Source: http://www.google.com/patents/US7584075?dq=6,826,762
Timestamp: 2015-05-05 06:36:52
Document Index: 467484373

Matched Legal Cases: ['art 1000', 'art 1300', 'art 914', 'art 916', 'art 1400', 'art 1430', 'art 1700']

Patent US7584075 - Systems and methods of generating diagnostic images for structural health ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsSystems, methods and recordable media for generating tomographic images to monitor structural health conditions. A method includes the steps of obtaining a plurality of damage index values for a network that is coupled to a host structure and has a plurality of diagnostic network patches (DNP), generating...http://www.google.com/patents/US7584075?utm_source=gb-gplus-sharePatent US7584075 - Systems and methods of generating diagnostic images for structural health monitoringAdvanced Patent SearchPublication numberUS7584075 B2Publication typeGrantApplication numberUS 11/827,319Publication dateSep 1, 2009Filing dateJul 10, 2007Priority dateSep 22, 2003Fee statusLapsedAlso published asCN101014938A, CN101365928A, EP1685456A2, EP1685457A2, US7117742, US7197931, US7246521, US7281428, US7286964, US7590510, US7596470, US20050061076, US20050075846, US20060179949, US20060260402, US20060268263, US20070006653, US20070260425, US20070260427, US20070265806, US20070265808, US20080011086, WO2005031501A2, WO2005031501A3, WO2005031501B1, WO2005031502A2, WO2005031502A3, WO2005031502B1Publication number11827319, 827319, US 7584075 B2, US 7584075B2, US-B2-7584075, US7584075 B2, US7584075B2InventorsHyeung-Yun KimOriginal AssigneeAdvanced Structure Monitoring, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (65), Non-Patent Citations (6), Referenced by (4), Classifications (59), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetSystems and methods of generating diagnostic images for structural health monitoring
US 7584075 B2Abstract
Systems, methods and recordable media for generating tomographic images to monitor structural health conditions. A method includes the steps of obtaining a plurality of damage index values for a network that is coupled to a host structure and has a plurality of diagnostic network patches (DNP), generating a distribution of damage index value over a surface using the obtained damage index values, and formatting the distribution as at least one tomographic image. Each of the patches is able to operate as at least one of a transmitter patch and a sensor patch. The damage index values is a quantity to be affected by damage in the host structure or associated with signals generated by the patches in a response to an impact on the host structure.
1. A computer-implemented method of generating a tomographic image for structural health monitoring, comprising:
obtaining one or more damage index values for a network coupled to a structure and having a plurality of diagnostic network patches (DNP) secured to the structure, each of the patches being able to operate as at least one of a transmitter patch for transmitting a signal that propagates in the structure and a sensor patch for sensing the signal, each of the damage index values being a quantity to be affected by damage in a host structure of the network;
generating a distribution of damage index value over a surface using the obtained damage index values; and
formatting, by use of a computer process, the distribution as at least one tomographic image.
2. The method of claim 1, wherein the step of generating a distribution includes:
designating a plurality of points on the surface; and
assigning damage index values to the designated points using the obtained damage index values.
3. The method of claim 2, wherein the step of assigning damage index values includes:
employing an expert system to determine damage index values at the designated points.
4. The method of claim 3, wherein the expert system is a neuro-fuzzy inference system based on a fuzzy if-then rule for a distance of each of the transmission paths of the network and collaborated with a neural network.
5. The method of claim 4, wherein the neural network is a back propagation multiplayer perception with radial basis function networks.
6. The method of claim 2, wherein the step of generating a distribution further includes:
generating mesh-grid points on the surface; and
determining damage index values at the mesh-grid points using the assigned damage index values.
7. The method of claim 6, wherein the step of determining damage index values at the mesh-grid points includes:
utilizing an interpolation method to calculate damage index values at the mesh-grid points.
8. The method of claim 6, wherein the step of generating mesh-grid points on the surface includes:
performing Delaunay triangulation to form the mesh-grid points.
9. The method of claim 6, wherein the step of determining damage index values at the mesh-grid points includes:
applying an algebraic reconstruction technique.
10. The method of claim 6, wherein the step of determining damage index values at the mesh-grid points includes:
applying a simultaneous iterative reconstruction technique (SIRT).
11. The method of claim 6, wherein the step of determining damage index values at the mesh-grid points includes:
applying a scatter-operator-eigenfunction based technique.
training a cooperative hybrid expert system with an artificial damage; and
utilizing the cooperative hybrid expert system to filter out a false hot-spot region in the distribution.
refining the damage index values at the mesh-grid points; and
utilizing a genetic algorithm to assign the refined damage index values to the mesh-grid points thereby to generate an updated distribution of damage index value.
converting data of the mesh-grid points and the damage index values at the mesh-grid points into an eXtensible Markup Language (XML) formatted document.
15. The method of claim 1, wherein the step of generating a distribution includes:
determining intersection points where signal transmission paths of the network cross each other; and
for each said intersection point, assigning a product of two damage index values respectively associated with two crossing signal transmission paths to said intersection point.
16. The method of claim 15, wherein the step of generating a distribution further includes:
determining bisection points of a portion of the signal transmission paths, wherein each of the portion of signal transmission paths does not cross other signal transmission path, and
for each said bisection point, assigning a damage index value associated with a corresponding path to said bisection point.
17. The method of claim 15, wherein the product of two damage index values is calculated by use of a three-dimensional Gaussian function.
storing the tomographic image in a depository.
repeating the steps of obtaining a plurality of damage index values to formatting the distribution at a plurality of excitation frequencies thereby to generate a plurality of tomographic images; and
stacking the tomographic images to generate a hyperspectral cube.
repeating the steps of obtaining a plurality of damage index values to formatting the distribution at a plurality of consecutive temporal points of a damage state thereby to generate a plurality of tomographic images; and
stacking the tomographic images to generate a damage evolution manifold, wherein said damage evolution manifold represents an evolved state of a structural condition of the host structure.
21. A computer-implemented method of generating a tomographic image for structural health monitoring, comprising:
obtaining one or more damage index values for a network coupled to a structure and having a plurality of diagnostic network patches (DNP) secured to the structure, each of the patches being able to operate as at least one of a transmitter patch for transmitting a signal that propagates in the structure and a sensor patch for sensing the signal, each of the damage index values being associated with a signal generated by one of the patches in response to an impact on a host structure of the network;
22. The method of claim 21, wherein the step of generating a distribution includes:
23. The method of claim 22, wherein the step of generating a distribution further includes:
24. A computer readable medium carrying one or more sequences of instructions for generating a tomographic image for structural health monitoring, 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:
25. The computer readable medium of claim 24, wherein the step of generating a distribution includes:
26. The computer readable medium of claim 25, wherein the step of generating a distribution further includes:
27. The computer readable medium of claim 24, wherein the step of generating a distribution includes:
28. The computer readable medium of claim 27, wherein the step of generating a distribution further includes:
29. The computer readable medium of claim 24, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the additional steps of:
30. The computer readable medium of claim 24, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the additional steps of:
31. The computer readable medium of claim 24, 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-enabled cell phone, a Pocket PC with a HTML browser, or an HTML-enabled device.
32. A computer readable medium carrying one or more sequences of instructions for generating a tomographic image for structural health monitoring, 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:
33. The computer readable medium of claim 32, wherein the step of generating a distribution includes:
34. The computer readable medium of claim 33, wherein the step of generating a distribution further includes:
35. The computer readable medium of claim 32, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the additional steps of:
36. The computer readable medium of claim 32, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the additional steps of:
37. A system for generating a tomographic image for structural health monitoring, comprising:
a network to be coupled to a host structure and having a plurality of diagnostic network patches (DNP) secured to the structure, each of the patches being able to operate as at least one of a transmitter patch for transmitting a signal that propagates in the structure and a sensor patch for sensing the signal;
means for obtaining a plurality of damage index values for the network;
means for generating a distribution of damage index value over a surface using the obtained damage index values; and
means for formatting, by use of a computer process, the distribution as at least one tomographic image.
38. The system of claim 37, wherein the means for generating a distribution includes:
means for designating a plurality of points on the surface; and
means for assigning damage index values to the designated points using the obtained damage index values.
39. The system of claim 37, wherein the means for generating a distribution further. includes:
means for generating mesh-grid points on the surface; and
means for determining damage index values at the mesh-grid points using the assigned damage index values.
40. The system of claim 37, wherein the means for generating a distribution includes:
means for determining intersection points where signal transmission paths of the network cross each other; and
means for assigning, for each said intersection point, a product of two damage index values respectively associated with two crossing signal transmission paths to said intersection point.
41. The system of claim 40. wherein the means for generating a distribution further includes:
means for determining bisection points of a portion of the signal transmission paths, wherein each of the portion of signal transmission paths does not cross other signal transmission path, and
means for assigning, for each said bisection point, a damage index value associated with a corresponding path to said bisection point.
means for operating the means for obtaining a plurality of damage index values, for generating a distribution of damage index values, and for formatting the distribution at a plurality of excitation frequencies thereby to generate a plurality of tomographic images; and
means for stacking the tomographic images to generate a hyperspectral cube.
43. The system of claim 37, further comprising
means for operating the means for obtaining a plurality of damage index values, for generating a distribution of damage index values, and for formatting the distribution at a plurality of consecutive temporal points of a damage state thereby to generate a plurality of tomographic images; and
means for stacking the tomographic images to generate a damage evolution manifold, wherein said damage evolution manifold represents an evolved state of a structural condition of the host structure.
This application is a divisional application of application Ser. No. 10/942,714, entitled �Method for monitoring structural health conditions� by Kim, filed on Sep. 16, 2004, which claims the benefit of U.S. Provisional Applications No. 60/505,120, entitled �sensor and system for structural health monitoring,� filed on Sep. 22, 2003, which is hereby incorporated herein by reference in its entirety.
The present invention relates to diagnostics of structures, and more particularly to methods for monitoring structural health conditions.
Accordingly, it is one object of the invention to provide an accurate technique for determining the structural condition by using different types of methods, such as bisection, intersection, and adaptive-neural-fuzzy-inference positioning of network paths, where the technique is incorporated with convex-set interpolation.
These and other objects and advantages are attained by a structural health monitoring software that comprises interrogation, processing, classification and prognosis modules and analyses data from a diagnostic network patch (DNP) system that is attached to a host composite and/or metallic structure. The DNP system contains actuators/sensors and provides an internal wave-ray communication network in the host structure by transmitting acoustic wave impulses (or, equivalently, Lamb waves) between the actuators/sensors.
According to one aspect of the present invention, a method of generating a tomographic image for structural health monitoring includes the steps of: obtaining a plurality of damage index values for 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, each of the damage index values being a quantity to be affected by damage in a host structure of the network; generating a distribution of damage index value over a surface using the obtained damage index values; and formatting, by use of a computer process, the distribution as at least one tomographic image.
According to another aspect of the present invention, a method of generating a tomographic image for structural health monitoring includes the steps of: obtaining a plurality of damage index values for 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, each of the damage index values being associated with a signal generated by one of the patches in response to an impact on a host structure of the network; generating a distribution of damage index value over a surface using the obtained damage index values; and formatting, by use of a computer process, the distribution as at least one tomographic image.
According to still another aspect of the present invention, a computer readable medium carries one or more sequences of instructions for generating a tomographic image for structural health monitoring, 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 a plurality of damage index values for 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, each of the damage index values being a quantity to be affected by damage in a host structure of the network; generating a distribution of damage index value over a surface using the obtained damage index values; and formatting, by use of a computer process, the distribution as at least one tomographic image.
According to yet another aspect of the present invention, a computer readable medium carries one or more sequences of instructions for generating a tomographic image for structural health monitoring, 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 a plurality of damage index values for 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, each of the damage index values being associated with a signal generated by one of the patches in response to an impact on a host structure of the network; generating a distribution of damage index value over a surface using the obtained damage index values; and formatting, by use of a computer process, the distribution as at least one tomographic image.
According to a further aspect of the present invention, a system for generating a tomographic image for structural health monitoring includes: 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 obtaining a plurality of damage index values for the network; means for generating a distribution of damage index value over a surface using the obtained damage index values; and means for formatting, by use of a computer process, the distribution as at least one tomographic image.
The application file contains at least one drawing executed in color. Copies of this patent application publication with color drawings will be provided by the U.S. Patent and Trademark Office upon request and payment of the necessary fee.
FIG. 10 is a flow chart illustrating exemplary procedures of an interrogation module in accordance with one embodiment of the present invention.
FIG. 11A is a schematic diagram of an exemplary actuator network architecture including subgroups in accordance with one embodiment of the present invention.
FIG. 11B is a schematic diagram of a network architecture having actuators/sensors subgroups in accordance with another embodiment of the present invention.
FIG. 12 is a flow chart illustrating exemplary procedures for identifying Lamb wave modes in accordance with one embodiment of the present invention.
FIGS. 13A-B show a flow chart illustrating exemplary procedures for computing SCI values in accordance with one embodiment of the present invention.
FIG. 14A is a flow chart illustrating exemplary procedures for generating a tomographic image to identify the regions having changes in structural conditions or damages in accordance with one embodiment of the present invention.
FIG. 14B is a flow chart illustrating exemplary procedures for generating a tomographic image to identify the regions having changes in structural conditions or damages in accordance with another embodiment of the present invention.
FIG. 14D shows a hyperspectral tomography cube in accordance with one embodiment of the present invention.
FIG. 14E shows a 3-dimensional damage evolution manifold illustrating the variation of structural condition in accordance with one embodiment of the present invention.
FIG. 15A is a schematic diagram illustrating exemplary procedures of a neuro-fuzzy inference system for providing structured system condition index (SCI) distribution at the intersection points of network paths in accordance with one embodiment of the present invention.
FIG. 15B is a schematic diagram illustrating exemplary procedures of a cooperative hybrid expert system for simulating SCI distribution on the lattice grid points of a structure in accordance with one embodiment of the present invention.
FIG. 16A is a schematic diagram illustrating Gabor jets applied to a �hot-spot� region in accordance with one embodiment of the present invention.
FIG. 16B is a schematic diagram illustrating multilayer perception (MLP) for classifying the types of damages in accordance with one embodiment of the present invention.
FIG. 16C is a schematic diagram illustrating a fully-connected network classifier for classifying a structural condition in accordance with one embodiment of the present invention.
FIG. 16D is a schematic diagram illustrating modular network classifiers for classifying structural conditions in accordance with one embodiment of the present invention.
FIG. 17A is a flow chart illustrating exemplary procedures of a K-mean/learning vector quantization (LVQ) algorithm for developing a codebook in accordance with one embodiment of the present invention.
FIG. 17B is a schematic diagram illustrating exemplary procedures of a classification module to build a damage classifier using a codebook generated by the steps in FIG. 17A in accordance with one embodiment of the present invention.
FIG. 18A is a schematic diagram illustrating three evolution domains of a structure in operation/service, dynamics of sensory network system, and network system matrix, according to one embodiment of the present invention.
FIG. 18B schematically represents the architecture of a recurrent neural network for forecasting the future system matrix in accordance with one embodiment of the present invention.
FIG. 1A is a schematic top cut-away view of a patch sensor 100 in accordance with one embodiment of the present invention. FIG. 1B is a schematic cross-sectional view of the patch sensor 100 taken along a direction A-A of FIG. 1A. As shown in FIGS. 1A-B, the patch sensor 100 may include: a substrate 102 configured to attach to a host structure; a hoop layer 104; a piezoelectric device 108 for generating and/or receiving signals (more specifically, Lamb waves); a buffer layer 110 for providing mechanical impedance matching and reducing thermal stress mismatch between the substrate 102 and the piezoelectric device 108; two electrical wires 118 a-b connected to the piezoelectric device 108; a molding layer 120 for securing the piezoelectric device 108 to the substrate 102; and a cover layer 106 for protecting and sealing the molding layer 120. The piezoelectric device 108 includes: a piezoelectric layer 116; a bottom conductive flake 112 connected to the electrical wire 118 b; and a top conductive flake 114 connected to the electrical wire 118 a. The piezoelectric device 108 may operate as an actuator (or, equivalently, signal generator) when a pre-designed electric signal is applied through the electric wires 118 a-b. Upon application of an electrical signal, the piezoelectric layer 116 may deform to generate Lamb waves. Also, the piezoelectric device 108 may operate as a receiver for sensing vibrational signals, converting the vibrational signals applied to the piezoelectric layer 116 into electric signals and transmitting the electric signals through the wires 118 a-b. The wires 118 a-b may be a thin ribbon type metallic wire.
As moisture, mobile ions and hostile environmental condition may degrade the performance and reduce the lifetime of the patch sensor 100, two protective coating layers, a molding layer 120 and a cover layer 106 may be used. The molding layer 120 may be made of epoxy, polyimide or silicone-polyimide by the normal dispensing method. Also, the molding layer 120 may be formed of a low thermal expansion polyimide and deposited over the piezoelectric device 108 and is the substrate 102. As passivation of the molding layer 120 does not make a conformal hermetic seal, the cover layer 106 may be deposited on the molding layer 120 to provide a hermitic seal. The cover layer 120 may be made of metal, such as nickel (Ni), chromium (Cr) or silver (Ag), and deposited by a conventional method, such as electrolysis or e-beam evaporation and sputtering. In one embodiment, an additional film of epoxy or polyimide may be coated on the cover layer 106 to provide a protective layer against scratching and cracks.
FIG. 1C is a schematic top view of a piezoelectric device 130, which may be a conventional type known in the art and can be used in place of the piezoelectric device 108. FIG. 1D is a schematic cross-sectional view of the piezoelectric device 130 taken along the direction B-B of FIG. 1D. As shown FIGS. 1C-D, the piezoelectric device 130 includes: a bottom conductive flake 134; a piezoelectric layer 136; a top conductive flake 132 connected to a wire 138 b; a connection flake 142 connected to a wire 138 a; and a conducting segment 144 for connecting the connection flake 142 to the bottom flake 134. The top conductive flake 132 may be electrically separated from the connection flake 142 by a groove 140. FIG. 1E is a schematic top cut-away view of a patch sensor 150 in accordance with another embodiment of the present invention. FIG. 1F is a schematic side cross-sectional view of the patch sensor 150 shown in FIG. 1E. As shown in FIGS. 1E-F, the patch sensor 150 may include: a bottom substrate 151; a top substrate 152; a hoop layer 154; a piezoelectric device 156; top and bottom buffer layers 160 a-b; two electrical wires 158 a-b connected to the piezoelectric device 108. The piezoelectric device 156 includes: a piezoelectric layer 164; a bottom conductive flake 166 connected to the electrical wire 158 b; and a top conductive flake 162 connected to the electrical wire 158 a. The functions and materials for the components of the patch sensor 150 may be similar to those for their counterparts of the patch sensor 100. Each of the buffer layers 160 a-b may include more than one sublayer and each sublayer may be composed of polymer or metal. The top substrate 152 may be made of the same material as that of the substrate 102.
FIG. 2A is a schematic top cut-away view of a hybrid patch sensor 200 in accordance with one embodiment of the present invention. FIG. 2B is a schematic cross-sectional view of the hybrid patch sensor 200 taken along a direction C-C of FIG. 2A. As shown in FIGS. 2A-B, the hybrid patch sensor 200 may include: a substrate 202 configured to attach to a host structure; a hoop layer 204; a piezoelectric device 208; an optical fiber coil 210 having two ends 214 a-b; a buffer layer 216; two electrical wires 212 a-b connected to the piezoelectric device 208; a molding layer 228; and a cover layer 206. The piezoelectric device 208 includes: a piezoelectric layer 222; a bottom conductive flake 220 connected to the electrical wire 212 b; and a top conductive flake 218 connected to the electrical wire 212 a. In an alternative embodiment, the piezoelectric device 208 may be the same as the device 130 of FIG. 1C. The optical fiber coil 210 may include; a rolled optical fiber cable 224; and a coating layer 226. Components of the hybrid patch sensor 200 may be similar to their counterparts of the patch sensor 100.
FIG. 2C is a schematic top cut-away view of a hybrid patch sensor 240 in accordance with another embodiment of the present invention. FIG. 2D is a schematic side cross-sectional view of the hybrid patch sensor 240 shown in FIG. 2C. As shown in FIGS. 2C-D, the hybrid patch sensor 240 may include: a bottom substrate 254; a top substrate 242; a hoop layer 244; a piezoelectric device 248; an optical fiber coil 246 having two ends 250 a-b; top and bottom buffer layers 260 a-b; and two electrical wires 252 a-b connected to the piezoelectric device 248. The piezoelectric device 248 includes: a piezoelectric layer 264; a bottom conductive flake 262 connected to the electrical wire 252 b; and a top conductive flake 266 connected to the electrical wire 252 a. The optical fiber coil 246 may include; a rolled optical fiber cable 258; and a coating layer 256. Components of the hybrid patch sensor 240 may be similar to their counterparts of the hybrid patch sensor 200.
FIG. 3A a schematic top cut-away view of an optical fiber patch sensor 300 in accordance with one embodiment of the present invention. FIG. 3B a schematic side cross-sectional view of the optical fiber patch sensor 300 taken along the direction D-D of FIG. 3A. As shown in FIGS. 3A-B, the optical fiber patch sensor 300 may include: a substrate 302; a hoop layer 304; an optical fiber coil 308 having two ends 310 a-b; a molding layer 316; and a cover layer 306. The optical fiber coil 308 may include; a rolled optical fiber cable 312; and a coating layer 314. The material and function of each element of the optical fiber patch sensor 300 may be similar to those of its counterpart of the hybrid patch sensor 200 in FIG. 2A. The diameter 313 of the innermost loop may be determined by the material property of the optic fiber cable 312.
It is noted that the optical fiber coils 308 and 318 show in FIGS. 3C-D may be attached directly to a host structure and used as optical fiber coil sensors. For this reason, hereinafter, the terms �optical fiber coil� and �optical fiber coil sensor� will be used interchangeably. FIGS. 3E-F are alternative embodiments of the optical fiber coil 308. As illustrated in FIG. 3E, the optical fiber coil 330 may include: an optical fiber cable 334 having two ends 338 a-b and being rolled in the same manner as the cable 312; and a coating layer 332. The coil 330 may have a hole 336 to accommodate a fastener as will be explained later. Likewise, the optical fiber coil 340 in FIG. 3F may include: an optical fiber cable 344 having two ends 348 a-b and being rolled in the same manner as the cable 322; and a coating layer 342. The-coil 340 may have a hole 346 to accommodate a fastener. FIG. 3G is a schematic side cross-sectional view of the optical fiber coil 330 taken along the direction DD of FIG. 3E.
FIG. 4A a schematic top cut-away view of a diagnostic patch washer 400 in accordance with one embodiment of the present invention. FIG. 4B a schematic side cross-sectional view of the diagnostic patch washer 400 taken along the direction E-E of FIG. 4A. As shown in FIGS. 4A-B, the diagnostic patch washer 400 may include: an optical fiber coil 404 having two ends 410 a-b; a piezoelectric device 406; a support element 402 for containing the optical fiber coil 404 and the piezoelectric device 406, the coil 404 and the device 406 being affixed to the support element 402 by adhesive material; a pair of electrical wires 408 a-b connected to the piezoelectric device 406; and a covering disk 414 configured to cover the optical fiber coil 404 and the piezoelectric device 406.
As shown in FIGS. 4A-B, the diagnostic patch washer 400 may have a hollow space 403 to accommodate other fastening device, such as a bolt or rivet. FIG. 4C is a schematic diagram of an exemplary bolt-jointed structure 420 using the diagnostic patch washer 400 in accordance with one embodiment of the present invention. In the bolt-jointed structure 420, a conventional bolt 424, nut 426 and washer 428 may be used to hold a pair of structures 422 a-b, such as plates. It is well known that structural stress may be concentrated near a bolt-jointed area 429 and prone to structural damages. The diagnostic patch washer 400 may be incorporated in the bolt-joint structure 420 and used to detect such damages.
FIG. 4D is a schematic cross-sectional diagram of an exemplary bolt-jointed structure 430 using the diagnostic patch washer 400 in accordance with another embodiment of the present invention. In the bolt-joint structure 430, a conventional bolt 432, nut 434 and a pair of washers 436 and 438 may be used to hold a honeycomb/laminated structure 440. The honeycomb and laminate structure 440 may include a composite laminate layer 422 and a honeycomb portion 448. To detect the structural damages near the bolt-joint area, a pair of diagnostic patch washers 400 a-b may be inserted within the honeycomb portion 448, as illustrated in FIG. 4D. A sleeve 446 may be required to support the top and bottom patch washers 400 a-b against the composite laminate layer 442. Also, a thermal-protection circular disk 444 may be inserted between the composite laminate layer 422 and the diagnostic patch washer 400 b to protect the washer 400 b from destructive heat transfer.
FIG. 5A is a schematic diagram of an interrogation system 500 including a sensor/actuator device in accordance with one embodiment of the present invention. As shown in FIG. 5A, the system 500 may include: a sensor/actuator device 502 for generating and/or receiving Lamb wave signals; a two-conductor electrical wire 516; a conditioner 508 for processing signals received by the device 502; analog-to-digital (A/D) converter 504 for converting analog signals to digital signals; a computer 514 for managing entire elements of the system 500; an amplifier 506; a waveform generator 510 for converting digital signals into the analog Lamb wave signals; and a relay switch array module 512 configured to switch connections between the device 502 and the computer 514. In general, more than one device 502 may be connected to the relay switch 512.
FIG. 5B is a schematic diagram of an interrogation system 520 including a sensor in accordance with another embodiment of the present invention. The system 520 may include: a sensor 522 having an optical fiber coil; optical fiber cable 525 for connections; a laser source 528 for providing a carrier input signal; a pair of modulators 526 and 534; an acoustical optic modulator (AOM) 530; a pair of coupler 524 and 532; a photo detector 536 for sensing the light signal transmitted through the optical fiber cable 525; an A/D converter 538; a relay switch 540; and a computer 542. The sensor 522 may be one of the sensors described in FIGS. 2A-4D that may include an optical fiber coil. In one embodiment, the coupler 524 may couple the optical fiber cable 525 to another optical fiber 527 that may be connected to another sensor 523.
FIG. 6A is a schematic diagram of a diagnostic network patch system (DNP) 600 applied to a host structure 610 in accordance with one embodiment of the present invention. As illustrated in FIG. 6A, the system 600 may include: patches 602; transmission links 612; at least one bridge box 604 connected to the transmission links 612; a data acquisition system 606; and a computer 608 for managing the DNP system 600. The patches 602 may be a device 502 or a sensor 522, where the type of transmission links 612 may be determined by the type of the patches 602 and include electrical wires, optical fiber cables, or both. Typically, the host structure 610 may be made of composite or metallic material.
FIG. 6B is a schematic diagram of a diagnostic network patch system 620 having a strip network configuration in accordance with one embodiment of the present invention. As shown in FIG. 6B, the system 620 may be applied to a host structure 621 and include: patches 622; a bridge box 624 connected to a computer 626; and transmission links 632. The patches 622 may be a device 502 or a sensor 522, where the type of the patches 622 may determine the type of transmission links 632. The transmission links 632 may be electrical wires, optical fiber cables, or both.
Another configuration for building up wave �cross-talk� paths between patches may be a pentagonal network as shown in FIG. 6C. FIG. 6C is a schematic diagram of a diagnostic network patch system 640 having a pentagon network configuration in accordance with another embodiment of the present invention. The system 640 may be applied to a host structure 652 and may include: patches 642; a bridge box 644 connected to a computer 646; and transmission links 654. The patches 642 may be a device 502 or a sensor 522. As in the system 630, the patches 642 may detect a defect 650 by sending or receiving Lamb waves indicated by the arrows 648.
FIG. 6D is a schematic perspective view of a diagnostic network patch system 660 incorporated into rivet/bolt-jointed composite laminates 666 and 668 in accordance with another embodiment of the present invention. As illustrated in FIG. 6D, the system 660 may include: patches 662; and diagnostic patch washers 664, each washer being coupled with a pair of bolt and nut. For simplicity, a bridge box and transmission links are not shown in FIG. 6D. The patches 662 may be a device 502 or a sensor 522. In the system 660, the patches 662 and diagnostic patch washers 664 may detect the defects 672 by sending or receiving Lamb waves as indicated by arrows 670. Typically, the defects 672 may develop near the holes for the fasteners. The diagnostic patch washers 664 may communicate with other neighborhood diagnostic patches 662 that may be arranged in a strip network configuration, as shown in FIG. 6D. In one embodiment, the optical fiber coil sensors 330 and 340 may be used in place of the diagnostic patch washers 664.
FIG. 6E is a schematic perspective view of a diagnostic network patch system 680 applied to a composite laminate 682 that may be repaired with a bonding patch 686 in accordance with one embodiment of the present invention. As illustrated in FIG. 6E, the system 680 may include patches 684 that may be a device 502 or a sensor 522. For simplicity, a bridge box and transmission links are not shown in FIG. 6E. In the system 680, the patches 684 may detect the defects 688 located between the repair patch 686 and the composite laminate 682 by sending or receiving Lamb waves as indicated by arrows 687.
FIG. 6F is a schematic diagram illustrating an embodiment of a wireless data communication system 690 that controls a remote diagnostic network patch system in accordance with one embodiment of the present invention. As illustrated in FIG. 6F, the system 690 includes: a bridge box 698; and a ground communication system 694 that may be operated by a ground control 692. The bridge box 698 may be coupled to a diagnostic network patch system implemented to a host structure, such as an airplane 696, that may require extensive structural health monitoring.
As a microphone array may be used to find the direction of a moving source, a clustered sensor array may be used to find damaged locations by measuring the difference in time of signal arrivals. FIG. 7A is a schematic diagram of a diagnostic network patch system 700 having clustered sensors in a strip network configuration in accordance with one embodiment of the present invention. As illustrated in FIG. 7A, the system 700 may be applied to a host structure 702 and include clustered sensors 704 and transmission links 706. Each clustered sensor 704 includes two receivers 708 and 712 and one actuator/receiver device 710. Each of the receivers 708 and 712 may be one of the sensors described in FIGS. 1A-4D, while the actuator/receiver device 710 may be one of the sensors described in FIGS. 1A-2D and FIGS. 4A-D and have a piezoelectric device for generating Lamb waves. When the actuator/receiver 710 of a clustered sensor 704 sends Lamb waves, the neighboring clustered sensors 704 may receive the Lamb waves using all three elements, i.e., the actuator/receiver device 710 and receivers 708 and 712. By using all three elements as a receiver unit, each clustered sensor 704 can receive more refined Lamb wave signals. Also, by measuring the difference in time-of-arrivals between the three elements, the direction of the defect 714 may be located with enhanced accuracy.
FIG. 7B is a schematic diagram of a diagnostic network patch system 720 having clustered sensors in a pentagonal network configuration in accordance with another embodiment of the present invention. As illustrated in FIG. 7B, the system 720 may be applied to a host structure 722 to detect a defect 734 and include clustered sensors 724 and transmission links 726. Each clustered sensor 724 may be similar to the clustered sensor 704.
FIG. 8A shows a schematic diagram of a clustered sensor 800 having optical fiber coils in a serial connection in accordance with one embodiment of the present invention. The clustered sensor 800 may be similar to the clustered sensor 704 in FIG. 7A and include two sensors 804 and 808 and an actuator/sensor 806. In this configuration, an input signal may enter the sensor through one end 810 a and the output signal from the other end 810 b may be a sum of the input signal and contribution of the three sensors 804, 806 and 808. In one embodiment, the signal from each sensor may be separated from others using a wavelength-based de-multiplex techniques.
FIG. 8B a schematic diagram of a clustered sensor 820 having optical fiber coils in a parallel connection in accordance with one embodiment of the present invention. The clustered sensor 820 may be similar to the clustered sensor 704 in FIG. 7A and include two sensors 824 and 828 and an actuator/sensor 826. In this configuration, input signals may enter the three sensors through three end 830 a, 832 a and 834 a, respectively, while output signals from the other ends 830 b, 832 b and 834 b may be a sum of the input signal and contribution of the three sensors 824, 826 and 828, respectively.
FIG. 9 shows a plot 900 of actuator and sensor signals in accordance with one embodiment of the present invention. To generate Lamb waves, an actuator signal 904 may be applied to an actuator, such as a patch sensor 100. The actuator signal 904 may be a toneburst signal that has several wave peaks with the highest amplitude in the mid of waveform and has a spectrum energy of narrow frequency bandwidth. The actuator signal 904 may be designed by the use of Hanning function on various waveforms and have its central frequency within 0.01 MHz to 1.0 MHz. When the actuator receives the actuator signal 904, it may generate Lamb waves having a specific excitation frequency.
Portions 914 of sensor signals 912 may be electrical noise due to the toneburst actuator signal 904. To separate the portions 914 from the rest of sensor signals 912, masking windows 916, which may be a sigmoid function delayed in the time period of actuation, may be applied to sensor signals 912 as threshold functions. Then, moving wave-envelope windows 920, 922 and 924 along the time history of each sensor signal may be employed to extract the wave packets 926, 928 and 930 from the sensor signal of 912. The wave packets 926, 928 and 930 may be the sensor part of the sensor signal 912. The envelope windows 920, 922 and 924 may be determined by applying a hill-climbing algorithm that searches for peaks and valleys of the sensor signals 912 and interpolating the searched data point in time axis. The magnitude and position of each data point in the wave signal may be stored if the magnitude of the closest neighborhood data points are less than that of the current data point until the comparison of wave magnitude in the forward and backward direction continues to all the data points of the wave signal. Once wave envelopes 918 are obtained, each envelope may break into sub envelope windows 920, 922 and 924 with time spans corresponding to those of Lamb-wave modes.
The sub envelop windows 920, 922 and 924 may be applied to extract wave packets 926, 928 and 930 by moving along the entire time history of each measured sensor signal 912.
Upon completion of applying a DNP system to a host structure, a structural health monitoring software may start processing the DNP system, where the monitoring software may comprise interrogation, processing, classification and prognosis modules. FIG. 10 is a flow chart 1000 illustrating exemplary procedures of the interrogation module in accordance with one embodiment of the present invention. The interrogating module may find damages, identify impacts and monitor the curing and repaired-boning-patch performance of the host structures. In step 1002, the interrogation module may partition the diagnostic patches of the DNP system into subgroup sets, and designate one actuator in each of the subgroups. It is noted that each of the diagnostic patches may function as an actuator at one point in time, and thereafter the same patch may be switched to function as a sensor. FIG. 11A illustrates an example of actuator network architecture 1100 that may include subgroups partitioned by the interrogation module in accordance with one embodiment of the present invention. As each of the actuators 1102, 1104, 1106 and 1108 may also function as a sensor, various combinations of subgroups can be formed of those actuators. Arrows 1110 represent the propagation of Lamb wave signals between actuators 1102, 1104, 1106 and 1108. Table 1 shows the possible subgroups, where each group has one actuator. For example, subgroup 1 has one actuator A1 1102 and two sensors A2 1104 and A4 1108.
FIG. 11B illustrates another example of actuator/sensor network architecture 1120 that may include subgroups partitioned by the interrogation module in accordance with another embodiment of the present invention. As illustrated in FIG. 11B, four subgroups 1122, 1124, 1126 and 1128 may be generated using four actuator/sensors 1132 a-1132 d and thirteen sensors 1130 a-1130 m. Table 2 shows the elements of each subgroup formed of the patches in FIG. 11B.
The network architecture of a diagnostic patch system, such as shown in FIGS. 11A-B, can be configured to maximize overall network performance with the minimum number of the actuators and sensors. The diagnostic network can be represented by an undirected graph G=(N, E), in which the nodes N and edges E represent the patch sites and wave-communication paths, respectively. The graph G may be a picture of the relation of the diagnostic network communication, whereas the node points 1102, 1104, 1006, 1108 in FIG. 11A represent the elements of actuator and sensor sets, and solid lines as edges 1110 in FIG. 11A represent the ordered pairs in the relation from the actuator set and the sensor set in the Table 1. A graph G is connected if there is at least one path between every pair of nodes i and j. In an exemplary optimal design for network path uniformity, the following notation may be defined: n is the number of nodes; xij ∈ {0,1} is a decision variable representing paths between nodes i and j; and x(={x12, x13, . . . , xn−1,n}) is a topological architecture of network design. R(x) is a constraint of network design, such as the number of the patches; cij is the cost variable of the network design, such as the distance of Lamb wave propagation, the number of intersection points on each network path being crossed by other network paths or the sensitivity factor to excitation frequency. The optimal design of diagnostic network can be represented as follows:
arg max Z ( x ) = ∑ i = 1 n - 1 ∑ j = i + 1 n c ij x ij s . t . R ( x ) ≥ R min . , where this optimal problem must be solved for the values of the variables x(={x12, x13, . . . , xn−1,n}) that satisfy the restriction Rmin and meanwhile minimize the objective function Z(x) representing network path uniformity.
∑ c = 1 k x ic = 1 , i = 1 , � , m and ∑ c = 1 k y jc = 1 , i = 1 , � , n , where k is the number of subgroups specified, and m, n are the number of actuators and sensors, respectively.
In step 1006, the actuator in ith subgroup may be activated to generate the Lamb wave signals according to the sequential order from the relay switch array module 512 (shown in FG. 5A). Then, the signals carrying structural condition information (SCI) may be measured by the sensors of jth subgroup in step 1008, where the jth subgroup may include the th subgroup. In step 1010, the interrogation module may compute the deviation of the measured signals from baseline signals, wherein the baseline signals may be prepared by performing the steps 1004 and 1006 in the absence of the artificial defects. Next, the interrogation module may store the deviation and measured signals into a suitable signal database depository (such as computer 514) as extensible Markup Language (XML) formatted documents in step 1012. In addition, the interrogation module may save the coordinates of the actuators and sensors as well as the set-up information data including the actuation frequency, the identification number of the actuators and sensors, voltage level, patch type and operational failure status. Subsequently, the interrogation module may stop the interrogation process in step 1014.
The interrogation module may perform the steps 1006, 1008, 1010 and 1012 at discrete set of excitation frequencies, where the actuators of the DNP system may be activated at each excitation frequency to generate Lamb waves. Then, the process module may process the stored sensor signals to determine structural condition index (SCI) for each network path at an excitation frequency. The SCI of a network path between a pair of actuator and sensor refers to a quantity that may be affected by damages of the host structure and, as a consequence, represent the degree of structural condition changes probably located in the interior region of the host structure. The SCI may include, but not limited to, the time-of-arrivals for Lamb wave modes, the spectrum energy for Lamb wave modes distributed on their time-frequency domain or peak amplitude of sensor signal. FIG. 12 is a flow chart illustrating exemplary procedures 1200 for identifying and determining the time-of-arrivals for Lamb wave modes in accordance with one embodiment of the present invention. In step 1202, the process module may load a set of sensor signal data from a signal database depository, such as computer 514, where each sensor signal data may be measured at one excitation frequency. Hereinafter, the excitation frequency refers to a frequency at which the actuators of the DNP system are activated to generate Lamb waves. The stored set-up information data for the network patch system may be checked and the network path numbers may be identified to check whether the appropriate actuator and sensor are assigned to the path of each network link. Then, in step 1204, the process module may detrend each of the loaded sensor signal data to remove a non-stationary signal component. Next, in step 1206, the electrical noise 914 due to the toneburst actuator signal 904 may be removed by applying a masking window 918 to the detrended each signal data. Subsequently, in step 1208, the short-time Fourier or wavelet transformation may be performed on the noise-removed signal data to obtain a time-frequency signal energy distribution about the center frequency bandwidth of excitation along the time axis.
As will be explained later, the trajectory of the S0, S0 � ref and A0 mode waves determined in step 1214 may be utilized for designing the moving envelope windows is of various time spans with respect to the mode waves. The ridge extraction method may provide an accurate determination of the arrival time of each mode wave so that the phase velocities and the arrival-time differences between these modes can be exactly computed instead of calling for a dispersion curve formula of the structure. It is noted that the scope of the invention is not limited to the use of wavelet transformation in the time-frequency interpretation method.
FIGS. 13A-B show a flow chart 1300 illustrating exemplary procedures for computing SCI values (or, equivalently, damage index values) in accordance with one embodiment of the present invention. To compute SCI values, the process module may use the sensor signal dataset measured at a set of excitation frequencies. In step 1302, the process module may load a plurality of sensor signal datasets, where each sensor signal dataset is measured at one excitation frequency, where each sensor signal of a dataset, such as the signal 912, may correspond to a network path of the DNP system. Then, in step 1304, one of the plurality of sensor signal datasets may be selected. Subsequently, a sensor signal may be selected from the selected sensor signal data set in step 1306. In step 1308, the selected sensor signal may be detrended by applying a moving-average filter and partitioned into the actuation part 914 and receiving part 916 by applying a masking window 918 (shown in FIG. 9). In step 1310, the sensor signal may be decomposed into the several sub-bandwidth wave packets 926, 928 and 930 by a wavelet decomposition filter that preferably uses the Daubechies wavelet filter coefficients. For sub-bandwidth wave packet decomposition, a dyadic filter is designed for the Daubechies wavelet filter coefficients to provide high and low decomposition, and high and low reconstruction filter. The decomposition filter decomposes the detrended signal into the wavelet coefficients for multiresolution levels. Next, in step 1312, the process module may synthesize new sub-bandwidth wave packets within the frequency range of interest, where the Lamb wave signal may contain the waves of S0, S0 � ref, and A0 modes in the frequency bandwidth. The frequency range in a synthesized signal may be determined using a ridge extraction method to cover the range of the sub-bandwidth variation of each wave signal such that the multi resolution levels selected in the reconstruction filter may correspond to the bandwidth of the synthesized signal containing the wave signals of S0, S0 � ref, and A0. The synthesized signal is then generated using the reconstruction filer and the wavelet coefficients in signal decomposition. Then, in step 1314, the process module may apply signal extracting windows (or equivalently, moving envelop windows) 920, 922 and 924 to the synthesized Lamb wave signal to extract the S0, S0 � ref and A0 mode waves 926, 928 and 930 as independent waveforms. Each of S0, S0 � ref and A0 mode waves 926, 928 and 930 may be fitted within an envelop of each wave mode. In step 1316, the process module may determine the maximum, center position and span width of each of the envelope windows 920, 922 and 924 in the time axis. Then, it may compute in step 1318 SCI for the selected sensor signal. In one embodiment, the SCI may be based on the change in the spectrum energy of each wave of the S0, S0 � ref and A0 modes. In this embodiment, the process module may determine the spectrum energy of each wave of the S0, S0 � ref and A0 modes. Next, the process module may calculate the summation of these spectrum energies of the S0, S0 � ref and A0 modes and determine the difference in the summed energies between the baseline and damaged conditions of the host structure. Consequently, the spectrum energy difference may be utilized as a SCI value of the selected sensor signal. In an alternative embodiment, the process module may choose the changes in the maximum and center positions of the envelope windows as the SCI values.
1 / N ∑ i = 1 N x i 2 , 3 , 4 p ( x i ) for the amplitude distribution p(xi). From these estimates of the amplitude distribution, the covariance δ, skewness factor η, and flatness factor κ of the DPDF may be used to determine, in step 1322, a normality constant α on each sensor signal. The normality factor may be defined in terms of the product of these factors with power weightings: α=δ3/2 η−2 κ3/4. In step 1324, the process module may check if all of the sensor signals contained in the selected sensor signal dataset have been considered. Upon negative answer to the decision step 1324, the process may proceed to the step 1306. Otherwise, the process may proceed to step 1326 in FIG. 13B.
FIGS. 14A shows a flow chart 1400 illustrating exemplary procedures for generating a tomographic image to identify the regions having changes in structural conditions or damages in accordance with one embodiment of the present invention. In step 1402, the process module may load the coordinate data for diagnostic patches and SCI values for the network paths defined by the diagnostic patches.
For any ith network path line, the bisection point of a network path may be calculated in step 1404 from the actuator and sensor coordinates of {xi act, yi act} and {xi sen, yi sen} as the half of the minimum distance of the path line, tangential to the surface of the structural geometry. Then, the SCI value of the ith network path may be designated to the bisection point of the ith network path. Next, the process module may calculate intersection points of the network paths in step 1406. The process module may calculate the slope of mi=(yi sen−yi act)/(xi sen−xi act), its inverse m i=1/mi, and the constants of Ci=yi act−mixi act and C i=xi act− m iyi act for the ith path line. Then, the process module may determine the coordinate {(Ck−Ci)/(mi−mk), (miCk−mkCi)/(mi−mk)} on the ith path line for all of the other kth path lines intersecting the ith path line, with the condition on the slope mi to meet (Ck+mkxk sen+yk sen)/( C k+ m kyk sen−xk sen)≦mi≦( C k+mkxk act−yk act)/(Ck+ m kyk act−xk act). In step 1408, the process module may calculate the product of SCI values of the ith and kth network paths to assign a new SCI on each of the intersection points. In the case of no intersection, the designated SCI may be the half of the SCI value of the ith path line and the intersection point may be the same as the bisection point. Thus, the SCI values considered as the z-axis data on the coordinate plane of the actuators and sensors in the network path lines may be assigned to all of the bisection and intersection points. In one embodiment, the SCI data of all the bisection and intersection points may be stored as eXtensible Markup Language(XML) formatted documents into a SCI database depository.
For any ith path line, the process module may set a z-axis Gaussian or generalized bell function in the plane normal to the path line direction such that the maximum at the center of the Gaussian function may be the SCI value of the path. In step 1410, this z-axis function may be used to create a 3-dimensional block on the network path coordinate plane, in the manner that the cross section of the Gaussian function may run in parallel to the path line from the beginning and the end of the path line. Actually, this 3-dimensional function of the ith path line may intersect by being overlapped with other 3-dimensional functions of any other kth path lines. The SCI values at the intersection area may be determined by the product of the intersecting Gaussian SCI functions on the network path coordinate plane. The width of this 3-dimensional function in the cross-section plane may be the shortest distance in all the path lines, which is multiplied by the SCI value ratio of the ith path to the shortest distance path line. The process module may continue to compute the SCI values on the network plane until all the network paths are considered. In step 1412, the process module may interpolate the SCI dataset for each of the bisection, the intersection and the 3-dim Gaussian-function overlapping points over the mesh-grid points, made by dividing the entire region of the structure into small mesh elements. In this interpolation, the process module may employ the Delaunay triangulation of the convex-hull set for the grid data of SCI values.
The SCI distribution on the mesh-grid points corresponding to the final chromosomes may represent the degree of changes in the structural condition of the host structure. The regions of area where the structural condition changes or damages may occur in the host structure can be exactly identified from this refined SCI distribution. In step 1426, for the structural condition or damage identification of the host structure, the process module can provide a genetic-based tomography image using the interpolated SCI distribution. Also, by repeating the steps 1402-1426 at a set of excitation frequencies, a set of tomographic images may be is obtained.
FIG. 14B is a flow chart 1430 illustrating exemplary procedures for generating a tomographic image to identify regions having changes in structural conditions or damages in accordance with another embodiment of the present invention. In step 1432, the process module may load a time-of-arrival dataset of a Lamb wave mode, such as So mode. As mentioned, the time-of-arrival for a Lamb wave mode can be used as a SCI. Using the extracted ridge curves in step 1212, the process module may exactly determine for all the network paths the time-of-arrival differences between the Lamb wave modes. Next, in step 1434, a conventional algebraic reconstruction technique may be applied to the loaded time-of-arrival dataset for the global inspection of damage on the host structure. Then, based on the reconstructed time-of-arrival data, a tomography of the entire region of the host structure may be generated in step 1436. In one embodiment, the steps 1432-1436 may be repeated to generate a set of tomographic images of the entire region, where each tomographic image may be based on a time-of-arrival dataset measured at a different excitation frequency. By stacking the set of tomographic images, a hyperspectral tomography cube of the entire region may be obtained.
FIG. 14D shows a hyperspectral tomography cube 1460 in accordance with one embodiment of the present invention. As illustrates in FIG. 14D, the hyper spectral tomography cube 1460 comprises layers of two-dimensional tomographic images 1462, 1464 and 1466, where each image may be generated at an excitation frequency and the z-axis may represent the excitation frequency. For simplicity, only three layers 1462,1464 and 1466 are shown in FIG. 14D. However, it should be apparent to those of ordinary skill that the hyperspectral tomography cube 1460 may comprise image layers generated at continuous excitation frequency range.
FIG. 14E shows a 3-dimensional damage evolution manifold 1470 illustrating the variation of structural condition in accordance with one embodiment of the present invention. Like the hyperspectral tomography cube 1460, the manifold 1470 may comprise two-dimensional tomographic images stacked in z-direction, wherein each image is generated after a number of vibrational repetition cycles corresponding to the z-value has been applied to the host structure. Also, in each tomographic image, only a portion that shows structural changes has been displayed. Thus, each of slices on the 3-dim damage-evolution manifold 1470 may represent the evolution state of structural condition or damage in a structure.
As mentioned in the step 1410 of FIG. 14A, the process module may determine SCI values near the intersection points of network paths. A classification module that includes a neuro-fuzzy inference system may also determine the SCI values at the intersection points. FIG. 15 is a schematic diagram 1500 illustrating procedures of a neuro-fuzzy inference system for providing structured system condition index (SCI) distribution at the intersection points of network paths in accordance with one embodiment of the present invention. As explained in step 1408, each of the intersection points in the network paths has two crossing path lines with their SCI values and distances. To obtain structured SCI values at the intersection points, the distance of two crossing path lines may be exploited by a fuzzy if-then rule system collaborated with a neural network. Then, this expert system may generate the output of SCI values of the intersecting paths.
For any of the n intersection points P1-Pn 1502, each of two crossing path line distances 1504 can be input into three fuzzy membership functions 1506, (A1/B1, A2/B2, A3/B3), in the terms of �short�, �medium�, �long� distance. For the membership function, generalized bell functions of μA i /B i =1/[1+|(x−ci)/αi|2b], i=1,2,3, may be used with the adjustment parameters of (a, c) to cover each input region of the path line distance normalized to a structure dimension. In layer 1508, every node may be a fixed node labeled Π and generate an output vi k that may be the product of the incoming signals of Ai,Bi: vi k=μAi (xk)μBi (xk), k=1, . . . , n. Each node output may represent the firing strength of a rule. Any ith node of layer 1510, labeled N, may calculate the ratio wi k of the ith rule's firing strength to the sum of all rules' firing strengths: wi k=vi k/(v1 k+v2 k+v3 k), i=1,2,3 so that the output wi k of the layer 1510 may be a normalized firing strength. Moreover, SCI values of step 1408 at intersecting paths in layer 1512 may be inputted into a multilayer perception or neural network. In layer 1514, each node may be adapted with a node function of, ci k=fi k(s1 k, s2 k), i=1,2,3 where ci k is the consequent parts in a network-layered representation which can be compared with a simple backpropagation multilayer perception with the input layer 1512 of SCI values Si k. Here, fi k (s1 k, s2 k) require two SCI values of the intersecting path lines as input. If all three neurons 1514 and one neuron 1516 have identity functions in FIG. 15A, the presented neuro-fuzzy is equivalent to Sugeno (TSK) fuzzy inference system, which accomplishes linear fuzzy if-then rules. Adjusting the relevant connection strengths or weighting factors on the neural network link according to the error distance may initiate the adaptation in the neural network. In one embodiment, a sigmoidal function may be used as the neuron function in the consequent layer 1514. In another embodiment, the neural network layer can use a back propagation multilayer perception and radial basis function networks. In layer 1516 as an output of the consequent layer 1514, the node may compute the summation of all incoming signals like yk=Σiwi kci k/Σiwi k and generate output 1518 that may comprise the SCI values at intersection points.
FIG. 15B is a schematic diagram 1519 illustrating exemplary procedures of a cooperative hybrid expert system for simulating SCI distribution on the mesh-grid (or, equivalently, lattice grid) points of a structure from SCI distribution on the intersection points in accordance with one embodiment of the present invention. For artificial damage such as attachment of the various-sized rubber patches on a structure with the prior-known information on the location and extent of the damage, the classification module can generate output 1528 that may be the first SCI chromosomes sprior j the grid points following the steps 1418-1426. If the input 1518 to this corporative hybrid expert system is the SCI distribution on the intersection points given with the coordinates of the rubber patches and their sizes, the final output 1540 of this cooperative hybrid expert system may be the SCI distribution for �hot-spot� regions for the various sized rubber patches by using an adapted SCI chromosome set 1524, which is derived form the steps 1534,1536 and 1538. Moreover, the neuro-fuzzy inference system as shown in FIG. 15A may be applied again to the intersection points and their SCI values 1518 for the artificial damage, and adapted SCI chromosomes sadapt j 1524 may be obtained by the use of steps 1418-1426 from output yadapt k of the neuro-fuzzy inference system_shown in FIG. 15A. In step 1534, the difference between the two chromosome set may be calculated to give a root mean square norm E: E=√{square root over ((sprior j−sadapt j)2)}, j=1, . . . , n�m, where n�m is the dimension of the grid points. The fitness value of each chromosome is determined in step 1536 according to the calculated difference: fitness=exp(−E). Then, the genetic operation in step 1538 may be performed for the crossover and mutation of chromosomes, where the operation scheme in this module may use genetic algorithms in the art. Then, the classification module may provide the SCI chromosome distribution 1524 on the grid points, best fitted to the artificial damage. With these SCI chromosomes, an unsupervised neural network can be trained in step 1526 to achieve the clustering or classification on the SCI distribution set on the grid points. However, the classification module can repeat to adapt the hybrid expert system while the process module process to renew the SCI distributions for each excitation frequency.
The classification module may continue to classify the damage types (or, equivalently, �hot-spot� regions) from the SCI distribution 1540 on the grid points.
FIG. 16A is a schematic diagram 1600 illustrating Gabor jets applied to a �hot-spot� region in accordance with one embodiment of the present invention. As illustrated in FIG. 16, a �hot-spot� region 1610 may be recognized and segmented from the background SCI distribution 1602 on the grid points. In general, the shape and location of the hot-spot region 1610 may vary according to the excitation frequency and the number of network paths. Also, the diversity in physical characteristic and geometry of structures monitored may increase the difficulty level in classifying the damages. In one embodiment of the present invention, the classification module may employ a multilayer perception (MLP) or feedforward neural network to classify the damage of �hot-spot� region 1610 in a structure. The classification module may use Gabor wavelet features 1606 to combine those features into a MLP as will be explained later. The Gabor wavelet features 1606 may be obtained from the Gabor wavelet transformation of the SCI distribution with different orientations 1608 and multiresolution scales 1604. The Gabor wavelet function may be defined as G(x,y)=exp {−π[(x−x0)2α2+(y−y0)2β2]+2 πj[u0(x−x0)+v0(y−y0)]}, where j=√{square root over (−1)}, (x0, y0) are position parameters to localize the wavelet to a selected region, (u0,v0) are modulation parameters to orient the wavelet in a preferred direction, and (α, β) are scaled parameters. With a set of coefficient called �Gabor jet�, the classification module may compute the Gabor project for multiple orientations and resolutions at a given �hot-spot� region 1610. Each Gabor jet may contain a number of coefficients corresponding to the number of orientations and the resolution levels such that it consists of logons of orientations and different scales. The classification module can capture local SCI-distribution structure of each of the �hot-spot� regions by computing a set of Gabor jets at several points of the region to get the input feature.
FIG. 16B is a schematic diagram 1620 illustrating multilayer perception (MLP) for classifying the type of damage in accordance with one embodiment of the present invention. As illustrated in FIG. 16B, the MLP 1624 may include three layers: an input feature layer 1628 for receiving Gabor jets; a hidden layer 1630; and a output classification layer 1632 for determining the types of damages in hot-spots 1610. A number of neurons in the output classification layer 1632 can be the nodes representing the structural condition types.
FIG. 16C is a schematic diagram 1640 illustrating the fully connected network classifier for classifying a structural condition in accordance with one embodiment of the present invention. As illustrated in FIG. 16C, a set of Gabor jets 1642 may be generated using a SCI distribution 1643 that may contain 3 hot-spot regions 1641. A MLP 1644 may be similar to the MPL 1624 and classify the types of damages in hot-spot regions 1641 into one of the categories C0-C5 1646. For simplicity, only three hot-spots regions 1641 and six categories are shown in FIG. 16C. However, it should be apparent to those of ordinary skill that the present invention may be practiced with any number of hot-spot regions and categories.
FIG. 16D is a schematic diagram 1650 illustrating modular network classifiers for classifying structural conditions in accordance with one embodiment of the present invention. As illustrated in FIG. 16D, a set of Gabor jets 1652 for each hot-spot region 1641 of the SCI distribution 1643 may be generated. Each MLP 1654 may be similar to the MPL 1624 and classify the type of damage in each hot-spot region 1641. Then, a nonlinear transformation and mixing process 1656 may be applied to the results from the MLP 1654 prior to the classification of the damages. The structural condition may be trained with the different condition or damage of structures so that the highest value in the output nodes may be taken to be one of the structural condition types.
For each type of the structural condition or damage, the diagnosis classification module may setup reference templates as a �codebook� in accordance with one embodiment of the present invention. The codebook for each type of damage may be the data set of cluster points of the different versions of SCI distribution or of wavelet transformation coefficients of the SCI distribution, explained later in FIG. 17B. Each template or SCI distribution for the �hot-spot� region may be clustered by a K-mean and learning vector quantization (LVQ) clustering algorithm. The K-mean algorithm may partition a collection of n vector into c groups Gi, i=1, . . . , c and finds a cluster center in each group such that a cost function of dissimilarity measure may be minimized. This algorithm may use an unsupervised learning data clustering method to locate several clusters without using the class information. Once the K-mean algorithm determines the clusters of SCI distribution of the �hot-spot� region on the grid points, the clustered data may be labeled before moving to the second step of a supervised learning to locate several cluster centers. During the supervised learning, the cluster centers may be fine-tuned to approximate a desired decision hypersurface. The learning method may be straightforward. First, the cluster center c that is closest to the input vector x must be found. Then, if x and c belong to the same class, c is moved toward x; otherwise c is moved form the input vectorx. This LVQ algorithm can classify an input vector by assigning it to the same class as the output unit that has the weight vector closest to the input vector. Thus, the LVQ network may use the class information of SCI values to fine-tune the cluster centers to minimize the number of misclassified cases.
FIG. 17A is a flow chart 1700 illustrating exemplary procedures of a K-mean/LVQ algorithm for developing a clustered �codebook� in accordance with one embodiment of the present invention. The classification module may begin the first K-mean clustering process, as an unsupervised learning data clustering method, with step 1702 where the cluster centers Ci, i=1, . . . , c may be initialized by randomly selecting c points from the SCI data on the �hot-spot� regions. In step 1704, the classification module may determine the membership matrix S by the equation: Sik=1 if ∥xk−ci∥≦∥xk−ci∥; 0 otherwise, where the binary membership matrix S may define the c partition groups of Gi, i=1, . . . , c, and x is a randomly selected input vector. Then, the classification module may compute in step 1706 the cost function of
L = ∑ i = 1 c L i and L i = ∑ x k ∈ G i  x k - c i  2 where the Euclidean distance may be chosen as the dissimilarity measure between the SCI vector xk and the corresponding cluster center ci. Next, in step 1708, the cluster centers may be updated according to the equation
c i = 1 /  G i  ∑ x k ∈ G i x k and go to decision step 1710 to check if either the cost is below a certain tolerance value. If answer to the step 1710 is YES, the process proceeds to the step 1714. Otherwise, it may proceed to another decision step 1712 to determine if the newly calculated cost is smaller than the previous one. If answer to the step 1712 is NO, the process proceeds to the step 1714. Otherwise, it may proceed to step 1704. Next, the classification module may begin the second LVQ clustering process to fine-tune the cluster centers in step 1714 to minimize the number of misclassified cases. Here, the clusters obtained from the steps 1702-1708 may be labeled by a voting method (i.e., a cluster is labeled class i if it has data points belong to class i as a majority within the cluster.) In step 1716, the classification module may randomly select a training input vector x and find i such that ∥xk−ci∥ is a minimum. Next, in step 1718, the classification module may update ci by □ci=γ(xk−ci) if xk and ci belong to the same class; otherwise by □ci=−γ(xk−ci), where y is a learning rate and a positive small constant that may decrease with each of iterations. In step 1720, the classification module can generate a codebook that may include the SCI cluster center of the SCI distribution of the �hot-spot� regions on the grid points.
FIG. 17B is a schematic diagram 1730 illustrating exemplary procedures of a classification module to build a damage classifier using a codebook generated by the steps in FIG. 17A in accordance with one embodiment of the present invention. The damages may be located in a �hot-spot� region on the grid points of the diagnostic network paths. The SCI distribution 1734 of �hot-spot� regions for each structural condition may be used to design the codevector for structural conditions or damages, where each type of damage may belong to one of the types 1732. Each SCI distribution 1734 may be obtained at an actuation frequency. For the network signals measured at a different excitation frequency, another block template 1738 can be also attained from the collection 1734 on the SCI distributions of the �hot-spot� regions. The codevector may be given by the set of the cluster centers of the block template of the SCI distribution of the �hot-spot� regions. Then, the classification codebook 1738 comprising a set of the optimized block templates according to each of the structural condition or damage references may be obtained by differentiating actuation frequency. In order to establish the codebook-based classifiers considering the actuation frequency, a frequency multilayer perception 1740 must be given in the codevectors of the codebook 1738 corresponding to the set of actuation frequencies. The output from the frequency multilayer perception 1740 may be input into a neural network input layer 1741. Then, using the output from the neural network input layer 1741, other multilayer perception 1742 may also classify the structural condition or damage 1744 to combine the outputs of the frequency multilayer perception. In one embodiment of the present invention, the coefficients of Fourier and wavelet transformation of these SCI values instead of the SCI values of the �hot-spot� regions can be utilized as the input of the K-mean algorithm in FIG. 17A. In another embodiment of the present invention, the principal component analysis, incorporated with Fisher linear discriminant analysis or eigenspace separation transformation, can be used in the PCA-based LVQ clustering method for the SCI distributions or wavelet-transformed SCI distributions to provide different codebooks with high sensitivity to damage types.
A structure suffers aging, damage, wear and degradation in terms of its operation/service capabilities and reliability. So, it needs a holistic view that the structural life has different stages starting with the elaboration of need right up to the phase-out. Given a network patch system, the current wave transmission of the network patch system may obey different time scales during the damage evolution to query the structure of its time-variant structural properties. FIG. 18A illustrates a schematic diagram 1800 of three evolution domains of a structure in operation/service, dynamics of the network patch system, and network system matrix in accordance with one embodiment of the present invention. In illustrated in FIG. 18A, a slow-time coordinate τ designating the structure damage evolution is introduced, and, in addition, the fast-time coordinate n describing the current network dynamics for the wave transmission is introduced.
In the fast timeframe nested in the long-term lifetime, the dynamic system of the diagnostic network patch system, as a black-box model to be identified from the input actuation and output sensing signals, can be described by an autoregressive moving average with exogenous inputs (ARMAX) or state space model. Rather than using the ARMAX model possibly incorporated in a fault diagnostic system to query the functionality of built-in system components, the state-space dynamics models of the network patch system at a fixed lifetime r can be used. The state-space dynamic model, considered in non-distributed domain for the brevity of explanation, may be represented by xτ(n+1)=Aτxτ(n)+Bτf(n), where the state vector xτ(n) is the wave-transmission state vector of the network system and f(n) is the input force vector of the actuators in the network patches. Aτ, Bτ are the system matrix and the input matrix, respectively. The excitation force for generating Lamb wave in all network paths is assumed to be unchanged during the lifetime of τe. The measurement equation of the network sensors is written as yτ(n)=Cτxτ(n) where yτ(n) is the sensor signal vector and Cτ is the system observation matrix. The system matrix Στ(=[Aτ, Bτ, Cτ]) of the diagnostic network patch system can be considered independent of the fast time coordinate.
To model the network dynamics of the diagnostic patch system, the prognosis module may compute the system matrix Στ(=[Aτ, Bτ, Cτ]) by using a subspace system identification method that reconstructs the dynamic system from the measured actuator/sensor signals in the network patches. The procedures disclosed by Kim et al., �Estimation of normal mode and other system parameters of composite laminated plates,� Composite Structures, 2001 and by Kim et al., �Structural dynamic system reconstruction method for vibrating structures, Transaction of DSMC, ASME, 2003, which are incorporated herein in its entirety by reference thereto, can be employed to establish the reconstructed dynamic system model using the multiple inputs and outputs of the present sensory network system.
To determine the near future structural condition in damage evolution domain, the prognosis module may employ the current trend of the system matrix as the damage/impact related temporal symptom of a host structure. If the temporal symptom shows sign of deterioration, as exemplified by the change of damage/impact related symptom increasing with time τ, the prognostic module will predict the behavior of the �hot-spot� regions with respect to the remaining life span of a structure and trigger an early warning. Consequently, the future trend of the system matrix Στ produced by the network dynamics of Lamb-wave transmission on the structure makes it possible to forecast the structure damage/impact conditions. To estimate the future system matrix Στ, the prognosis module preferably utilizes a training method of recurrent neural network (RNN) with the previous dynamic reconstruction models determined from the simulated sensor signals, because of its highly nonlinear characteristics of the SCI vector I(τ). In an alternative embodiment, the feed-forward neural network (FFN) can be used. The curves 1802 and 1810 may represent the evolution of the SCI vector I(τ) and the matrix Στ, respectively, and span up to the time of structural death τe 1804. Sensor signals 1808 may be measured to access the structural conditions at time τv 1806.
FIG. 18B schematically illustrates the architecture of a recurrent neural network 1830 for forecasting the future system matrix in accordance with one embodiment of the present invention. As shown in FIG. 18B, the architecture of the RNN 1830 may have four input nodes 1836 and additional feedback-path node 1838, four hidden nodes 1834 and one output node 1832. The input data set may be a set of the elements of discrete time-delayed system matrix series. The output layer may consist of one neuron 1832 corresponding to the system matrix elements that are being predicted at the first time step in the future. In the RNN 1830, the current activation state of the output is a function of the previous activation states as well as the current inputs. At timerτ,the output node (output signal atτ+1) may be calculated by the activation of hidden nodes 1834 at the previous time steps τ, τ−1, τ−2, . . . , τ−n etc. Therefore, each training pattern will contain the current Στ, the previous three time lagged values {Στ−3, Στ−2, Στ−1}, and an extra input from additional feedback loop 1840, and the output {circumflex over (Σ)}τ+1 is one step ahead predicted value. This network can provide the estimated value of the next future system matrix based on the current and previous system matrix values. A sigmoid function of 1/(1+e−x) may be used as the activation functions of the nodes contained in the hidden and output layers. The nodes should operate in the ranges of the activation functions, and all the element data in the system matrix in activation may be scaled to the interval [−0.5 0.5]. The level of the RNN's learning may be determined by a prediction error between the actual outputs from the network and the target outputs corresponding to an input data set. The error may be utilized in adjusting the weights until the actual outputs are consistent with the target values. The RNN in prognosis module may complete the learning process when the number of training iterations has reached a prescribed number and the error can be judged acceptably small.
By the use of the state-space model of the future system matrix {circumflex over (Σ)}τ+1, the prognosis module may develop the prognostic sensor signals for the �hot-spot� regions of the structure from the inputs of the same actuator signals. Now, the identification and classification methods, as explained in FIGS. 9-1 8B, can apply to the prognostic sensor signals to compute the one-step ahead SCI vector I(τ+1). Finally, the prognosis module can display the prognostic tomography image and store it into a prognosis tomography database depository.
As mentioned, the monitoring software may comprise interrogation, processing, classification and prognosis modules. These application modules may use eXtensible Markup Language (XML) to save their processed data and/or images to a structured-query-language(SQL) based database and retrieve the reference and system data for device locations, network paths and parameters of structural condition monitoring system. Each XML-formatted document may be described by its data and tags created by the structural monitoring system. Also, each module can parse the XML document to read data that may be input to other application modules. Tags in XML documents may consist of root element in the outmost node and child elements in the nested nodes and may have attributes that appear as name/value pairs following the name of the tag.
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