availability modeling for complex system - logistics...
TRANSCRIPT
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Availability Modeling for
Complex SystemJustin B. Brown
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Overview & Outline
Background & Introduction
Modeling Approach
Example
Steps
Conclusion
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Why Model?
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Background & Introduction
Complex System - “a system with numerous components and
interconnections, interactions, or interdependencies that are difficult to
describe, understand, predict, manage, design, and/or change.” -
Complex System Classification, INCOSE 14th Annual International
Symposium, 2004
Availability – A measure of “the percentage of time that a system or group
of systems within a unit are operationally capable of performing an
assigned mission.” DoD RAM-C Manual
Measure of Operational Suitability
Measure of Operational Effectiveness
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Need
Complex System comprehension
Early System Development
Networked Systems
Sustainment KPP (Key Performance Parameter)
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Modeling Approach
Bayesian Networks
Current Use
Based on Node Probability Tables
Model Based Systems Engineering
Data Sharing
Distributed Models
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Availability Equations
𝐼𝑛ℎ𝑒𝑟𝑒𝑛𝑡 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 𝐴𝑖 =𝑀𝑇𝐵𝐹
𝑀𝑇𝐵𝐹 +𝑀𝑇𝑇𝑅
𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 𝐴𝑜 =𝑀𝑇𝐵𝑀
𝑀𝑇𝐵𝑀 + (𝑀𝑇𝑇𝑅 +𝑀𝐿𝐷𝑇)
▪ MTBF = Mean Time Between Failure▪ MTBM = Mean Time Between Maintenance▪ MTTR = Mean Time To Repair▪ MLDT = Mean Logistics Delay Time
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Reasons for Using Bayesian Networks
Pros Cons
New evidence can be incorporated Difficult incorporating expert opinion into
Conditional Probability Tables
Simple representation of uncertainty Spatial and temporal dynamics
Missing observations can be taken into account Continuous data representation
Straightforward representation of causal
relationships between system variables
No feedback loops in model
Use a variety of input data Determining BN structure with experts
Structural and parameter learning
Variables can be visually displayed
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Bayesian Network Availability Modeling
Process
Step 1
• Availability Decomposition
Step 2
• Parameter Initial Estimation
Step 3
• Build Node Probability Tables
Step 4
• Integrate Bayesian Network
Step 5
• Validate Results
Step 6
• Incorporate into Complex System
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Step 1: Availability Decomposition
Can Availability be modeled?
Breakdown Availability into variables with known models:
MTBF
MTTR
MLDT
Determine variable relevancy to the problem
Determine if variable is controllable
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Example Complex System Availability
Analysis
Goal: Meet 95%
Reliability to meet
Availability KPP
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Step 2: Parameter Initial Estimation
Example: MTBF
Qualitative
Expert Opinion
Surveys
Quantitative
Field data
System analysis
Determine distribution
Normal
Weibull
Exponential
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Step 3: Build Node Probability Tables
Node Conditional Probability
Mission Success
True False
System A Functional True 0.6 0.5
False 0.4 0.5
Node Conditional Probability
Mission Success
True False
System B Functional True 0.8 0.1
False 0.2 0.9
Example Node Probability Table
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Step 4: Integrate Bayesian Network
2 Exponential systems
System A: FR = 0.0001
System C1: FR = 0.0001
2 Weibull
System B: Shape(β)=8 Scale(η)=10,000
System C2: Shape(β)=6 Scale(η)=10,000
Probability Density Function
Probability of success at 8,000 hours
Results:
94.5% Reliability
Modeled with AgenaRisk
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Example CS Availability Analysis15
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Step 5: Validate Results
Validation against expert opinion
Validation against other source data
Validation against published results
Initial estimations can skew results
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Step 6: Incorporate Into Complex
System Model
𝐶𝑜𝑚𝑝𝑙𝑒𝑥 𝑆𝑦𝑠𝑡𝑒𝑚𝐼𝑛ℎ𝑒𝑟𝑒𝑛𝑡 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 =
𝐴𝑖 =𝑀𝑇𝐵𝐹𝐶𝑆
𝑀𝑇𝐵𝐹𝐶𝑆 +𝑀𝑇𝑇𝑅𝐶𝑆
▪ Initial Estimation
▪ Use data from analysis: MTBF = 13,567 hours
▪ Assume MTTR = 8.5 hours
▪ Input known data into Availability Analysis
▪ Inherent Availability = 99.94%
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Model Based Systems TapestryProgram Management
Product Life Cycle Management
System
Requirements
MBSE Systems
Analysis
Architecture
Model
System
Model
System
Design
Thermal
Power
Structure
Reliability
Mechanical
Electrical
Software
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Detailed System Framework19
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Program Management
Product Life Cycle Management
System
Requirements
MBSE SystemsAnalysis
Architecture
Model
System
Model
System
Design
Thermal
Power
Structure
Availability
Mechanical
Electrical
Software
Model Based Systems Engineering
Incorporation
CS results valid?
CS results meet performance requirements?
Follow SE Process
Design Synthesis
Meets functional requirements
Design Loop
Optimization using systems engineering
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Use Existing Data to Improve Analysis
Accuracy
C2 MTTF Observed: 10,000 hours
Resulting Reliability Increased to 95.3%
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Summary and Conclusions
Integrated Systems Engineering model
Optimization based on procurement or mission need
Continuous improvement approach
Ideal and efficient design selection
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Next Steps and Future Work
Integrate model into actual program
Apply model to other requirements
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