Systems and methods of prognosticating damage for structural health monitoring

• US 7,596,470 B2
• Filed: 07/10/2007
• Issued: 09/29/2009
• Est. Priority Date: 09/22/2003
• Status: Expired due to Fees

• Current Assignee: Advanced Structure Monitoring, Inc.
• Original Assignee: Advanced Structure Monitoring, Inc.
• Inventors: Kim, Hyeung-Yun
• Primary Examiner(s): Feliciano; Eliseo Ramos
• Assistant Examiner(s): O MALLEY, MARY CATHERINE
• Application Number:
  US11/827,244
• Publication Number:
  US 20070265808A1
• Time in Patent Office: 812 Days
• Field of Search: 702/81, 702182-183, 702/185, 702/188, 703/1, 703 6- 7, 714 25- 26, 714/47
• US Class Current: 702/183
• 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 an actuator patch and a receiver patch, at least one of the patches being able to operate as both an actuator patch and a receiver patch, the method comprising:

(a) causing, by use of a computer processor, the 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, each said sensor signal being a portion of the transmitter signal emitted by the at least one of the patches and propagated through the structure;

(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 at 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. The method of claim 1, wherein the transmitter signal includes at least one of ultrasonic wave, Lamb wave, vibrational wave, and acoustic wave.

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

preparing a network that includes a plurality of paths and nodes, each of the sensors corresponding to one of the nodes, each path connecting two of the nodes of the network to thereby form an edge of the network, at least one of the sensors being adapted to emit a transmitter signal that propagates through a host structure of the network, the sensors being adapted to receive the transmitter signal emitted by the at least one of the sensors;

causing the sensors to obtain a set of sensor signals generated by an external load applied to the host structure at a point in time;

analyzing the set of sensor signals by use of the network to characterize the external load and a variation in a structural condition of the host structure;

repeating the steps of causing the sensors to obtain a set of sensor signals and analyzing the set of sensor signals to thereby obtain multiple sets of sensor signals from the sensors and variations in the structural condition of the host structure; and

prognosticating, based on the multiple sets of sensor signals and the variations in the structural condition, a structural condition of the host structure upon application of an external load to the host structure at a future point in time.

11. The method of claim 10, 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 future paint in time.

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

13. The method of claim 12, further comprising:

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

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

15. The method of claim 13, wherein the step of prognosticating a structural condition Includes:

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

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

17. The method of claim 10, wherein the sensor signal includes at least one of ultrasonic wave, Lamb wave, vibrational wave, and acoustic wave.

18. 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 an actuator patch and a receiver patch, at least one of the patches being able to operate as both an actuator patch and a receiver 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, the 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, each said the sensor signal being a portion of the transmitter signal emitted by the at least one of the patches and propagated through a host structure;

(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.

19. The computer readable medium of claim 18, 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.

20. The computer readable medium of claim 18, 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, or HTML enable devices.

21. The computer readable medium of claim 18, 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.

22. The computer readable medium of claim 18, wherein the transmitter signal includes at least one of ultrasonic wave, Lamb wave, vibrational wave, and acoustic wave.

23. A computer readable medium carrying one or more sequences of instructions for prognosticatlng 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:

preparing a network that includes a plurality of paths and nodes, each of the sensors corresponding to one of the nodes, each path connecting two of the nodes of the network to thereby form an edge of the network, at least one of the sensors being adapted to emit a transmitter signal that propagates through a host structure of the network, the sensors being adapted to receive the transmitter signal emitted by the at least one of the sensors;

causing the sensors to obtain a set of sensor signals generated by an external load applied to the host structure at a point in time;

analyzing the set of sensor signals by use of the network to characterize the external load and a variation in a structural condition of the host structure;

repeating the steps of causing the sensors to obtain a set of sensor signals and analyzing the set of sensor signals to thereby obtain multiple sets of sensor signals from the sensors and variations in the structural condition of the host structure; and

prognosticating, based on the multiple sets of sensor signals and the variations in the structural condition, a structural condition of the host structure upon application of an external load to the host structure at a future point in time.

24. The computer readable medium of claim 23, 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 future point in time.

25. The computer readable medium of claim 23, wherein the transmitter signal includes at least one of ultrasonic wave, Lamb wave, vibrational wave, and acoustic wave.

26. A processor for prognosticating a structural condition, the processor being adapted to be included in a system having 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 an actuator patch and a receiver patch, the processor being further adapted to:

cause a first set of the patches to emit a first set of transmitter signals and a portion of the other patches to receive a first set of sensor signals at a first point in time, each of the first set of sensor signals being a portion of the first set of transmitter signals emitted by the first set of the patches and propagated through the host structure;

cause a second set of the patches to emit a second set of transmitter signals and a portion of the other patches to receive a second set of sensor signals at a second point in time, each of the second set of sensor signals being a portion of the second set of transmitter signals emitted by the second set of the patches and propagated through the host structure;

generate multiple sets of sensor signals including the first and second sets of sensor signals; and

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

27. The processor of claim 26, wherein the processor is further adapted to:

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

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

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

predict the structural condition by use of the system parameter estimated at the target point in time.

28. the processor of claim 26, wherein the transmitter signal includes at least one of ultrasonic wave, Lamb wave, vibrational wave, and acoustic wave.