAlarm & Event

State Detection

Envelope Detector

Band-PassFilter

Half-waveor

Full-waveRectifier

Peak-HoldSmoothing

AccelerometerData

Band-PassFilteredSignal

RectifiedSignal

EnvelopeDetected

Signal

2

1 1

2

1

4

14

m

j

N

ijij

N

ii

rrm

rrNNA

Data

Manipulation

Smart Sensor

DistributedComputingTechnology

Data

Acquisition

Sensor

Open Systems Architecture for Condition Based Maintenance

• Standardized architecture for health and condition monitoring systems

• Breaks monitoring system into functional layers

• Defines inputs and outputs required for each layer

• Modules not confined to one locale

• Middleware independent


Machinery Information Management Open Standards

Alliance (MIMOSA)

Failure Histories

Geo-Spatial Tracking

Component Tracking

Model Database

OEM Model

Reliability InfoRCM

Analysis Info

Root Cause

Analysis Info

Spare Part

Analysis Info

MRO Tools

MRO Labor

MRO Materials

Work Order Tracking

Pre-Planned Work Packages

Reactive Main-

tenancePreventive

Main-tenance

Condition-Based Maint-enance

Calibration & Config.

Mgmt

Open Object

Registry Management

MIMOSA Technology TypesREG (Physical Asset Register Management) WORK (O&M Agent Work Management)DIAG (Diagnostics / Prognostics / Health Assessment)TREND (Operational Scalar Data & Alarms)DYN (Dynamic Vibration/Sound Data & Alarms)SAMPLE (Oil/Fluid/Gas/Solid Test Data & Alarms)BLOB (Binary Data/Thermography Data & Alarms)REL (RCM/FMECA/Model Reliability Information)ALGORITHM (Algorithm Management Information)AGENT (Intelligent Agent Management Information)TRACK (Physical Asset GeoSpatial Tracking Info.)FORECAST (Capability Forecasting & Projections)

Standards for Enabling System Management


Beyond System Health Monitoring

IntelligentMonitoring

FaultDetection

Safety

FaultLocation

& SeverityMaintenance/

Repair

Diagnosis& Remaining

Life

Condition-Based

Maintenance

Prognosis

HealthManagement

IntelligentControl+

Relating Subsystem

Health

IntelligentSystems

Management

VehicleMonitoring& Control

VehicleManagement

Intelligent“System”

AutomationMission

Management

Need techniques to capture decisions and actions in addition to data and permit machines to mine those high level control “lessons learned”


Other Data Mining Challenges

• Data management

• Data storage

• Indexing data for efficient computer access

• Ownership of data, data rights, and security

• Distributed processing architectures

• Data summarization, trend detection anomaly detection are key technologies

• Translation models


Conclusions

• Data mining is the search for hidden relationships and meaning in data

• Applications in ISHM include modeling, diagnostics, and prognostics

• Useful for system development and for closed-loop improvement of systems

• Biggest payoff could be in creation of virtual transitional failure data sets

• Information customers are driving for network-centric applications so databases and data warehouses will be there – we need to be sure the data is useful for ISHM

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