WO2005031502B1 - Methods for monitoring structural health conditions - Google Patents

Methods for monitoring structural health conditions

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Publication number
WO2005031502B1
WO2005031502B1 PCT/US2004/030268 US2004030268W WO2005031502B1 WO 2005031502 B1 WO2005031502 B1 WO 2005031502B1 US 2004030268 W US2004030268 W US 2004030268W WO 2005031502 B1 WO2005031502 B1 WO 2005031502B1
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WO
WIPO (PCT)
Prior art keywords
new
sci
signal
network
readable medium
Prior art date
Application number
PCT/US2004/030268
Other languages
French (fr)
Other versions
WO2005031502A2 (en
WO2005031502A3 (en
Inventor
Kim Hyeung-Yun
Original Assignee
Kim Hyeung-Yun
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Filing date
Publication date
Application filed by Kim Hyeung-Yun filed Critical Kim Hyeung-Yun
Priority to EP04788775A priority Critical patent/EP1685457A2/en
Priority to JP2006527011A priority patent/JP2007511741A/en
Priority to AU2004277167A priority patent/AU2004277167A1/en
Publication of WO2005031502A2 publication Critical patent/WO2005031502A2/en
Publication of WO2005031502A3 publication Critical patent/WO2005031502A3/en
Publication of WO2005031502B1 publication Critical patent/WO2005031502B1/en

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Classifications

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    • G01H11/06Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties by electric means
    • G01H11/08Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties by electric means using piezoelectric devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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    • G01N29/22Details, e.g. general constructional or apparatus details
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    • G01N29/22Details, e.g. general constructional or apparatus details
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    • G01N29/22Details, e.g. general constructional or apparatus details
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    • G01N29/22Details, e.g. general constructional or apparatus details
    • G01N29/28Details, e.g. general constructional or apparatus details providing acoustic coupling, e.g. water
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4409Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
    • G01N29/4418Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with a model, e.g. best-fit, regression analysis
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    • G01N29/44Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4463Signal correction, e.g. distance amplitude correction [DAC], distance gain size [DGS], noise filtering
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88Sonar systems specially adapted for specific applications
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    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00Indexing codes associated with group G01N29/00
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    • G01N2291/015Attenuation, scattering
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    • G01N2291/024Mixtures
    • G01N2291/02491Materials with nonlinear acoustic properties
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    • G01N2291/10Number of transducers
    • G01N2291/106Number of transducers one or more transducer arrays

Abstract

The present invention provides methods for interrogating a damage of a host structure using a diagnostic network patch (DNP) system having patches. An interrogation module partitions the plurality of patched in subgroups and measures the sensor signals generated and received by actuator and sensor patches, respectively. Then, a process module loads sensor signal data to identify Lamb wave modes, determine the time of arrival of the modes and generate a tornographic image. It also determines distribution of other structural condition indices to generate tomographic images of the host structure. A set of tomographic images can be stacked to generate a hyperspectral tomography cube. A classification module generates codebook based on K-mean/Leaming Vector Quantization algorithm and uses a neural-ftizzy-inference system to determine the type of damages of the host structure.

Claims

60
AMENDED CLAIMS received by the International Bureau on 19 February 2007
This listing of claims will replace all prior versions, and listings, of claims in the application:
LISTING OF CLAIMS:
1-41. (canceled)
42. (New) A computer-implemented method for interrogating health conditions of a structure by using a plurality of diagnostic network patches (DNP) implemented thereto, each of the patches being able to operate as at least one of a transmitter patch and a sensor patch, the method comprising: forming a diagnostic network including the patches and a plurality of signal transmission paths, each said signal transmission path being a signal link between a transmitter patch and a sensor patch; representing the diagnostic network by a graph, wherein the patches and signal transmission paths are respectively abstracted as nodes and edges in the graph; partitioning the diagnostic network into one or more subgroups, each of the subgroups including a designated transmitter patch and one or more sensor patches; causing, by use of a computer processor, the designated transmitter patch to transmit a signal and the sensor patches to receive the signal; and comparing the received signal with a baseline signal to determine a deviation therebetween, the baseline signal being measured by use of the diagnostic network in absence of structural anomaly; and analyzing the deviation to determine the health conditions of the structure.
43. (New) The method of claim 42, further comprising: storing the received signal and the deviation in a depository.
44. (New) The method of claim 42, wherein the step of analyzing includes: performing a diagnostic data processing; 61 generating a structural condition index (SCI); and generating a tomographic image.
45. (New) The method of claim 42, wherein the anomaly includes at least one selected from the group consisting of damage, impact, cavity, corrosion, local change of internal temperature and pressure, degradation of material, and a repair bonding patch applied to the structure.
46. (New) The method of claim 42, wherein two neighboring ones of the subgroups share one or more patches.
47. (New) The method claim 42, wherein the step of representing the diagnostic network by a graph includes: generating a topological architecture, x, of the diagnostic network, wherein x is represented by the equation:
X= {X12, X13, , XrH . n} and wherein xy e {0,1} is a decision variable representing the path between ith and jth nodes and n is the number of the nodes.
48. (New) The method of claim 42, wherein the step of representing the diagnostic network by a graph includes: performing a common integer programming formulation to generate a matrix of path connection, each element (i,k) of the matrix being 1 if ith sensor patch is connected to kth transmitter patch and 0 otherwise.
49. (New) The method of claim 42, further comprising: optimizing the diagnostic network by use of a cost variable associated with network path uniformity so that network performance is maximized while the number of patches is minimized.
50. (New) The method of claim 49, wherein the cost variable is the distance of signal transmission, the number of intersection points of each said signal 62 transmission path crossed by neighboring paths or a sensitivity of each said signal transmission path to an excitation frequency of the signal.
51. (New) The method of claim 42, further comprising, prior to the step of forming a diagnostic network: applying one or more artificial defects to the structure to simulate damages in the structure, wherein the step of forming a diagnostic network is performed by a genetic algorithm to optimize the diagnostic network.
52. (New) The method of claim 42, wherein the step of causing the designated transmitter patch to transmit a signal and the sensor patches to receive the signal includes: providing a toneburst signal for the designated transmitter patch, the toneburst signal having a spectral energy distribution within a narrow frequency bandwidth centered at one excitation frequency.
53. (New) The method of claim 42, further comprising: repeating the steps of forming a diagnostic network to comparing the received signal at a plurality of excitation frequencies of the signal.
54. (New) The method of claim 42, wherein the signal is a Lamb wave signal or a vibrational signal.
55. (New) The method of claim 42, further comprising: storing coordinates of patches and setup information including an excitation frequency of the signal, types of patches, identification numbers of patches, a voltage level of patches, and operational status of patches.
56. (New) The method of claim 42, further comprising: converting the baseline signal and received signal into extensible Markup Language (XML) formatted data; and parsing XML formatted data to read the signals. 63
57. (New) The method of claim 44, wherein the step of performing a diagnostic data processing includes: filtering out interference signal in the signal received at a sensor patch; detrending the signal to remove a nonstationary signal component; building a envelope window from the received signal; breaking the envelope window in sub envelope windows; and applying sub envelope windows to extract wave packets from the received signal.
58. (New) The method of claim 57, wherein the wave packets correspond to Lamb-wave modes including fundamental symmetric mode So and its reflected mode Sref_o> fundamental antisymmetric mode Ao, and basic shear mode SH.
59. (New) The method of claim 57, wherein the step of building a envelope window from the received signal includes: utilizing a hill-climbing algorithm to construct the envelope window.
60. (New) The method of claim 57, further comprising: obtaining a time-frequency energy distribution of the signal by use of a transformation, wherein the transformation is a short-time Fourier transformation or a wavelet transformation; and identifying the time of arrival of each mode wave in the signal by use of the time-frequency energy distribution.
61. (New) The method of claim 60, further comprising: utilizing a dispersion curve formula for Lamb wave to identify the time of arrival of each mode wave.
62. (New) The method of claim 60, further comprising: obtaining a multi-bandwidth energy distribution of signals for a plurality of excitation frequencies; extracting ridge curves from the multi-bandwidth energy distribution, the ridge curves representing the trajectory of each wave mode; and 64 identifying the time of arrival by use of the ridge curves.
63. (New) The method of claim 57, further comprising: decomposing the signal into several sub-bandwidth signals by use of a wavelet decomposition filter; synthesizing a plurality of sub-bandwidth signals to forming a synthesized signal containing the frequency bandwidth of interest; repeating the steps from building an envelope window to applying sub envelope windows to extract wave packets from the synthesized signal; and determining the spectrum energy of each wave packet by use of the envelope window.
64. (New) The method of claim 44, wherein the SCI is the time of arrival of each mode wave or the time-of-arrival change of the signal compared to a baseline signal.
65. (New) The method of claim 44, wherein the SCI is the spectrum energy of each wave mode or the change of the spectrum energy of the signal compared to a baseline signal.
66. (New) The method of claim 44, wherein the SCI is the summing of the spectrum energy for a plurality of wave modes or the change of the summing of the spectrum energy of the signal compared to a baseline signal.
67. (New) The method of claim 44, wherein the SCI is a parameter of a envelop window associated with each wave mode or the change of the parameter of the envelop window of the signal compared to a baseline signal, whereby the parameter is the maximum, center position or span width of the envelope window.
68. (New) The method of claim 44, wherein the step of generating a structural condition index (SGI) includes: removing an abnormal signal to checking the SCI; deleting an outlier SCI from SCI dataset; and compensating the SCI for temperature change in the measurement of the received signal compared to a baseline signal.
69. (New) The method of claim 68, wherein the step of removing an abnormal signal includes: determining a discrete probability density function (DPDF) of the amplitude of the received signal; calculating the covariance, skewness factor and flatness factor of the DPDF; and checking a normality constant of the signal to removing the abnormal signal.
70. (New) The method of claim 68, wherein the step of deleting an outlier SCI from SCI dataset includes: computing a probability density function (PDF) of a SCI dataset; finding the outlier in the SCI dataset in the PDF; and deleting the outlier by use of a normality constant of the SCI dataset.
71. (New) The method of claim 68, wherein the step of compensating the SCI value for temperature change in the measurement of the received signal compared to a baseline signal includes: checking the temperature change therebetween; building a temperature reference table; calculating a temperature-adjustment parameter from the table; and scaling the SCI value by the parameter to compensate the SCI value.
72. (New) The method of claim 44, wherein the step of generating a structural condition index (SCI) includes: computing a structural dynamic parameter from vibrational signals, whereby the vibration signals are measured by a traditional sensor patch such as accelerometer, displacement transducer or strain gage; and utilizing the structural dynamic parameter as a SCI value, wherein the SCi value is one or more of natural frequencies, damping ratios or mode shapes for a plurality of sensor locations. 66
73. (New) The method of claim 44, wherein the step of performing a diagnostic data processing includes: designating a plurality of points on a network plane, wherein said network plane containing the lines of transmission paths and the coordinates of patches; allocating a SCI value to each said designated point; generating mesh-grid points in the network plane, the network plane being divided into mesh elements; distributing the SCI values of the designated points on the mesh-grid points by use of an interpolation method; and whereby, for the case of signal measurement at a sensor patch not linked with transmission paths to any transmitter patches, each said designated point is the same point of the location of the sensor patch.
74. (New) The method of claim 73, further comprising: refining the SCI values of the mesh-grid points.
75. (New) The method of claim 73, further comprising: generating a tomographic image of the SCI values of the mesh-grid points with a color range; storing the tomographic image and the color range in a depository; and wherein the color range is adjusted to have a clear look at 'hot-spot' zones.
76. (New) The method of claim 73, wherein the tomographic image has marks and lines to show the location of patches and transmission paths respectively.
77. (New) The method of claim 73, wherein said network plane is a surface of structure containing the network of patches.
78. (New) The method of claim 73, wherein the step of generating mesh-grid points on the network plane includes: employing the Delaunay triangulation to distribute the SCI values of the designated points on the grid points of the triangulated mesh elements. 67
79. (New) The method of claim 73, wherein the step of designating a plurality of points on a network plane includes: determining the bisection points of transmission paths; and computing the intersection points of transmission paths.
80. (New) The method of claim 73, wherein the step of allocating a SCI value to each said designated point includes: providing the bisection point with the SCI value of transmission path; and providing the intersection point with the product of the SCI values of two transmission paths intersecting each other.
81. (New) The method of claim 80, further comprising: providing the points of a cross sectional area with SCI values by use of three- dimensional Gaussian functions.
82. (New) The method of claim 74, wherein the step of refining the SCI values of the mesh-grid points includes: utilizing a genetic algorithm to distributing the refined SCI values on the mesh- grid points.
83. (New) The method of claim 73, further comprising: applying an algebraic reconstruction technique (ART) to the time-of-arrival SCI values.
84. (New) The method of claim 73, further comprising: applying a simultaneous iterative reconstruction technique (SIRT) to the time- of-arrival SCI values.
85. (New) The method of claim 73, further comprising: applying a scatter-operator-eigenfunctlon based technique to the time-of- arrival SCI values. 68
86. (New) The method of claim 75, further comprising: repeating the steps of generating a network tomographic image at a plurality of excitation frequencies; and stacking each network tomographic image to generate a hyperspectral cube.
87. (New) The method of claim 75, further comprising: repeating the steps of generating a network tomographic image at a plurality of consecutive temporal points of damage state; and stacking each network tomographic image to generate a damage evolution manifold, wherein said damage evolution manifold represents the evolved state of structural condition.
88. (New) The method of claim 73, wherein the step of allocating a SCI value to each designated point includes: employing an expert system to determine the structured SCI distribution of the designated points of transmission paths.
89. (New) The method of claim 88, wherein the expert system is a neuro- fuzzy inference system consisting of a fuzzy if-then rule system for the distance of each transmission path, collaborated with a neural network.
90. (New) The method of claim 89, wherein said neural network is a back propagation multiplayer perception with radial basis function networks.
91. (New) The method of claim 74, wherein the step of refining the SCI values of the mesh-grid points includes: utilizing a cooperative hybrid expert system to simulate SCI distribution of the mesh-grid points for 'hot-spot' regions of damage, the expert system being trained with prior-known artificial damage.
92= (New) The method of claim 91 , wherein the step of utilizing a cooperative hybrid expert system includes: 69 applying a genetic algorithm to the SCI distribution of the designated points for input damage, the genetic algorithm providing a SCI chromosome set of input; adapting the SCI chromosome set to that of prior-known damage; employing an unsupervised neural network for the adapted SCI chromosome set; and training the unsupervised neural network to clustering the SCI distribution of the mesh-grid points for 'hot-spot' regions of damage.
93. (New) The method of claim 92, further comprising: repeating the steps from applying a genetic algorithm to training the neural network for a plurality of excitation frequencies.
94. (New) The method of claim 73, further comprising: converting the SCI data of designated and mesh-grid points into extensible Markup Language (XML) formatted documents; and parsing XML formatted documents to read the SCI data.
95. (New) The method of claim 44, further comprising: building a damage classifier of multilayer perception, wherein the multilayer perception is a feedforward neural network; and training the damage classifier to classify damage type.
96. (New) The method of claim 95, wherein the damage classifier is a fully connected network classifier covering multiple inputs and multiple outputs for 'hot- spot' regions and damage categories respectively.
97. (New) The method of claim 95, wherein the damage classifier is a modular network classifier applying nonlinear transformation and mixing process on the outputs of multiple multilayer perceptions.
98. (Mew) The method of ds'Φ. 95, -.vhsrsin the step of building a damage classifier includes: 70
producing a set of wavelet features to segmenting 'hot-spot' regions from background SCI distribution; and employing multilayer perception layers to recognizing the damage type on each 'hot-spot' region.
99. (New) The method of claim 44, further comprising: implementing a codebook for damage types, the codebook consisting the reference templates as codevectors; utilizing the codebook to classify the damage type of each 'hot-spot' region.
100. (New) The method of claim 99, further comprising: applying a frequency multilayer perception to the codevectors. '
101. (New) The method of claim 99, wherein the step of implementing a codebook for damage types includes: clustering SCI distribution for 'hot-spot' regions to generate cluster centers of the SCI distribution; fine-tuning the cluster centers to minimize misclassified cases; and wherein the set of said cluster centers is the codevector of each reference template for a damage type.
102. (New) The method of claim 101 , wherein the SCI distribution is a different version of SCI distribution for 'hot-spot' regions, and the coefficients of its Fourier and wavelet transformation.
■ 103. (New) The method of claim 101, wherein clustering SCI distribution is performed by a K-mean clustering algorithm.
104. (New) The method of claim 101, wherein fine-tuning the cluster centers is performed by a learning vector quantization (LVQ) algorithm.
105. (New) The method of claim 101, further comprising: 71 utilizing a principal component analysis in the step of clustering SCI distribution.
106. (New) The method of claim 105, wherein the step of utilizing principal component analysis includes: incorporating a Fisher linear discriminant analysis and eigenspace separation transformation.
107. (New) The method of claim 44, further comprising: building a dynamic system model of the sensory network system; representing anomaly by use of a system parameter of the dynamic system model; and performing a prognostic data processing for the anomaly.
108. (New) The method of claim 107, wherein the step of performing a prognostic data processing includes: identifying the dynamic system model at current time; estimating a system parameter of the dynamic system model at one-step- ahead time; and generating signals by use of the dynamic system model having the estimated system parameter.
109. (New) The method of claim 108, further comprising: repeating the steps from identifying the parameters at current time to generating signals for a plurality of time steps.
110. (New) The method of claim 107, wherein the dynamic system model of the sensory network system is a state space model or an ARMAX model.
111. (New) The method of claim 108, wherein the step of identifying the dynamic svstem mode' ?t current time includes. 72
utilizing a subspace system identification method, the method reconstructing a dynamic system model of the sensory network system by use of the signals of transmitting and receiving.
112. (New) The method of claim 108, wherein the step of estimating the system parameter of the dynamic system model at one-step-ahead time includes: employing a neural network, wherein the neural network is a recurrent neural network or a feedforward neural network; and training the neural network with the system parameters of previously reconstructed dynamic models to provide the system parameter of one-step-ahead model.
113. (New) The method of claim 108, further comprising: generating a tomographic image for one-step-ahead time; and storing the tomographic image in a prognosis tomography database depository.
114. (New) A computer readable medium carrying one or more sequences of instructions for interrogating health conditions of a structure by use of a plurality of diagnostic network patches (DNP) implemented thereto, each of the patches being able to operate as at least one of a transmitter patch and a sensor patch, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of: forming a diagnostic network including the patches and a plurality of signal transmission paths, each said signal transmission path being a signal link between a transmitter patch and a sensor patch; representing the diagnostic network by a graph, wherein the patches and signal transmission paths are respectively abstracted as nodes and edges in the graph; partitioning the diagnostic network into one or more subgroups, each of the subgroups including a designated transmitter patch and one or more sensor patches; causing the designated transmitter patch to transmit a signal and the sensor patches to receive the signal; and 73 comparing the received signal with a baseline signal to determine a deviation therebetween, the baseline signal being measured by use of the diagnostic network in absence of structural anomaly; and analyzing the deviation to determine the health conditions of the structure.
115. (New) The computer readable medium of claim 114, 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 storing the received signal and the deviation into a depository.
116. (New) The computer readable medium of claim 114, wherein the step of analyzing includes: performing a diagnostic data processing; generating a structural condition index; and generating a tomography image.
117. (New) The computer readable medium of claim 114, wherein two neighboring ones of the subgroups share one or more patches.
118. (New) The computer readable medium of claim 114, wherein the step of representing the diagnostic network by a graph includes: generating a topological architecture, x, of the diagnostic network, wherein x is represented by the equation:
X= {X12, X13 Xn-1, n} and wherein xtj e {0,1} is a decision variable representing the path between/11 and jth nodes and n is the number of the nodes.
119. (New) The computer readable medium of claim 114, wherein the step of representing the diagnostic network by a graph includes: performing a common integer programming formulation to generate a matrix of path connection, each element (i,k) of the matrix being 1 if itn sensor patch is connected to kth transmitter patch and 0 otherwise. 74
120. (New) The computer readable medium of claim 114, 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 optimizing the diagnostic network by use of a cost variable associated with network path uniformity so that network performance is maximized while the number of patches is minimized.
121. (New) The computer readable medium of claim 120, wherein the cost variable is the distance of signal transmission, the number of intersection points of each said signal transmission path crossed by neighboring paths or a sensitivity of each said signal transmission path to an excitation frequency of the signal.
122. (New) The computer readable medium of claim 114, 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, prior to the step of forming a diagnostic network: applying one or more artificial defects to the structure to simulate damages in the structure, wherein the step of forming a diagnostic network is performed by a genetic algorithm to optimize the diagnostic network.
123. (New) The computer readable medium of claim 114, 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 repeating the steps of forming a diagnostic network to comparing the received signal at a plurality of excitation frequencies of the signal.
124. (New) The computer readable medium of claim 114, 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: storing coordinates of patches and setup information including an excitation frequency of the signal, types of patches, identification numbers of patches, a voltage level of patches, and operational status of patches. 75
125. (New) The method of claim 114, 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 the baseline signal and received signal into extensible Markup Language (XML) formatted data; and parsing XML formatted data to read the signals.
126. (New) The computer readable medium of claim 116, wherein the step of performing a diagnostic data processing includes: filtering out interference signal in the signal received at a sensor patch; detrending the signal to remove a nonstationary signal component; building a envelope window from the received signal; breaking the envelope window in sub envelope windows; and applying sub envelope windows to extract wave packets from the received signal.
127. (New) The computer readable medium of claim 126, 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: obtaining a time-frequency energy distribution of the signal by use of a transformation, wherein the transformation is a short-time Fourier transformation or a wavelet transformation; and identifying the time of arrival of each mode wave in the signal by use of the time-frequency energy distribution.
128. (New) The computer readable medium of claim 127, 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: obtaining a multi-bandwidth energy distribution of signals for a plurality of excitation frequencies; extracting ridge curves from the rnυ Itl-bandwldih energy distribution, the ridge curves representing the trajectory of each wave mode; and identifying the time of arrival by use of the ridge curves. 76
129. (New) The computer readable medium of claim 126, 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: decomposing the signal into several sub-bandwidth signals by use of a wavelet decomposition filter; synthesizing a plurality of sub-bandwidth signals to forming a synthesized signal containing the frequency bandwidth of interest; repeating the steps from building an envelope window to applying sub envelope windows to extract wave packets from the synthesized signal; and determining the spectrum energy of each wave packet by use of the envelope window.
130. (New) The computer readable medium of claim 116, wherein the step of generating a structural condition index (SCI) includes: removing an abnormal signal to checking the SCI; deleting an outlier SCI from SCI dataset; and compensating the SCI for temperature change in the measurement of the received signal compared to a baseline signal.
131. (New) The computer readable medium of claim 130, wherein the step of removing an abnormal signal includes: determining a discrete probability density function (DPDF) of the amplitude of the received signal; calculating the covariance, skewness factor and flatness factor of the DPDF; and checking a normality constant of the signal to removing the abnormal signal.
132. (New) The computer readable medium of claim 130, wherein the step of deleting an outlier SCI from SCI dataset includes: computing s probability density function (FDF) of a SCl dataset, finding the outlier in the SCI dataset in the PDF; and deleting the outlier by use of a normality constant of the SCI dataset. 77
133. (New) The computer readable medium of claim 130, wherein the step of compensating the SCI value for temperature difference in the measurement of the received signal compared to a baseline signal includes: checking the temperature change therebetween; building a temperature reference table; calculating a temperature-adjustment parameter from the table; and scaling the SCI value by the parameter to compensate the SCI value.
134. (New) The computer readable medium of claim 116, wherein the step of performing a diagnostic data processing includes: designating a plurality of points on a network plane, wherein said network plane containing the lines of transmission paths and the coordinates of patches; allocating a SCI value to each said designated point; generating mesh-grid points in the network plane, the network plane being divided into mesh elements; distributing the SCI values of the designated points on the mesh-grid points by use of an interpolation method; and whereby, for the case of signal measurement at a sensor patch not linked with transmission paths to any transmitter patches, each said designated point is the same point of the location of the sensor patch.
135. (New) The computer readable medium of claim 134, 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 refining the SCI values of the mesh-grid points.
136. (New) The computer readable medium of claim 134, 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: generating a tomographic image of the SCI values of the mesh-grid points with a color range; storing the tomographic image and the color range in a depository; and 78 wherein the color range is adjusted to have a clear look at 'hot-spot' zones.
137. (New) The computer readable medium of claim 135, wherein the step of refining the SCI values on the mesh-grid points includes: utilizing a genetic algorithm to distributing the refined SCI values on the mesh- grid points.
138. (New) The computer readable medium of claim 134, 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 applying algebraic reconstruction technique (ART) to the time-of-arrival SCI values.
139. (New) The computer readable medium of claim 134, 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 applying simultaneous iterative reconstruction technique (SIRT) to the time-of-arrival SCI values.
140. (New) The computer readable medium of claim 136, 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: repeating the steps of generating a network tomographic image at a plurality of excitation frequencies; and stacking each network tomographic image to generate a hyperspectral cube.
141. (New) The computer readable medium of claim 136, 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: repeating the steps of generating a network tomographic image at a plurality of consecutive temporal points of damage state; and stacking each network tomographic image to generate a damage evolution manifold, wherein said damage evolution manifold represents the evolved state of structural condition. 79
142. (New) The computer readable medium of claim 134, wherein the step of allocating a SCI value to each said designated point includes: employing an expert system to determine the structured SCI distribution of the designated points of transmission paths.
143. (New) The computer readable medium of claim 135, wherein the step of refining the SCI values of the mesh-grid points includes: utilizing a cooperative hybrid expert system to simulate SCI distribution of the mesh-grid points for 'hot-spot' regions of damage, the expert system being trained with prior-known artificial damage.
144. (New) The computer readable medium of claim 134, 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 the SCI data of designated and mesh-grid points into extensible Markup Language (XML) formatted documents; and parsing XML formatted documents to read the SCI data.
145. (New) The computer readable medium of claim 116, 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: building a damage classifier of multilayer perception, wherein the multilayer perception is a feedforward neural network; and training the damage classifier to classify damage type.
146. (New) The computer readable medium of claim 145, wherein the step of building a damage classifier includes: producing a set of wavelet features to segmenting 'hot-spot' regions from background SCI distribution; and employing multilayer perception layers to recognizing the damage type on each 'hot-spot' reσion. 80
147. (New) The computer readable medium of claim 116, 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: implementing a codebook for damage types, the codebook consisting the reference templates as codevectors; utilizing the codebook to classify the damage type of each 'hot-spot' region.
148. (New) The computer readable medium of claim 147, wherein the step of implementing a codebook for damage types includes: clustering SCI distribution for 'hot-spot' regions to generate cluster centers of the SCI distribution; fine-tuning the cluster centers to minimize misclassified cases; and wherein the set of said cluster centers is the codevector of each reference template for a damage type.
149. (New) The computer readable medium of claim 116, 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: building a dynamic system model of the sensory network system; representing anomaly by use of a system parameter of the dynamic system model; and performing a prognostic data processing for the anomaly.
150. (New) The computer readable medium of claim 149, wherein the step of performing prognostic data processing includes: identifying the dynamic system model at current time; estimating a system parameter of the dynamic system model at one-step- ahead time; and generating signals by use of the dynamic system model having the estimated system parameter.
151. (New) The computer readable medium of claim 150, wherein execution of one or more sequences of instructions by one or more processors causes the one 81
or more processors to perform the additional step of repeating the steps from identifying the parameters at current time to generating signals for a plurality of time steps.
152. (New) The computer readable medium of claim 150, wherein the step of estimating the system parameter of the dynamic system model at one-step-ahead time includes: employing a neural network, wherein the neural network is a recurrent neural network or a feedforward neural network; and training the neural network with the system parameters of previously reconstructed dynamic models to provide the system parameter of one-step-ahead model.
153. (New) The computer readable medium of claim 150, 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: generating a tomographic image for one-step-ahead time; and storing the tomographic image in a prognosis tomography database depository.
154. The computer readable medium of claim 114, wherein the one or more sequences of instructions implement a remote processing method of Simple Object Access Protocol (SOAP) or Remote Procedure Call/eXtensible Markup Language(RPC-XML) for Internet Web Services.
155. The computer readable medium of claim 114, wherein the one or more sequences of instructions implement a wireless communication method of Wireless Application Protocol (WAP) or Wireless Markup Language (WML) for the Internet Web Access of a WAP-enable cell phone, Pocket PC with a HTML browser, or other HTML enable devices.
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