Systems and methods of prognosticating damage for structural health monitoring

• US 20070265808A1
• Filed: 07/10/2007
• Published: 11/15/2007
• Est. Priority Date: 09/22/2003
• Status: Active Grant

• Current Assignee: Advanced Structure Monitoring, Inc.
• Original Assignee: Advanced Monitoring Systems, Inc.
• Inventors: Kim, Hyeung-Yun
• Granted Patent: US 7,596,470 B2
• Time in Patent Office: Days
• US Class Current: 702/188
• CPC Class Codes: G01H 11/00 Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties
• CPC Class Codes: G01H 11/08 using piezoelectric devices
• CPC Class Codes: G01H 9/004 using fibre optic sensors light guides per se G02B6/00, acousto-optical devices specially adapted for gating or modulating in optical wave guides G02F1/125
• CPC Class Codes: G01M 11/086 Details about the embedment of the optical fiber within the DUT
• CPC Class Codes: G01M 5/0033 by determining damage, crack or wear
• CPC Class Codes: G01M 5/0066 by exciting or detecting vibration or acceleration vibration testing of structures G01M7/00
• CPC Class Codes: G01M 5/0091 by using electromagnetic excitation or detection
• CPC Class Codes: G01N 2291/011 Velocity or travel time
• CPC Class Codes: G01N 2291/015 Attenuation, scattering
• CPC Class Codes: G01N 2291/02491 Materials with nonlinear acoustic properties
• CPC Class Codes: G01N 2291/0258 Structural degradation, e.g. fatigue of composites, ageing of oils
• CPC Class Codes: G01N 2291/0422 Shear waves, transverse waves, horizontally polarised waves
• CPC Class Codes: G01N 2291/0423 Surface waves, e.g. Rayleigh waves, Love waves
• CPC Class Codes: G01N 2291/0427 Flexural waves, plate waves, e.g. Lamb waves, tuning fork, cantilever
• CPC Class Codes: G01N 2291/106 one or more transducer arrays
• CPC Class Codes: G01N 29/043 in the interior, e.g. by shear waves
• CPC Class Codes: G01N 29/0609 Display arrangements, e.g. colour displays indicating or recording in connection with measuring in general G01D
• CPC Class Codes: G01N 29/0672 by acoustic tomography medical tomography A61B8/13
• CPC Class Codes: G01N 29/07 by measuring propagation velocity or propagation time of acoustic waves
• CPC Class Codes: G01N 29/223 Supports, positioning or alignment in fixed situation mounting transducers per se G10K11/004
• CPC Class Codes: G01N 29/245 : Ceramic probes, e.g. lead zirconate titanate [PZT] probes
• CPC Class Codes: G01N 29/2475 : Embedded probes, i.e. probes incorporated in objects to be inspected
• CPC Class Codes: G01N 29/2493 : Wheel shaped probes
• CPC Class Codes: G01N 29/28 : providing acoustic coupling , e.g. water impedance matching G10K11/02
• CPC Class Codes: G01N 29/4418 : with a model, e.g. best-fit, regression analysis
• CPC Class Codes: G01N 29/4463 : Signal correction, e.g. distance amplitude correction [DAC], distance gain size [DGS], noise filtering

Abstract

Systems, methods and recordable media for prognosticating a structural condition. A method for prognosticating a structural condition by use of a network having a plurality of diagnostic network patches (DNP), each of the patches being able to operate as at least one of a transmitter patch and a sensor patch, includes the steps of: (a) causing at least one of the patches to emit a transmitter signal and a portion of the other patches to receive a set of sensor signals at a point in time; (b) repeating the step (a) to receive multiple sets of sensor signals at multiple points in time, respectively; and (c) prognosticating, based on the multiple sets of signals, a structural condition of a host structure of the network at a target point in time.

Claims

1. A computer-implemented method for prognosticating a structural condition by use of a network having a plurality of diagnostic network patches (DNP), each of the patches being able to operate as at least one of a transmitter patch and a sensor patch, the method comprising:

(a) causing, by use of a computer processor, at least one of the patches to emit a transmitter signal and a portion of the other patches to receive a set of sensor signals at a point in time;

(b) repeating the step (a) to receive multiple sets of sensor signals at multiple points in time, respectively; and

(c) prognosticating, based on the multiple sets of signals, a structural condition of a host structure of the network at a target point in time.

2. The method of claim 1, wherein the step of prognosticating a structural condition includes:

building a dynamic system model of the network, the dynamic system model having at least one system parameter;

determining the system parameter for each of the multiple sets of sensor signals;

estimating the system parameter at the target point in time based on the determined system parameters for the multiple sets of sensor signals; and

predicting the structural condition by use of the estimated system parameter.

3. The method of claim 2, further comprising:

generating a set of sensor signals by use of the estimated system parameter.

4. The method of claim 3, further comprising:

adding the generated set of sensor signals to the multiple sets of signals thereby to update the multiple sets of signals; and

predicting, based on the updated multiple sets of signals, the structural condition at a target point in time.

5. The method of claim 2, wherein the dynamic system model is a state space model or an autoregressive moving average with exogenous input (ARMAX) model.

6. The method of claim 2, wherein the step of determining the system parameter is performed by use of a subspace system identification method.

7. The method of claim 2, wherein the step of estimating the system parameter includes:

training a neural network with the determined system parameters; and

causing the trained neural network to generate the estimated system parameter.

8. The method of claim 7, wherein the neural network is a recurrent neural network or a feedforward neural network.

9. A computer-implemented method for prognosticating a structural condition by use of a network having a plurality of sensors, the method comprising:

obtaining multiple sets of sensor signals from the sensors, each of the multiple sets being received at a point in time; and

prognosticating, based on the multiple sets of signals, a structural condition of a host structure of the network at a target point in time.

10. The method of claim 9, wherein the step of prognosticating a structural condition includes:

training a neural network with the obtained multiple sets of sensor signals; and

causing the trained neural network to generate a set of sensor signals at the target point in time.

11. The method of claim 10, wherein the neural network is a recurrent neural network or a feedforward neural network.

12. The method of claim 11, further comprising:

building a dynamic system model of the network, the dynamic system model having at least one system parameter.

13. The method of claim 12, wherein the dynamic system model is a state space model or an autoregressive moving average with exogenous input (ARMAX) model.

14. The method of claim 12, wherein the step of prognosticating a structural condition includes:

predicting a structural condition of a host structure of the network by use of the system parameter.

15. The method of claim 14, wherein the structural condition is affected by an impact on the host structure.

16. A computer readable medium carrying one or more sequences of instructions for prognosticating a structural condition by use of a network having a plurality of diagnostic network patches (DNP), each of the patches being able to operate as at least one of a transmitter patch and a sensor patch, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:

(a) causing, by use of a computer processor, at least one of the patches to emit a transmitter signal and a portion of the other patches to receive a set of sensor signals at a point in time;

(b) repeating the step (a) to receive multiple sets of sensor signals at multiple points in time, respectively; and

(c) prognosticating, based on the multiple sets of signals, a structural condition of a host structure of the network at a target point in time.

17. The computer readable medium of claim 16, wherein the step of prognosticating a structural condition includes:

building a dynamic system model of the network, the dynamic system model having at least one system parameter;

determining the system parameter for each of the multiple sets of sensor signals;

estimating the system parameter at the target point in time based on the determined system parameters for the multiple sets of sensor signals; and

predicting the structural condition by use of the estimated system parameter.

18. The computer readable medium of claim 16, wherein the one or more sequences of instructions implement a wireless communication method of Wireless Application Protocol (WAP) or Wireless Markup Language (WML) for the Internet Web Access of a WAP-enable cell phone, Pocket PC with a HTML browser, or other HTML enable devices.

19. The computer readable medium of claim 16, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the additional step of:

converting each of the multiple sets of sensor signals into an eXtensible Markup Language (XML) formatted document.

20. A computer readable medium carrying one or more sequences of instructions for prognosticating a structural condition by use of a network having a plurality of sensors, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:

obtaining multiple sets of sensor signals from the sensors, each of the multiple sets being received at a point in time; and

prognosticating, based on the multiple sets of signals, a structural condition of a host structure of the network at a target point in time.

21. The computer readable medium of claim 20, wherein the step of prognosticating a structural condition includes:

training a neural network with the obtained multiple sets of sensor signals; and

causing the trained neural network to generate a set of sensor signals at the target point in time.

22. A system for prognosticating a structural condition, comprising:

a network to be coupled to a host structure and having a plurality of diagnostic network patches (DNP), each of the patches being able to operate as at least one of a transmitter patch and a sensor patch;

means for causing at least one of the patches to emit a transmitter signal and a portion of the other patches to receive a set of sensor signals at a point in time;

means for operating the means for causing at least one of the patches to emit a transmitter signal and a portion of the other patches to receive a set of sensor signals so that multiple sets of sensor signals are received at multiple points in time, respectively; and

means for prognosticating, based on the multiple sets of signals, a structural condition of the host structure at a target point in time.

23. The system of claim 22, wherein the means for prognosticating a structural condition includes:

means for building a dynamic system model of the network, the dynamic system model having at least one system parameter;

means for determining the system parameter for each of the multiple sets of sensor signals;

means for estimating the system parameter at the target point in time based on the determined system parameters for the multiple sets of sensor signals; and

means for predicting the structural condition by use of the estimated system parameter.