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Distribution A: Approved for public release;
distribution unlimited. 2015-10
Presented to: RAM 8
Structural Health
Monitoring Systems
Presented by:
Thomas C. Null, PhD
U.S. Army Aviation and Missile Research,
Development, and Engineering Center
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Overview
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SHM Technology
Apache Tail Boom FEM
Design
Implementation
Testing
Detection
Localization
Future Work
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Structural Health Monitoring
Structural Health Monitoring (SHM) promises to do the following:
1. Reduce Unnecessary Inspections –
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By monitoring the structure maintainers can move away from usage based inspection
and only perform them when damage in suspected. This removes a potential source
of damage since a disassembly can often result in damage the structure (dents,
scratches that break the corrosion barrier, etc.)
2. Increased asset availability
–
with less scheduled maintenance an asset is available for duty.
3. Reduced burden on the warfighter
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An automated inspection process frees up a serviceman for other more important
tasks
4. Increases safety
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automated inspections reduces the risk of missing faults
5. Reduces costs
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An automated SHM enables the prediction of when a component will fail. Maintainers
with this knowledge can anticipate maintenance actions and reduces the amount of
spares needed thus shortening the logistics chain. Another factor is the unscheduled
maintenance is by far the most costly type in the Army. Just by reducing that will
allow for a large savings.
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Our Goals
Design a repeatable process to optimally perform
1. Detection
– Is there a problem?
2. Localization
– Where is the problem?
3. Classification (Characterization)
– How bad is the problem?
4. Prognostication
– How long before I need a repair?
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Framework
MODELS &
CONSTRAINTS
SENSORS &
ACTUATORS
OPTIMIZATION
ANALYZE
SIMULATE
SIGNAL
PROCESSING
METHOD
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Design Process
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Theoretical Development
• Physics-based model of the dynamical behavior of
the structure(e.g., FEM, FDM; elastic, thermal)
Mz(t )  Gz (t )  Kz (t )  Fu (t )
• State-Space Representation for Dynamic Systems
x (t )  Ax(t )  Bu (t )
x = state vector
u = excitation signals
y = sensor measurements
A = system matrix (dynamics)
B = excitation influence
C = observations (sensor locations)
y (t )  Cx(t )  Du (t )
x(0)  xo
• First order State-Space Representation with
Accelerometers
A=
0
I
,
−M−1 K −M −1 G
𝐶 = −Ca M −1 K G ,
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B=
0
M−1 F
D = Ca M −1 F
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S280 Physics Model
• FEM (Finite Element Model)
– Data
• Drawing Package
• 3D Optometric Scanning
• Caliper/Micrometer
– Tool
• Nastran
• V&V
– Modal Analysis
• Output
– Mass Matrix (M)
– Stiffness Matrix (K)
– And?
Mz(t )  Gz (t )  Kz (t )  Fu (t )
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Design Process
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Optimal Sensor and Actuator Design
• Measurement: Output Power Covariance Sensitivity
Q y
ref
dam
 Qy  Qy
J
2
p 2
• Steady State Response of the System:
Qy  CQxCT  DQu DT
– Controlability Gramian
AQ x + Q X AT + BQ u BT = 0
Lyapunov
• Objective function for known damage location
Qy
C
Qx T
C T D
Qu T
DT
T
T

QxC  C
C  CQx

Qu D  D
D  DQu
p
p
p
p
p
p
p
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Optimal Sensor and Actuator Design
• Unknown Damage Location
– Problem: Too many objective functions
– Solution: Global Sensitivity
• Controlability Gramian
AQ x + Q X AT + BQ u BT = 0
Lyapunov
• Observability Gramian
• Hankle
AT Q 0 + Q 0 A + C T Q u C = 0
Lyapunov
– More computationally efficient
Q 𝐱 ∗ Q0
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Optimal Sensor and Actuator Design
• Objective 1
– Maximize the minimum singular value of Hankle
• Objective 2
– Maximize sensitivity to mass change at known damage locations
• Objective 3
– Maximize sensitivity to stiffness change at known damage locations
• Objective 4
– Minimize sensitivity to manufacturing variations.
• Objective 5
– Minimize number of sensors
• Objective 6
– Minimize number of actuators
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Optimal Sensor and Actuator Design
Optimization
– Obviously a variety of methods can be
implemented
– Genetic Algorithm was chosen
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Combinatorial problem
Solutions scattered throughout design space
Pareto Optimization
Multi-objective
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Apache Tail Boom
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Model / Constraints
– FEM
Sensors
– Accelerometers
Actuators
– Piezoelectric
Signal Processing
– Detection- Change in covariance
– Maximize Observability and Controlability
– Maximize Sensitivity to “Hot Spots”
– Minimize Sensitivity to Manufacturing Variation
– Minimize Number of Sensor
– Minimize Number of Actuators
Use GA to Find Designs
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Design Process
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Evaluate Solutions
Global Sensitivity (S280)
– 1 objective function, 40 designs
– Clear shifts in objective function for increasing number of
actuators
– Hankle objective balances weighting in controllability and
observability
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Evaluate Solutions
Stiffness Comparison
– 3 Designs, 20 Objective Functions
– Normalized for comparison
– Location 19 is the least sensitive for all three designs
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Evaluate Solutions
Mass Comparison
– 3 Designs (same as previous), 20 Objective Functions
– Normalized for comparison
– Design tradeoffs
• Design 1 is best for crack detection at location 19 but worst for
corrosion detection at 19
• Design 3 has the best overall corrosion detection, but the worst crack
detection at location 19
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Design Process
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Implementation
• Designs Chosen
– 1 Actuator - 2 Sensor
– 2 Actuator - 4 Sensor
• Translate GA genes to FEM node IDs
– Verify you can actually put a sensor/actuator at those locations.
Actuators
Sensors
17558
9549
7875
2120
20110
24303
1
20
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Implementation
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Attach sensors/actuators
Connect Data Acquisition Unit
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Design Process
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Damage
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Multiple sites
– Stabilizer mount holes
– Skin
– Frame Ring
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Tool
– Exacto Precision Razor
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Sizes (inches)
– .02, .03, .04, .06, .08
– .16, .32, .64
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Damage Detection
• Location 3
• Design
– 1 Actuator
– 2 Sensors
• Key Takeaways
– Metric increases as damage increases
– Metric uses noise compensation
– Detection for a 0.02 inch crack
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Damage Localization Theory
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Dynamic Damage Localizing Vector (DDLV)
– “Damage Localization from the Null Space of Changes in the
Transfer Matrix” Bernal 2007
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General Concepts
– Change in transfer function can be found
– Loads in the Null Space can be calculated
– Applying those load to a model will produce no strain in regions of
change
– Approach is robust to model error because the excitation that
locates the damage is a data driven feature
Finds Where
Damage is Not
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Damage Localization
• Location 3
• Design
– 2 Actuators
– 4 Sensors
• Key takeaways
– 99.9% of Structure does not
require inspection
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Damage Localization
• Location 10
• Design
– 2 Actuators
– 4 Sensors
• Key takeaways
– 99.2% of Structure does not
require inspection
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Damage Localization
• Location 20
• Design
– 2 Actuators
– 4 Sensors
• Key takeaways
– 96.4% of Structure does not
require inspection
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Future Work
• Characterization
– Actively working on this with the AMCOM Corrosion Lab
• Prognostics (RUL)
– Near future
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