Small-scale Structural Health Monitoring in the Cloud

Transcription

1 Small-scale Structural Health Monitoring in the Cloud Tom Henderson, PI DDDAS PI Meeting Yorktown Heights, NY 1-3 December

2 Colleagues Eddie Grant Tom Henderson Dan Adams V. John Mathews 2 Anshul Joshi Wenyi Wang Sabita Nahata Jingru Zhou Sungwon Kim Bibhisha Uprety

3 Vision for SHM

4 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, 41 st Annual Review of Progress in Quantitative NDE, Boise, ID, July, Numerical Simulation and Experimental Validation of Lamb Wave Propagation Behavior in Composite Plates, S. Kim, B. Uprety, D.O. Adams, and V.J. Mathews, 41 st Annual Review of Progress in Quantitative NDE, Boise, ID, July, Impact Location Estimation in Anisotropic Structures, J. Zhou and V.J. Mathews, 41 st Annual Review of Progress in Quantitative NDE, Boise, ID, July, Damage Mapping in Structural Health Monitoring Using a Multi-Grid Architecture, V.J. Mathews, 41 st Annual Review of Progress in Quantitative NDE, Boise, ID, July,

5 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 , Generative Cognitive Representation for Embodied Agents,'' A. Joshi and T.C. Henderson, IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems, Beijing, Sept , 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 ,

6 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

7 SHM Needs in New Composite Airplanes Assess the flightworthiness of airplanes each time before they take off 7

8 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

9 A Model-Free SHM System (Mathews and Adams) Excite structure Structure To be monitored Data acquisition Detect events - Identify event - Choose/Place sensors Assess extent of damage Structural analysis Residual strength assessment Maintenance recommendation 9

10 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

11 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

12 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, m, n j) Solve for d(m,n) from an over-determined set of linear equations Same idea as that of tomographic reconstruction of medical images. 12

13 Block Diagram of Anomaly Mapping Algorithm 13

14 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

15 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

16 Damage Mapping Example for a Series of Damaging Impacts Image reproduced with permission from Boeing 16

17 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 17 Estimated group velocity for mode identification

18 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. 18 Here S0(t) and A0(t) are the modes calculated using analytical model and X(t) is the experimental result.

19 Separation of Overlapped Modes When the Dispersion Curves of the Structure are Unknown t represents time and f represents frequency 19

20 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

21 Wave Propagation using Lamb Waves and Born Linearization (Wenyi Wang) Imaging using Kirchoff migration 21

22 Straight Path Imaging 22

23 Zig-Zag & Spiral Path 23

24 Other Path 24

25 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

26 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

27 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

28 Major Objectives (cont d) 3. Rigorous uncertainty models of: System states Model parameters Sensor network parameters (e.g., location, noise) Material damage assessments 28

29 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 29

30 Experiments on an Aluminum Plate Aluminum panel of thickness 1.6mm Fixed sensors (Acellent) Mobile sensors (VS900-RIC Sensor: khz, ceramic face, integrated preamplifier; 34 db gain) Excitation signal 5 cycle, Hanning window 30

31 SLAMbot: First Cut Movie: Henderson-Thomas- DDDAS-Pimeeting- Dec2014-Movie1 31

32 Damage Detection (cont d) 32

33 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 33

34 Damage Detection: Based on Ultrasound Physics Model 34

35 Test Configuration 35

36 Ultrasound Signals Received 36

37 Recovered Ellipses 37

38 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 38

39 SLAMBOT II Camera 39 Ultrasound Sensors

40 EKF SLAM Estimate p(x t, m z 1:t, u 1:t ) where x t : robot s pose at time t m: map of environment z 1:t : sensor measurements u 1:t : control values 40

41 Vision & Ultrasound SLAM Y-axis h Map damage Locations & Uncertainty 41 X-axis

42 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

43 VVUQ for Sensor Networks 43

44 Camera Data Ultrasound Data HCI BCSN Router Lamb Wave Simulation & Model Calibration Onboard Sensors 44

45 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

46 Data Routing Example 46

47 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. 47

48 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

49 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

50 Decision Maker Support Argumentation System Bayesian Framework Make weaknesses of models and uncertainties of results evident to human 50

51 Bayesian Argumentation Framework (with X. Fan) 51

52 Bayesian Argumentation (Michael Bradshaw) Agent 1 says C(a) = , C(!a) = Agent 1 says C(b) = , C(!b) = Agent 1 says C(c) = , C(!c) = Agent 1 says C(d) = , C(!d) = Agent 1 says C(e) = , C(!e) = Agent 2 says C(x) = , C(!x) = Agent 2 says C(y) = , C(!y) = Agent 2 says C(z) = , C(!z) = Agent 2 says C(d) = , C(!d) = Agent 2 says C(e) = , C(!e) =

53 Bayesian Argumentation Asking agent 1: why a: () why b: () why c: ( a b) why d: ( c) why e: ( d) (Michael Bradshaw) Asking agent 2: why x: () why y: () why z: ( x y) why!d: ( z) why!e: (!d) 53!e

54 Other DDDAS-based SHM Research Directions 54

55 BRECCIA 55

56 IEEE MFI 2015 San Diego Special Session: DDDAS Keynote Speaker: 56 Frederica Darema

57 Questions? 57

58 Sensor Acoustic Technology Group, Inc VS900-RIC Sensor: khz, ceramic face, integrated preamplifier (34 db gain) 58