Structural Health Monitoring for Aerospace Vehicles
Structural Health Monitoring for
Aerospace Vehicles
Mark Hedley
CSIRO ICT Centre
Australia
Research Partners:
CSIRO Industrial Physics
CSIRO Manufacturing & Infrastructure Technology
NASA Langley Research Center
Boeing Phantom Works
Defence Science & Technology Organisation (DSTO)
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Commonwealth Scientific and Industrial Research
Organisation (CSIRO)
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Where have we come from in the last 100 years?
Where are we now?
Where will we be in 50-100 years?
Boeing 747
1970s
Wright Bros., Kitty Hawk, 17 December 1903
29 October 2004
CENS Seminar
Boeing 7E7
2008
Structural Health Monitoring for Aerospace Vehicles
Future aerospace vehicles will
 Re-configurable (morphing)
 Structural Self Assessment
 Self Repair
 Intelligent adaptive response
Bio/Nano/Thinking/Sensing
Vehicle
This requires multi-functional material and structures
 Active/sensory/structural
 Embedded intelligence
Biomimetic functionality is being explored for ways to
achieve this
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
What is structural health monitoring?
The ability to monitor damage, assess structural health and
diagnose damage conditions
What is structural health management?
Taking action is response to damage, form a prognosis, make a
decision and take remedial action
How do they differ from current practice?
Currently based on periodic inspection
First Step - Conditioned Based Maintenance
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Outline of talk:
1. Motivation
2. General requirements and principles of future SHM systems
• Requirements for a SHM system
• Proposed Architectures
• Agents and Sensing
3. Example: the CSIRO multi-agent test-bed
•
•
•
•
Objectives and simple damage scenario
Architecture and hardware
Multi-agent algorithms
Current system status
4. Summary and conclusions
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Acknowledgements:
A team of ~ 20 people at CSIRO (CIP and the ICT Centre) has contributed the
ideas and done all the work I will describe here. The major contributors have been:
Tony Farmer
Andy Scott
Graeme Edwards
Mark Hedley
Mark Johnson
Chris Lewis
Phil Valencia
Nigel Hoschke
Mikhail Prokopenko
Peter Wang
Vadim Gerasimov
Geoff Poulton
Geoff James
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Technology
Advancement
General requirements and principles of future SHM systems.
Where do we want to go with SHM?
A few bonded
sensors connected
to a processor or
data logger
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• Integrated / embedded sensors
• Mobile sensors
• Large numbers of sensors (10ⁿ)
• Autonomous diagnostics & prognostics
• Intelligent decision-making
• Remediation strategies / self-repair
• Sensory / active materials
• Robust, adaptive, reconfigurable.
Time
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Functional Requirements for a SHM System:
Identification of threats
 What is the operational nature and environment of the structure?
 What are the potential sources of damage?
 Where might they occur?
 Can the threat be detected and avoided before damage occurs?
Monitoring
 Detection (or deduction) of damage
Sensing, interpretation, learning
 Evaluation of damage
 Diagnosis of damage
Decision-making
 Prognosis for structure
 Remediation decision
Damage models, prior knowledge, learning
Options for action: repair or remediation.
Actions
 What responses are appropriate, achievable?
 Nothing, report only, modify operational conditions, repair, abandon ship!!
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Application Requirements for a SHM System:
 Robustness: Must be able to operate effectively in the presence of damage
 Scalability: System may contain a very large number of sensors,
processors, structural elements, etc.
 Reliability:
Must be more reliable than the vehicle structure
Validation/certification is a major issue as systems become more complex
This is a problem for all types of control and safety systems
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Specific Characteristics Needed for a SHM System:
1. Wide range of reaction/response times:
 Milliseconds – impact damage, pressure leaks, …
 Seconds – cracks, disbonds, composite degradation, …
 Hours (or longer) – fatigue, corrosion, creep, wear, …
In many cases, the required reaction time depends on how early the
damage (or threat) was detected.
2. Range of decision-making processes and response types:
 “Panic” or reflex response (no reasoning)
 Considered, reasoned (intelligent) response
3. Broad spectrum of environments, risks
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CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
The ideal network architecture is a
Sensor Network:
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Robust network topology
Scalable
Distributed Processing
Wired or Wireless
Limited Network Power
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
The ideal distributed processing architecture is a
Complex Multi-Agent System:
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
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autonomous agents that interact with each other and with their environment
each agent is incapable of solving a problem alone
can display self-organization, or complex emergent behaviour
inherent redundancy, no single point of failure
well suited for handling evolving, dynamic problems
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Complex Systems
Features
 Complex vs Complicated
 Characterised by Emergent Behaviour
 No processor has global view
Issue
 Design agent properties to obtain desired emergent
behaviour using only local knowledge
We design complexity out of engineering
structures because we don’t yet know how to
control it!
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Agents in a Multi-agent-based SHM system:
Agents are autonomous, but are only aware of their local environment –
an agent can’t see the “big picture”, can’t solve the “big problem”.
Agent functions:
 Controls a suite of sensors and/or active elements
 Processes sensor data to infer local damage information
 Communicates with other agents (neighbours only)
 Contributes to emergent response
Agents may be static (embedded) or mobile (robotic)
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Sensing in a Multi-agent-based SHM system:
Sensor characteristics:
 Direct vs. indirect (damage inference/likelihood)
 Passive vs. active
 Local vs. remote
 Embedded vs. mobile
Sensing strategies in a SHM system may be very different
from “traditional” periodic inspection-based NDE
 Continuous monitoring vs. periodic inspection
– Aim to detect damage at early stage
 Primary vs. secondary sensing
 Dense vs. representative sensing
 Use of network as a sensor
The only certainty is that a multi-sensor strategy will be needed!!
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Outline of talk:
1. Motivation
2. General requirements and principles of future SHM systems
• Requirements for a SHM system
• Proposed Architecture
• Agents and Sensing
3. Example: the CSIRO multi-agent test-bed
•
•
•
•
Objectives and simple damage scenario
Architecture and hardware
Multi-agent algorithms
Current system status
4. Summary and Conclusions
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Example: the CSIRO/NASA Multi-Agent Test-bed
Objectives:
• Purpose: to experiment with and demonstrate concepts for an intelligent
vehicle health monitoring system within a relatively simple environment.
The prime focus, initially, is on systems issues (conversion of data to
information, diagnosis, prognosis, intelligent decision-making, … ),
rather than on particular sensing issues.
• Intended as a research tool and demonstrator for concepts and
techniques – to explore the possibilities.
• NOT intended to be a prototype of a practical system. Constraints of
weight, cost, power consumption, EMI, … not considered.
• High level of processing power for maximum flexibility.
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Damage scenario:
• Simple damage environment: impacts from fast particles, such
as micrometeoroids or space debris.
• 1st stage of development: aim to detect, locate and evaluate the
effects of particle impacts anywhere within the aluminium skin of a
“vehicle”.
• Later stages will develop diagnosis, prognosis and remediation
decision-making, incorporate other sensors & strategies, damage
scenarios, etc.
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Damage Simulation:
• As the hardware will not be flown in space, other
sources of impacts are required:
• A light-gas gun was used to fire 1 mm diameter stainless steel
ball bearings to speeds up to 1.5 km/s
• The focused pulse from a Nd:YAG laser (wavelength 1.06 nm)
with duration 8 ns and energy up to 0.5 J provides a good
simulation of a normally incident non-penetrating impact
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Architecture and Hardware:
Serial Communication Links
800 mm
400 mm
UDP
Communication
Link
Concept Demonstrator
Hardware Containing
Sensors and Physical Cells
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PC Cluster for
Simulating Cells
Workstation for Initialising
and Monitoring Test-bed
Vital statistics:
• Hexagonal prism
• 192 agents (cells)
• 1 mm Al sheet skin
• Cells ~100 x100 mm
• Regular mesh
• 4 x 2.5 mm dia. PVDF
sensors/cell
• Sensors on ~60 mm
square
• Passive sensing only
• Laser pulse “impacts”
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Sensor Signals:
200
Laser Pulses
Sensor signals (mV)
Particles
V ~ 200 m/s
100
25
0
0
-100
-25
-200
-20
-50
-30
-10
0
10
20
20
10
10
5
0
0
-10
-5
-20
-30
-20
-10
Time (µs)
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V ~ 1 km/s
50
0
10
-20
-20
-10
0
10
-10
0
10
20
Time (µs)
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Cell Architecture:
Skin
Sensors
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Electronics
Electronics
Preprocessing
Preprocessing
Analysis
Analysis
Communications
Communications
Data
Acquisition
Sub-module
Network
Application
Sub-Module
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Data Acquisition Sub-module:
• 5 Sensor Channels (bidirectional)
with analog filtering and
amplification of input
• 14-bit ADC up to 16 MSPS
• 150 MIPS DSP
• 256 kbyte FLASH
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Network Application Sub-module:
• 400 MIPS DSP
• 400k gate FPGA
• 2 Mbytes FLASH
• 8 Mbytes SDRAM
• 5 communication ports
• 1.2 W typical power consumption
• 64-bit unique identifier
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CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Cell Hardware:
+
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Structural Health Monitoring for Aerospace Vehicles
System Hardware:
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CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Impact Localisation:
• Lowest order extensional plate
wave detected (about 5.3 km/s)
• Localisation within cell based on
triangulation using time of arrival
difference for the four sensors
• Table lookup for fast calculation
• Error few mm within rectangle,
grows quickly outside
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Communication Protocols:
• Agent to Agent (only to neighbours)
• Between agent and workstation
(asymmetric) – not required for
operational network (network query)
• Agent to local neighbourhood
• Between static and mobile agents
• Don’t require communication between
arbitrary agents!
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Communication Protocols:
Reprogram
Nodes
Node
and
Node
Network
Control
Status
Data
Transfer Monitor
Link Control CD Flood
Protocol
Protocol
Master Route Protocol &
Master Flood Protocol
Application Layer
Agent
Software
Transport Layer
Simple Agent
Protocol
Data Link Layer
Data Link Layer
UDP (Simulated Nodes)
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Network Layer
Serial Port Device Driver
CENS Seminar
Physical Layer
Structural Health Monitoring for Aerospace Vehicles
Visualisation – Building the Network
Network discovery by the visualizer of the 188 physical cells. There are four cells
missing as a small window was left in the demonstrator to allow observation of
the inside.
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Visualisation - Current Flow
Current flow in the demonstrator, showing flow into and out of cells,
around absent cells, and the net current across cell edges
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Visualisation – 3D and Boundaries
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Multi-Agent Algorithms:
 Aim to develop local agent algorithms that will result in desirable
emergent behaviour of the collective system of agents.
 Simple algorithms developed so far:
– Impact boundaries
– Damage networks
– Clusters based on damage severity
– Self-replication of damage region
 Entropy-based metrics developed to measure stability of
emergent behaviour.
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Impact Boundaries: simple, involves single cells only.
White cells: direct impact
damage
Red cells: indirect damage
Blue cells: form boundary
between damaged and
undamaged regions.
White lines: form
continuous, connected
damage boundary.
May be used to route
messages around a
damaged region.
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Impact Networks: involves groups of cells.
White cells: direct impact
damage.
Algorithm simulates ants
foraging for food. Forms
shortest path between
“food sources” (and
avoiding obstacles).
Green cells: pheromone
levels.
May be useful for damage
evaluation, e.g. guidance
of a mobile sensor or
repair agent.
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Current System Status:
• We have built a platform for undertaking research in SHM
• Dense wired sensor network for impact detection
• 192 Nodes and 4 Sensors per Node
• We have undertaken initial research in the use of multi-agent
systems for distributed processing for structural health
diagnostics
Underlying research issue is to develop techniques for multiagent-based knowledge management, learning and emergent
decision-making.
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Current Developments:
• Mobile robot for
secondary inspection
and repair
• Wireless communication
using acoustics through
skin and RF (802.15.4)
• Damage evaluation
using active acoustic
sensors
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Future Developments:
• Secondary Inspection
• Actions
• Reflex response
• Intelligent Response
• Detection of other damage modes
• Other materials (e.g. composites, metal foams)
29 October 2004
CENS Seminar
Structural Health Monitoring for Aerospace Vehicles
Conclusion:
 We can expect to see large qualitative as well as
quantitative changes in SHM in coming years
 The extent and directions will depend on
advances in:
― Multi-functional materials
― Embedded processing
― Intelligent systems
 This will be an enabling technology not just for
more efficient maintenance, but for radically
different aerospace vehicles
29 October 2004
CENS Seminar