Small-scale Structural Health Monitoring in the Cloud · Small-scale Structural Health Monitoring in the Cloud 1 DDDAS PI Meeting Dec 1-3 2014 Tom Henderson, PI DDDAS PI Meeting Yorktown

Small-scale Structural Health

Monitoring in the Cloud

DDDAS PI Meeting Dec 1-3 2014 1

Tom Henderson, PI

DDDAS PI Meeting

Yorktown Heights, NY

1-3 December 2014

Colleagues

Dan Adams V. John

Mathews

Sabita

Nahata Jingru

Zhou

Sungwon

Kim Bibhisha

Uprety

Anshul

Joshi

Tom Henderson Eddie Grant

Wenyi

Wang


2020 Vision for SHM http://www.cs.wright.edu/~jslater/SDTCOutreachWebsite/pic13.pdf


Mid-Year Results

• “A Comparative Evaluation of Piezoelectric Sensors for Acoustic Emission-

Based Impact Location Estimation and Damage Classification in Composite

Structure,” B. Uprety, S. Kim, D.O. Adams, and V.J. Mathews, 41st Annual

Review of Progress in Quantitative NDE, Boise, ID, July, 2014.

• “Numerical Simulation and Experimental Validation of Lamb Wave

Propagation Behavior in Composite Plates,” S. Kim, B. Uprety, D.O. Adams,

and V.J. Mathews, 41st Annual Review of Progress in Quantitative NDE,

Boise, ID, July, 2014.

• “Impact Location Estimation in Anisotropic Structures,” J. Zhou and V.J.

Mathews, 41st Annual Review of Progress in Quantitative NDE, Boise, ID,

July, 2014.

• “Damage Mapping in Structural Health Monitoring Using a Multi-Grid

Architecture,” V.J. Mathews, 41st Annual Review of Progress in Quantitative

NDE, Boise, ID, July, 2014.


Mid-Year Results (cont’d)

• “SLAMBOT: Structural Health Monitoring using Lamb Waves,'' W. Wang,

T.C. Henderson, A. Joshi and E. Grant, IEEE Conference on Multisensor

Fusion and Integration for Intelligent Systems, Beijing, Sept. 28-30, 2014.

• “Generative Cognitive Representation for Embodied Agents,'' A. Joshi

and T.C. Henderson, IEEE Conference on Multisensor Fusion and

Integration for Intelligent Systems, Beijing, Sept. 28-30, 2014.

• “Symmetry Based Semantic Analysis of Engineering Drawings,'' T. C.

Henderson, N. Boonsirisumpun and A. Joshi, IEEE Conference on

Multisensor Fusion and Integration for Intelligent Systems, Beijing, Sept.

28-30, 2014.


Goal: Replace Schedule-based Maintenance

with Condition-based Maintenance

Schedule-based

• Structural design

• Perform full-scale fatigue testing

and identify problem areas

• Construct inspection schedule

Condition-based

• Monitor aircraft continuously for

problems

• Do maintenance based on the

state of the structure as need

arises

Increases availability

Reduces maintenance costs

Increases reliability


SHM Needs in New Composite Airplanes

• Assess the flightworthiness of airplanes

each time before they take off

http://www.stanford.edu/~gorin/papers/IWSHM2007OptEst.pdf DDDAS PI Meeting Dec 1-3 2014 7

Advantages of DDDAS Approach

for Lamb Wave SHM

• The multimodal and dispersive characteristics of

Lamb waves may change due to changes in

environmental conditions and structural properties.

• This may result in failure of models (used to locate

and characterize damage) based on previous

knowledge of the medium.

• In the data driven approach, the models are

developed based on the data collected from the

experiments.

• The inference about the location, type and extent of

damage is drawn based on these models.


A Model-Free SHM System

(Mathews and Adams)

Data

acquisition

Structure

To be

monitored

Detect

events

- Identify event

- Choose/Place

sensors

Excite

structure

Assess extent

of damage

Structural

analysis

Residual strength

assessment

Maintenance

recommendation


Damage Assessment through Active SHM

Excite the structure with ultrasonic waveforms.

Collect signals that arrive at sensors distributed on the structure from the actuating transducer.

Evaluate the health of the structure based on the properties of the collection of signals.

Once the system is installed, automated inspection possible.

Physical access to inspection areas not needed during test.


Estimation of Damage Extent

Surface waves propagating through the structure get reflected from both the front and back edges of the damage

Knowing the wave velocities and the time differences between transmitted and received signals for multiple transmitter-sensor pairs will allow estimation of the damage edges.


Anomaly Mapping without Knowledge

of Wave Velocities

Abnormalities at each point in the path contribute

to the dissimilarity index in accordance with the

severity of the “damage”

Solve for d(m,n) from an over-determined set of

linear equations

Same idea as that of tomographic reconstruction of

medical images.

nm

jinmlnmdjiD,

),,,(),(),(


Block Diagram of

Anomaly Mapping Algorithm


Robustness to Sensor Damage

The performance of the algorithm does not suffer significantly if a few

damaged sensors are removed from the calculations.

We have developed a method for identifying the damaged sensors by

analyzing the “local” statistics of the damage indices.


A Damage Mapping Example

SHM systems can perform as well or better than C-scan-based NDI

systems at much lower cost, faster speed, and without expert

human supervision


Damage Mapping Example for a Series of

Damaging Impacts

Image reproduced with permission from Boeing


Mode Identification Based on Group Velocity

Estimation

Excitation signal (X)

and sensed

signal(Y)

Band pass filter X and

Y

Hilbert transform of the filtered signals to calculate

envelope of the signals

Cross correlation

between the envelopes to estimate time delay

(∆𝑡)

Group

velocity=∆𝐷

∆𝑡

• ∆𝐷 is the distance separation

between actuator and sensor

• Center frequency is shifted by some

step and the process is repeated to

get a range of velocities.

Inference from the plot: First wave packet

in the sensed signal is S0 mode. Third

wave packet is A0 mode

Estimated group velocity for mode identification


Separation of Overlapped Modes When Dispersion

Characteristics for Structure are Approximately

Known

• The technique assumes the knowledge of dispersion curves of the material.

• A filter based on the Wiener-Hopf criterion is used.

• Input to the filter: Modes generated using analytical model.

• Desired signal : Result of the experiments at the receiver sensor.

• The estimated output: Combination of individual estimated modes.

Here S0(t) and A0(t) are the modes calculated using analytical model and X(t) is the

experimental result.


Separation of Overlapped Modes When the

Dispersion Curves of the Structure are Unknown

t represents time and f represents frequency


Theoretical Vs Estimated Velocity

Final Signal reconstruction is in the stage of implementation. Once individual modes are

identified and extracted, damage mapping can be done.


Wave Propagation using

Lamb Waves and Born

Linearization (Wenyi Wang)


• Imaging using Kirchoff migration

Straight Path Imaging


Zig-Zag & Spiral Path


Other Path


Remaining Issues

• Quantify resolution of image given

robot path

• Quantify resolution of image with

randomly generated robot path

• Find best next move of the robot

given observed data

• Propagation of robot position

uncertainties


DDDAS Major Objectives

1. Bayesian Computational Sensor

Networks

• Detect & identify structural damage

• Quantify physical phenomena and

sensors

• Characterize uncertainty in calculated

quantities of interest (real and Boolean

– i.e., logical statements)


Major Objectives (cont’d)

2. Active feedback methodology using

model-based sampling regimes

• Embedded and active sensor placement

• On-line sensor model validation

• On-demand sensor complementarity


Major Objectives (cont’d)

3. Rigorous uncertainty models of:

• System states

• Model parameters

• Sensor network parameters (e.g., location,

noise)

• Material damage assessments


Major Objectives (cont’d):

4. Exploit Grid or Cloud Resources:

• Real-time Data Storage

• Real-time On-line Sensor Reading

• Model Simulation and Calibration

• Information Feedback


Experiments on an Aluminum Plate

• Aluminum panel of thickness

1.6mm

• Fixed sensors (Acellent)

• Mobile sensors (VS900-RIC

Sensor: 100-900 kHz, ceramic

face, integrated preamplifier; 34

dB gain)

• Excitation signal – 5 cycle,

Hanning window


SLAMbot: First Cut


Movie:

Henderson-Thomas-

DDDAS-Pimeeting-

Dec2014-Movie1

Damage Detection (cont’d)


Dynamic Data-Driven Approach for SHM

Data collection

using mobile

SLAMBOT on

surface of the

structure

Identification of

modes from a

mixture of

modes and

reflected wave

packets

Separation of

overlapped

modes (in case

they overlap)

Locating damage

and its extent

using the

information

extracted from

individual modes


Damage Detection: Based on

Ultrasound Physics Model


Test Configuration


Ultrasound Signals Received


Recovered Ellipses


SLAM Exploitation

• Lamb Wave Data (ellipses) provide

landmarks that are incorporated into

SLAM algorithm

• Robot Motion Model based on standard 2-

track model

• Uncertainties characterized by Lamb wave

properties and motion error


SLAMBOT II


Camera

Ultrasound Sensors

EKF SLAM

• Estimate 𝑝(𝑥𝑡, 𝑚|𝑧 1:𝑡 , 𝑢 1:𝑡 )

where

𝑥𝑡: robot’s pose at time t

𝑚: map of environment

𝑧 1:𝑡 : sensor measurements

𝑢 1:𝑡 : control values


Vision & Ultrasound SLAM


h

X-axis

Y-axis

Map damage

Locations &

Uncertainty

Broader SHM Issues

• Update the wave propagation model (i.e.,

parameter calibration of physical properties)

• Perform optimal sensor placement (i.e.,

robot motion control)

• Store image and ultrasound data off-line

• Exploit other possible localization

possibilities (e.g., RSS from wireless)

• Use structure-based sensor/actuators


VVUQ for Sensor Networks



BCSN

Router

Ultrasound Data Camera Data

Onboard

Sensors

Lamb Wave Simulation

& Model Calibration

HCI

Data Routing Model

for Cloud Computing (developed with Nishith Tirpankar)

• Data sharing model

• Highly customizable

• Low latency between sensing and

computational resources

• Optimized socket applications

• Dynamic routing


Data Routing Example


Sensor Nodes

• Sensors on individual devices communicate with network

connected sensor nodes over preferred sensor protocol

• E.g., GPS sensors can exchange data over I2C or RS-232(serial)

interconnects

• Sensor nodes are applications running on host devices that have

the capability of talking with the Router over socket connections

• Gather and serialize files, configuration and other data

• Must be registered in the Router tables

• Each data message (a single message can consist of multiple packets)

can be sent independently to any processor node.

• E.g., a single sensor node that accepts camera and GPS data can

send data to 2 different processor nodes.


Router

• Can be running on a remote machine responsible for mapping

the sensor data to the relevant processor.

• Refers to a routing table that tells it to listen on the relevant

sockets for sensor nodes

• Has a list of the processor node responsibilities. The incoming

message contains the processor type requested by the sensor

node.

• Since the router spawns new threads to handle the

communication channel between sensor and processor, the

connection is quick and reliable.

• The log data from the entire system can be collected in a single

location and analyzed if required.


Processor Node

• Application runs on a remote machine that has a

good computing capability.

• Generally handles singular responsibilities but can

also be used to consolidate data from multiple

sensors and take a decision based upon the

multiple data points.

• Can communicate on multiple sockets with each

socket having distinct responsibilities.


Decision Maker Support

• Argumentation System

• Bayesian Framework

• Make weaknesses of models and

uncertainties of results evident to

human


Bayesian Argumentation

Framework (with X. Fan)


Bayesian Argumentation (Michael Bradshaw)


Agent 1 says C(a) = 0.9000, C(!a) = 0.1000

Agent 1 says C(b) = 0.9500, C(!b) = 0.0500

Agent 1 says C(c) = 0.5985, C(!c) = 0.4015

Agent 1 says C(d) = 0.4788, C(!d) = 0.5212

Agent 1 says C(e) = 0.4740, C(!e) = 0.5260

Agent 2 says C(x) = 0.8500, C(!x) = 0.1500

Agent 2 says C(y) = 0.8000, C(!y) = 0.2000

Agent 2 says C(z) = 0.6732, C(!z) = 0.3268

Agent 2 says C(d) = 0.3941, C(!d) = 0.6059

Agent 2 says C(e) = 0.5456, C(!e) = 0.4544

Bayesian Argumentation (Michael Bradshaw)


Asking agent 1:

why a: ()

why b: ()

why c: ( a b)

why d: ( c)

why e: ( d)

Asking agent 2:

why x: ()

why y: ()

why z: ( x y)

why !d: ( z)

why !e: (!d) !e

Other DDDAS-based SHM

Research Directions


BRECCIA


IEEE

MFI 2015

San Diego


Special Session: DDDAS

Keynote Speaker:

Frederica Darema

Questions?


Sensor

• VS900-RIC Sensor: 100-900 kHz,

ceramic face, integrated

preamplifier (34 dB gain)

Acoustic Technology Group, Inc


Documents

Small-scale Structural Health Monitoring in the Cloud · Small-scale Structural Health Monitoring in the Cloud 1 DDDAS PI Meeting Dec 1-3 2014 Tom Henderson, PI DDDAS PI Meeting Yorktown