Small-scale Structural Health Monitoring in the Cloud
Small-scale Structural Health Monitoring in the Cloud Tom Henderson, PI
DDDAS PI Meeting Yorktown Heights, NY 1-3 December 2014 1
DDDAS PI Meeting Dec 1-3 2014
Colleagues
Eddie Grant Tom Henderson Dan Adams
Anshul Joshi 2
Wenyi Wang
Sabita Nahata
Jingru Zhou
V. John Mathews
Sungwon Kim
Bibhisha Uprety
DDDAS PI Meeting Dec 1-3 2014
2020 Vision for SHM 3
http://www.cs.wright.edu/~jslater/SDTCOutreachWebsite/pic13.pdf
DDDAS PI Meeting Dec 1-3 2014
Mid-Year Results •
•
•
•
4
“A Comparative Evaluation of Piezoelectric Sensors for Acoustic EmissionBased 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. DDDAS PI Meeting Dec 1-3 2014
Mid-Year Results (cont’d) •
•
•
5
“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.
DDDAS PI Meeting Dec 1-3 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
6
DDDAS PI Meeting Dec 1-3 2014
SHM Needs in New Composite Airplanes
• Assess the flightworthiness of airplanes each time before they take off
7
http://www.stanford.edu/~gorin/papers/IWSHM2007OptEst.pdf
DDDAS PI Meeting Dec 1-3 2014
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. 8
DDDAS PI Meeting Dec 1-3 2014
A Model-Free SHM System (Mathews and Adams)
Excite structure Structure To be monitored
Data acquisition
Assess extent of damage
9
Detect events
- Identify event - Choose/Place sensors
Structural analysis
Residual strength assessment Maintenance recommendation
DDDAS PI Meeting Dec 1-3 2014
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.
10
DDDAS PI Meeting Dec 1-3 2014
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.
11
DDDAS PI Meeting Dec 1-3 2014
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”
D(i, j ) d (m, n)l (m, n, i, j ) m,n
Solve for d(m,n) from an over-determined set of linear equations Same idea as that of tomographic reconstruction of medical images.
12
DDDAS PI Meeting Dec 1-3 2014
Block Diagram of Anomaly Mapping Algorithm
13
DDDAS PI Meeting Dec 1-3 2014
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.
14
DDDAS PI Meeting Dec 1-3 2014
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
15
DDDAS PI Meeting Dec 1-3 2014
Damage Mapping Example for a Series of Damaging Impacts
Image reproduced with permission from Boeing
16
DDDAS PI Meeting Dec 1-3 2014
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 ∆𝐷 velocity= ∆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
17
DDDAS PI Meeting Dec 1-3 2014
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.
18
DDDAS PI Meeting Dec 1-3 2014
Separation of Overlapped Modes When the Dispersion Curves of the Structure are Unknown
t represents time and f represents frequency
19
DDDAS PI Meeting Dec 1-3 2014
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.
20
DDDAS PI Meeting Dec 1-3 2014
Wave Propagation using Lamb Waves and Born Linearization (Wenyi Wang) • Imaging using Kirchoff migration
21
DDDAS PI Meeting Dec 1-3 2014
Straight Path Imaging
22
DDDAS PI Meeting Dec 1-3 2014
Zig-Zag & Spiral Path
23
DDDAS PI Meeting Dec 1-3 2014
Other Path
24
DDDAS PI Meeting Dec 1-3 2014
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 25
DDDAS PI Meeting Dec 1-3 2014
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) 26
DDDAS PI Meeting Dec 1-3 2014
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
27
DDDAS PI Meeting Dec 1-3 2014
Major Objectives (cont’d) 3. Rigorous uncertainty models of: • System states • Model parameters • Sensor network parameters (e.g., location, noise) • Material damage assessments
28
DDDAS PI Meeting Dec 1-3 2014
Major Objectives (cont’d): 4. Exploit Grid or Cloud Resources: • • • •
29
Real-time Data Storage Real-time On-line Sensor Reading Model Simulation and Calibration Information Feedback
DDDAS PI Meeting Dec 1-3 2014
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
30
DDDAS PI Meeting Dec 1-3 2014
SLAMbot: First Cut Movie: Henderson-ThomasDDDAS-PimeetingDec2014-Movie1
31
DDDAS PI Meeting Dec 1-3 2014
Damage Detection (cont’d)
32
DDDAS PI Meeting Dec 1-3 2014
Dynamic Data-Driven Approach for SHM
Data collection using mobile SLAMBOT on surface of the structure
33
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
DDDAS PI Meeting Dec 1-3 2014
Damage Detection: Based on Ultrasound Physics Model
34
DDDAS PI Meeting Dec 1-3 2014
Test Configuration
35
DDDAS PI Meeting Dec 1-3 2014
Ultrasound Signals Received
36
DDDAS PI Meeting Dec 1-3 2014
Recovered Ellipses
37
DDDAS PI Meeting Dec 1-3 2014
SLAM Exploitation • Lamb Wave Data (ellipses) provide landmarks that are incorporated into SLAM algorithm • Robot Motion Model based on standard 2track model • Uncertainties characterized by Lamb wave properties and motion error 38
DDDAS PI Meeting Dec 1-3 2014
SLAMBOT II Camera
39
Ultrasound Sensors DDDAS PI Meeting Dec 1-3 2014
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 40
DDDAS PI Meeting Dec 1-3 2014
Vision & Ultrasound SLAM Y-axis h
Map damage Locations & Uncertainty
X-axis 41
DDDAS PI Meeting Dec 1-3 2014
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 42
DDDAS PI Meeting Dec 1-3 2014
VVUQ for Sensor Networks
43
DDDAS PI Meeting Dec 1-3 2014
Camera Data
Ultrasound Data
HCI
BCSN Router
Lamb Wave Simulation & Model Calibration Onboard Sensors 44
DDDAS PI Meeting Dec 1-3 2014
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 45
DDDAS PI Meeting Dec 1-3 2014
Data Routing Example
46
DDDAS PI Meeting Dec 1-3 2014
Sensor Nodes •
Sensors on individual devices communicate with network connected sensor nodes over preferred sensor protocol •
•
Sensor nodes are applications running on host devices that have the capability of talking with the Router over socket connections • • • •
47
E.g., GPS sensors can exchange data over I2C or RS-232(serial) interconnects
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.
DDDAS PI Meeting Dec 1-3 2014
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.
48
DDDAS PI Meeting Dec 1-3 2014
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.
49
DDDAS PI Meeting Dec 1-3 2014
Decision Maker Support • Argumentation System • Bayesian Framework • Make weaknesses of models and uncertainties of results evident to human
50
DDDAS PI Meeting Dec 1-3 2014
Bayesian Argumentation Framework (with X. Fan)
51
DDDAS PI Meeting Dec 1-3 2014
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 52
DDDAS PI Meeting Dec 1-3 2014
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) 53
!e
DDDAS PI Meeting Dec 1-3 2014
Other DDDAS-based SHM Research Directions
54
DDDAS PI Meeting Dec 1-3 2014
BRECCIA
55
DDDAS PI Meeting Dec 1-3 2014
IEEE MFI 2015 San Diego Special Session: DDDAS Keynote Speaker: Frederica Darema 56
DDDAS PI Meeting Dec 1-3 2014
Questions?
57
DDDAS PI Meeting Dec 1-3 2014
Sensor Acoustic Technology Group, Inc
• VS900-RIC Sensor: 100-900 kHz, ceramic face, integrated preamplifier (34 dB gain)
58
DDDAS PI Meeting Dec 1-3 2014
DDDAS PI Meeting Yorktown Heights, NY 1-3 December 2014 1
DDDAS PI Meeting Dec 1-3 2014
Colleagues
Eddie Grant Tom Henderson Dan Adams
Anshul Joshi 2
Wenyi Wang
Sabita Nahata
Jingru Zhou
V. John Mathews
Sungwon Kim
Bibhisha Uprety
DDDAS PI Meeting Dec 1-3 2014
2020 Vision for SHM 3
http://www.cs.wright.edu/~jslater/SDTCOutreachWebsite/pic13.pdf
DDDAS PI Meeting Dec 1-3 2014
Mid-Year Results •
•
•
•
4
“A Comparative Evaluation of Piezoelectric Sensors for Acoustic EmissionBased 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. DDDAS PI Meeting Dec 1-3 2014
Mid-Year Results (cont’d) •
•
•
5
“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.
DDDAS PI Meeting Dec 1-3 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
6
DDDAS PI Meeting Dec 1-3 2014
SHM Needs in New Composite Airplanes
• Assess the flightworthiness of airplanes each time before they take off
7
http://www.stanford.edu/~gorin/papers/IWSHM2007OptEst.pdf
DDDAS PI Meeting Dec 1-3 2014
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. 8
DDDAS PI Meeting Dec 1-3 2014
A Model-Free SHM System (Mathews and Adams)
Excite structure Structure To be monitored
Data acquisition
Assess extent of damage
9
Detect events
- Identify event - Choose/Place sensors
Structural analysis
Residual strength assessment Maintenance recommendation
DDDAS PI Meeting Dec 1-3 2014
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.
10
DDDAS PI Meeting Dec 1-3 2014
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.
11
DDDAS PI Meeting Dec 1-3 2014
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”
D(i, j ) d (m, n)l (m, n, i, j ) m,n
Solve for d(m,n) from an over-determined set of linear equations Same idea as that of tomographic reconstruction of medical images.
12
DDDAS PI Meeting Dec 1-3 2014
Block Diagram of Anomaly Mapping Algorithm
13
DDDAS PI Meeting Dec 1-3 2014
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.
14
DDDAS PI Meeting Dec 1-3 2014
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
15
DDDAS PI Meeting Dec 1-3 2014
Damage Mapping Example for a Series of Damaging Impacts
Image reproduced with permission from Boeing
16
DDDAS PI Meeting Dec 1-3 2014
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 ∆𝐷 velocity= ∆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
17
DDDAS PI Meeting Dec 1-3 2014
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.
18
DDDAS PI Meeting Dec 1-3 2014
Separation of Overlapped Modes When the Dispersion Curves of the Structure are Unknown
t represents time and f represents frequency
19
DDDAS PI Meeting Dec 1-3 2014
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.
20
DDDAS PI Meeting Dec 1-3 2014
Wave Propagation using Lamb Waves and Born Linearization (Wenyi Wang) • Imaging using Kirchoff migration
21
DDDAS PI Meeting Dec 1-3 2014
Straight Path Imaging
22
DDDAS PI Meeting Dec 1-3 2014
Zig-Zag & Spiral Path
23
DDDAS PI Meeting Dec 1-3 2014
Other Path
24
DDDAS PI Meeting Dec 1-3 2014
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 25
DDDAS PI Meeting Dec 1-3 2014
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) 26
DDDAS PI Meeting Dec 1-3 2014
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
27
DDDAS PI Meeting Dec 1-3 2014
Major Objectives (cont’d) 3. Rigorous uncertainty models of: • System states • Model parameters • Sensor network parameters (e.g., location, noise) • Material damage assessments
28
DDDAS PI Meeting Dec 1-3 2014
Major Objectives (cont’d): 4. Exploit Grid or Cloud Resources: • • • •
29
Real-time Data Storage Real-time On-line Sensor Reading Model Simulation and Calibration Information Feedback
DDDAS PI Meeting Dec 1-3 2014
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
30
DDDAS PI Meeting Dec 1-3 2014
SLAMbot: First Cut Movie: Henderson-ThomasDDDAS-PimeetingDec2014-Movie1
31
DDDAS PI Meeting Dec 1-3 2014
Damage Detection (cont’d)
32
DDDAS PI Meeting Dec 1-3 2014
Dynamic Data-Driven Approach for SHM
Data collection using mobile SLAMBOT on surface of the structure
33
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
DDDAS PI Meeting Dec 1-3 2014
Damage Detection: Based on Ultrasound Physics Model
34
DDDAS PI Meeting Dec 1-3 2014
Test Configuration
35
DDDAS PI Meeting Dec 1-3 2014
Ultrasound Signals Received
36
DDDAS PI Meeting Dec 1-3 2014
Recovered Ellipses
37
DDDAS PI Meeting Dec 1-3 2014
SLAM Exploitation • Lamb Wave Data (ellipses) provide landmarks that are incorporated into SLAM algorithm • Robot Motion Model based on standard 2track model • Uncertainties characterized by Lamb wave properties and motion error 38
DDDAS PI Meeting Dec 1-3 2014
SLAMBOT II Camera
39
Ultrasound Sensors DDDAS PI Meeting Dec 1-3 2014
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 40
DDDAS PI Meeting Dec 1-3 2014
Vision & Ultrasound SLAM Y-axis h
Map damage Locations & Uncertainty
X-axis 41
DDDAS PI Meeting Dec 1-3 2014
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 42
DDDAS PI Meeting Dec 1-3 2014
VVUQ for Sensor Networks
43
DDDAS PI Meeting Dec 1-3 2014
Camera Data
Ultrasound Data
HCI
BCSN Router
Lamb Wave Simulation & Model Calibration Onboard Sensors 44
DDDAS PI Meeting Dec 1-3 2014
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 45
DDDAS PI Meeting Dec 1-3 2014
Data Routing Example
46
DDDAS PI Meeting Dec 1-3 2014
Sensor Nodes •
Sensors on individual devices communicate with network connected sensor nodes over preferred sensor protocol •
•
Sensor nodes are applications running on host devices that have the capability of talking with the Router over socket connections • • • •
47
E.g., GPS sensors can exchange data over I2C or RS-232(serial) interconnects
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.
DDDAS PI Meeting Dec 1-3 2014
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.
48
DDDAS PI Meeting Dec 1-3 2014
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.
49
DDDAS PI Meeting Dec 1-3 2014
Decision Maker Support • Argumentation System • Bayesian Framework • Make weaknesses of models and uncertainties of results evident to human
50
DDDAS PI Meeting Dec 1-3 2014
Bayesian Argumentation Framework (with X. Fan)
51
DDDAS PI Meeting Dec 1-3 2014
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 52
DDDAS PI Meeting Dec 1-3 2014
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) 53
!e
DDDAS PI Meeting Dec 1-3 2014
Other DDDAS-based SHM Research Directions
54
DDDAS PI Meeting Dec 1-3 2014
BRECCIA
55
DDDAS PI Meeting Dec 1-3 2014
IEEE MFI 2015 San Diego Special Session: DDDAS Keynote Speaker: Frederica Darema 56
DDDAS PI Meeting Dec 1-3 2014
Questions?
57
DDDAS PI Meeting Dec 1-3 2014
Sensor Acoustic Technology Group, Inc
• VS900-RIC Sensor: 100-900 kHz, ceramic face, integrated preamplifier (34 dB gain)
58
DDDAS PI Meeting Dec 1-3 2014
