Los Alamos National Laboratory Building System Models through System Ethnography First Annual...

Los Alamos National Laboratory

Building System Models through System Ethnography

First Annual Conference on Quantitative Methods & Statistical Applications in Defense

Andrew Koehler, PhDAlyson Wilson, PhDChristine Anderson-Cook, PhDStatistical Sciences, D-1Los Alamos National Laboratory2/3/2006

LA-UR-06-0951

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Munitions Stockpile Reliability Assessment - Summary

Goal/Objective: The goal of this project is to develop a dependable and cost-effective suite of statistical methodologies and tools to assess the reliability of weapon stockpiles.

Approach/Tasks Methodological development

information integration uncertainty quantification with heterogeneous data

Applications collaboration Tool development

software for rapid development of systems and statistical models

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Collaborators and Customers DoD

MCPD Fallbrook (TOW) NSWC Corona (RAM,

ESSM) NSWC Yorktown (AMRAAM) AMCOM/RDEC (Stinger)

DOE LANL Enhanced Surveillance

Campaign LANL Core Surveillance

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The fundamental question is how to assess stockpiles as they change over time.

Stockpiles change over time due to materials degradation, life-extension programs, maintenance, use, and other factors.

Assessment requires

the development of system models that capture parts, functions, dynamics, and interactions

the integration of multiple data sources, including historical data, surveillance testing, accelerated life testing, computer model output, and materials characterization.

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Reliability as Currently Practiced

Guidance Section

Control SectionActive Optical Target

Detector

Ordnance Section

Propulsion Section

Canister

RAUR= RG*RC*RA* RO* RP*RCst=0.95

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The (growing) ChallengeSuppose that we are trying to assess a stockpile that has

Multiple variants, Multiple data sources, Distributed expertise, Limits on functional testing

and that we want A numerical estimate of current reliability and performance

based on individual and group characteristics, A prediction of how reliability and performance change over

time, Uncertainties on the estimates and predictions, perhaps as

part of a capability based surveillance plan design A system description that captures stockpile environments and

use dynamics.

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Technical Challenges

Facing a multilevel data modeling and inference challenge in order to incorporate system-component surveillance data sources

Keeping track of multi-level data and dependencies Existing optimal experimental design methods cannot be

employed to compare the relative value of multiple, multi-level experiment types

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To combine the data from these different data sources, we need an approach that allows flexibility:

There is a considerably variability in how much data is observed for different pieces of the system

Not all components will have quality assurance data The specification limits are thought to be approximations of when the part

will fail, but do not necessarily match exactly with the flight data Observed flight failure modes will not necessarily specify the failure of every

component There is frequently ambiguity about which component failed during flight

testing

Integrating Components of Model into Unified Analysis

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System Ethnography

a) Capturing hypotheses from all system stakeholders about what components exist in the system, and how those components relate to one another;

b) Encoding component behaviors as set of rules which can tested against observed system behaviours;

b) Incorporating dynamic system behaviors across all operational modes of the system;

c) Linking component state information to quantitative and qualitative data sources;

d) Performing checks to determine whether component reliability hypotheses are consistent and result in calculable reliability models;

e) And inferring all possible combinations of component states that can result in observed system behaviors.

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System Ethnography and Stitching together a System Behavioral Data Model

System component logic

--missile descriptions and documentation

--expert knowledge

--existing FMECA

--life-cycle/maintenance records

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System Ethnography and Stitching together a System Behavioral Data Model (II)

Missile time-

line information

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Fault/diagnostic/telemetry information

Activity Failure Mode

Related Hardware Possible Root Cause

RAMDesignation

Missile Not Detected

Ship Error in Ship Controls

Ship, Launcher Error in Ship to Launcher Interface

Launcher Error in Launcher

Umbilical Error in Launcher to GMRP Interface

TELEMETRY_PARAMETER

Fuse

AOTD (Active Optical Target Detector) Battery







ELX (Electronics) Battery

ELM (Electro Mechanical) Battery

ELX (Electronics) Battery

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Using surveillance information from multiple variants can reduce uncertainty and improve prediction.

• We are developing the Graphical Ontology Modeling and Inference Tool (GROMIT) for system representation and qualitative inference.

• The gray boxes are parts or functions that appear in other variants of the system.

Act RDL BPS(Front)

BPS(Rear)

IRU ElectronicsUnit

Block 1 I Same Same Same I I

Block 2 I Same Same Same I II

… … … … … … …

Block n II Same Same Same II VII

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Combine all available information to understand uncertainties in system reliability and performance.

• Data is often available from many different experiments: flight tests, component tests, accelerated life tests.

• GROMIT allows us to understand what the data tells us about the system.

• We also develop statistical methods to formally combine the information into a unified system reliability estimate.

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GROMIT allows us to combine information from different experts into an integrated system view.

• Different subject matter experts understand different parts of the system.

• GROMIT highlights potential differences in system assumptions and understanding from various experts, to create a more accurate system representation.

• Effective assessment requires an integrated system view.

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GROMIT captures the in-use dynamics and failure modes of a system.

Different failure modes affect the system under various use environments giving more precise information about specific component reliabilities.

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GROMIT facilitates qualitative exploration of systems.

• Any system function or part can be set to any state and the results are propagated throughout the system to produce cut-sets.

• For example, if a particular failure mode is observed, we can produce a list of all combinations of parts state which might have caused this.

• GROMIT is not binary, but handles multiple states.

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System Reliability Estimate

Stage 1

C1 C2 C3

Stage 8

C28 C29 C30

SystemWhat parts are in the system, how specification data links to the parts and…

…the reliability information content of a particular system level outcome upon component level performance.

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Individual Missile Component Information is then Rolled up to Provide System Reliability

System Reliability at any age is the product of all of the component reliabilities in a serial system

P(system success) = function of component reliabilities

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Future Directions “Response Space Knowledge Modeling” Improved fault isolation Better characterization of continuous, non-DAG

types of dependencies Stitching together analog FMECA (particularly for

very large architectures)

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