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Texas Tech University (TTU) Office of the Vice President for Research
Institute for Materials, Manufacturing, and Sustainment (IMMS) Lubbock, Texas
Dy D. Le, Director
Intelligent State Awareness for Intelligent State Awareness for Fatigue-Free Aviation Platforms
Presented to: Rensselaer Polytechnic Institute (RPI)
Troy, NY Dec 5, 2018
!"#$
Presentation Outline •! Why “Fatigue-Free Aviation Platforms” ?
•! Machine versus human “Longevity Perspective” and the search for “Bio-Inspired Living Aerial Platform”
•! Defying “Impossibilities” and envisioning “Discoveries”
-! Finding & catching “Materials Damage Precursors”
-! Cloning “Digital Nanomaterials Architecture (DNA)”
-! Enabling “Reconfigurable & Self-Healing Elements” and “Intelligent Sensing Network”
•! Demonstrating “Health State Awareness” concept of operation for achieving “Fatigue and Maintenance-Free Aircraft”
•! Enabling “Digital Twin” and “Autonomous Component Tracking” to increase aircraft safety and fleet health management
•! Highlighting “Artificial Intelligence” perspectives
•! Presenting “Intelligent State Awareness” hypothesis
•! Summary
1
%$
•! Achieve “zero-maintenance” to reduce sustainment costs
•! Increase safety and availability
Intelligent Health State Awareness Vision for Automated Structural
Health Monitoring and Fatigue-Free Aviation Platforms
Unleashing
Revolutionary Capability
MRO: Maintenance, Repair, and Operation
2
Demand
2015
2024
$79B
$69B $69B
Types Types
!"#$
$69B
2015
2024
$69B $69B Projection
$69B $69B $69B $69B 21%
Source: ICF International
19%
$69B $69B
(RW)
22%
Fixed-Wing Aircraft
Source: Global Fleet & MRO Market Forecast Summary, Oliver Wyman
2017: $76B
2027: $109B 2027: $109B Projection
North America
Western Europe Asia
Pacific
China
Value Proposition Rotary-Wing
Aircraft
2 Rotary-Wing Rotary-Wing Rotary-Wing Rotary-Wing Rotary-Wing Rotary-Wing
Aviation MRO Projections
Risk Assessment
Quantitative Total Risk
Health State Health Treatment Longevity
Sustainment
Genetics Life Style/Stress-Fatigue
Accident/Transmission
Human-Machine Longevity Sustainment
Autonomous & Assisted Environment
Preventive
Autonomous & Assisted
Design Usage/Stress & Fatigue
Environment Accident/Threats
NOT Autonomous BUT Assisted
Preventive
cs.stanford.edu Courtesy of photosearch
Adaptive
NOT Adaptive NOT Autonomous BUT Assisted
cs.stanford.edu
Platform Design Multifunctional Structures Intelligence Next-Gen Platform Intelligence Intelligence Intelligence Next-Gen Platform Intelligence Next-Gen Platform
Bio-inspired Living Aerial Platform (BiLAP)
Living Skin
3
FEEL
Multifunctional Precursor ID
Data Fusion
Diagnostics Info Format Precursor-based Indication
Physics Models
Diagnostics
Prognostics
High
Hazard Mapping
Degree of Risk
medium Low
Negligible
Quantitative Total Risk
Functional Hazard Assessment Fault Tree Analysis
Decompose SSC&I into Components
Probable Occasional
Remote Improbable
Identified Risk Acceptable Risk
Unacceptable Risk Unidentified Risk
Risk Matrix
Types of Risk
Health State Precursors
Adaptive Controls
Safety Significant Subsystems & Interfaces
Indirect Adaptive Controls
BiLAP Prototype
Multi-Scale Modeling Nano-Macro Interfaces Assessment Method
Smart Materials
Living-Skin & Robots
Integration
1 0 1 0 1 0 1 1 0 1 1 0 10110110101 0 1 0 1 0 1 0 1 0 1 1 0 1 0 101010
Prototype Prototype
Distributed Sensing
& Transmission
Direct Adaptive Controls
Assess Remaining Capabilities: Structural Integrity and Survivability
Inform Risk on Demanding Capability ASSESS RECOVER & ADAPT
Quantitative Total Risk
Probable Occasional
Remote Improbable
Identified Risk Acceptable Risk
Unacceptable Risk Unacceptable Risk Unidentified Risk
Risk Matrix
Types of Risk
On-Board Nano-Robots
Fly-by-Feel
High
Hazard Mapping
Degree of Risk
medium Low
Negligible
Functional Hazard Assessment Fault Tree Analysis Fault Tree Analysis
Decompose SSC&I into Components
Health State
Safety Significant Subsystems & Interfaces Safety Significant Subsystems & Interfaces Safety Significant Subsystems & Interfaces
Nano-Macro Interfaces
Quantitative Total Risk Quantitative Total Risk Quantitative Total Risk 1 0 1 0 1 0 1 1 0 1 1 0 10110 1 0 1 0 1 0 1 0 1 1 0 1 0 10101Self-Healing Precursors
Useful Life Remaining
Science & Technology for “Bio-Inspired Living Aerial Platform - BiLAP
4
1 0 1 0 1 0 1 1 0 1 1 0 10110 1 0 1 0 1 0 1 0 1 1 0 1 0 10101
Prototype Prototype 1 0 1 0 1 0 1 1 0 1 1 0 10110 1 0 1 0 1 0 1 0 1 1 0 1 0 10101
Risk Assessment
Adaptive Controls
Indirect Adaptive Controls
BiLAP Prototype
Direct Adaptive Controls
Living-Skin & Robots
Prototype 1 0 1 0 1 0 1 1 0 1 1 0 10110110101 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1010101 0 1 0 1 0 1 1 0 1 1 0 10110 1 0 1 0 1 0 1 0 1 1 0 1 0 10101
Digital Nanomaterial Architecture- Additive Manufacturing
Self-Healing/Advanced Sensing Network
Material Damage Precursor - Failure Correlation
Intelligent Health State Awareness Technology
5
Intelligent Health State Awareness Technology
CBM Tech
•!Benefits to operators: Enable aviation digital health monitoring, effective maintenance “big data” management - Immediate vehicle health state - Moving beyond Condition-based Maintenance (CBM) to informed material state-based awareness - Automated component health tracking
•!Empower operators with “breakthrough” technologies & capabilities to operate vertical lift aircraft with substantial reduction in maintenance costs
Present Future
Cos
ts
• Empower operators & capabilities to operate vertical lift aircraft with
Oxidation
Atomic Force Microscopy Elastic
Modulus Map of High Performance
Microfibers
Embedded Sensing
Corrosion
Rotorcraft Fatigue Crack Growth
Oxidation
Minimum Required
6
Nanorestructuring Prototype Materials “DNA” Cloning Nanorestructuring Materials “DNA” Cloning
Char
acte
rizat
ion
Build
ing
Bloc
ks
Engi
neer
ing
“Maintenance-Free” Aviation
“Fatigue-Free” Structures Materials by Design “Maintenance-Free” Aviation “Maintenance-Free” Aviation “Maintenance-Free”
Char
acte
rizat
ion
Build
ing
Bloc
ks
Build
ing
Bloc
ks C
hara
cter
izatio
n Materials Development Continuum
Materials Database – Computational M/S – Experimental & Digital Data
“Materials Genome Initiative for Global Competitiveness” – June 2011
DNA: Digital Nanomaterial Architecture
Extre
mely
Lig
htwe
ight
, Ad
aptiv
e, Du
rabl
e, an
d Da
mag
e Tol
eran
t Mat
eria
ls G
enom
e
Impr
oved
Reli
abilit
y an
d Lo
ngev
ity
7
•!Embedded microvascular networks within structural materials
•!Continuous transport of healing agents throughout structural lifetime
Can this technology be applied to composites materials with fiber
reinforcement in the resin?
•!Fire ants collectively entangle themselves to form an active structure capable of changing state from liquid to solid when subject to applied loads
Can we dynamically alter interconnections among subsystems to direct the flow of energy and entropy within networks to
achieve desired macroscopic properties?
Potential new process for new types of active, reconfigurable materials for structural morphing & healing, vibration attenuation, and dynamic load mitigation
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Hea
ling
8
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Loading Threshold
Optimizing Solution for Sustaining “Fatigue-Free”
Delayed Failure
Loading Threshold
.. !.. !.. !.. !.. !.. !..
!.. !.. !.. !.. !.. !..
.. ..
....
..
..
1
&$
5
&$&$&$&$&$Fatigue Cracks
Optimized Load
Aviation Health State Awareness Concept of Operation
7
3
6
Extended Usage
%&*+&,$-&.&/.,$0$$12343/.&456&$
Assessing Damage & Risk
Incidental Damage Delayed Failure
7
6
Extended Usage Highly Damaging Load
4
735*.&*3*/&)84&&$9:&43;*<$=&45>?$2 &$ 2 Damage Precursor
Executing Adaptive Maneuvers
Optimized Load
9
Platform Health State Awareness Controller Dashboard Health State Visualization Integrated Artificial Intelligence
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10
– Platform Health Management – !! Digital health monitoring !! Beyond CBM to informed health state-based awareness
– Beyond Intelligent Health State Awareness – !! Enable digital twin technology in near or real-time !! Allow autonomous component tracking
11 11
TTU IMMS-iDVL Collaborations
– Enable Digital Twin Concept – !! Update virtual life-limited components augmented with incoming data in real-time or near RT !! Determine remaining useful life based on actual usage and characterized damage
12
01010101 1010101 0101010101010101 0101010101001\01010101011 101010101010010101
Data uploading
TTU IMMS-iDVL
RFID
13
– Allow Autonomous Component Tracking – !! Aggregate “big data” to highlight patterns and trends from individual aircraft and fleet by serial
numbers – enable autonomous tracking of critical components
Aircraft Fleet at Glance
Zooming & Filtering
Details on Demand
TTU IMMS-iDVL Collaborations
-! Rule-Based AI – !! Good for well-defined problems and system
parameters with good known certainty !! Incapable of training and difficult to address new
hidden states and uncertainty - Statistical Learning AI -
!! Don’t follow exact rules but based on statistical models of certain types of problems – Deal with uncertainty & probability
!! Artificial Neural Network with different computation layers to process data
!! Couldn’t explain informed decision but could tell with level of probability
!! Difficult to train/address new hidden states - Physics-Centric Model Based AI-
!! Construct and/or update models in real environment & address new hidden states
!! Enable self training !! Capable of perceiving, learning, abstracting, and
reasoning
Probability of Outcomes
Machine Learning Decision Tree
Yd = f(Vi) + fe
Capable of perceiving, learning, abstracting, and
14
Cognitive capability with direct feedback
and learning
(b) Rule-based Pattern Recognition & Statistical Learning
(a) Artificial Intelligence Machine Learning (ML)
Recognition & Statistical
(a) Artificial Intelligence
Recognition & Statistical
(c) C
ogni
tive
Cap
abili
ty
(3) Facilitate system behavior change for sustaining longevity or self healing to disrupt failure cascade $
(2) Enable cognitive cueing and human-machine teaming, interaction, & communication in RT
(1) Identify Model of
Models properties,
e.g., ingredients of
longevity or
precursors of onset
of failure
Auto-Feedback Cog
nitiv
e C
apab
ility
(2) Enable cognitive Self-Learning
Achieve “Zero-Maintenance” thru intelligent comprehensive integrated solution
1
3
Goal
2 3 3 (a) Model of Models
(b) System of Systems
15
!"#$
Summary
!!Next-generation aviation platform needs multifunctional and intelligence capabilities to achieve “zero-maintenance” and “fatigue-free” vision
!!Extensive human-manual maintenance labor presents substantial cost burden for aviation stakeholders
!!CBM lack automation capability – and improving reliability - not a total solution
!!Advanced discoveries in materials damage precursor detection and characterization, materials genome, and self-healing are possible to help ease some poor reliability concerns
!!Health state awareness technology also enables Digital Twin concept and automated SHM and component tracking
!! In addition to rule-based and statistic learning, next generation of AI will include physics-models to provide cognitive capability including direct feedback and learning for Bio-inspired Living Living Platform
16
Dy D. Le, Director IMMS, Texas Tech University, Lubbock, TX
THANK YOU AND QUESTIONS?