WO2002089042A1 - Systeme pour generer des images thermographiques en utilisant la reconstruction de signaux thermographiques - Google Patents

Systeme pour generer des images thermographiques en utilisant la reconstruction de signaux thermographiques Download PDF

Info

Publication number
WO2002089042A1
WO2002089042A1 PCT/US2002/011971 US0211971W WO02089042A1 WO 2002089042 A1 WO2002089042 A1 WO 2002089042A1 US 0211971 W US0211971 W US 0211971W WO 02089042 A1 WO02089042 A1 WO 02089042A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
polynomial
sample
pixel
reconstructed
Prior art date
Application number
PCT/US2002/011971
Other languages
English (en)
Inventor
Steven M. Shepard
Original Assignee
Thermal Wave Imaging, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Thermal Wave Imaging, Inc. filed Critical Thermal Wave Imaging, Inc.
Publication of WO2002089042A1 publication Critical patent/WO2002089042A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G01N25/72—Investigating presence of flaws
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0007—Image acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G06T7/41—Analysis of texture based on statistical description of texture
    • G06T7/42—Analysis of texture based on statistical description of texture using transform domain methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30164—Workpiece; Machine component

Definitions

  • the present invention generally relates to thermal imaging and more particularly relates to non-destructive detection of defects in a sample using thermographic image data.
  • Active thermography is used to nondestructively evaluate samples for sub-surface defects. It is effective for uncovering internal bond discontinuities, del animations, voids, inclusions, and other structural defects that are not detectable by visual inspection of the sample.
  • active thermography involves heating or cooling the sample to create a difference between the sample temperature and the ambient temperature and then observing the infrared thermal signature that emanates from the sample as its temperature returns to ambient temperature.
  • An infrared camera is used because it is capable of detecting any anomalies in the cooling behavior, which would be caused by sub-surface defects blocking the diffusion of heat from the sample surface to the sample's interior. More particularly, these defects cause the surface immediately above the defect to cool at a different rate that the surrounding defect-free areas.
  • the infrared camera monitors and records an image time sequence indicating the surface temperature, thereby creating a record of the changes in the surface temperature over time. It is the current practice to use a human operator to view the record of these changes and to look for "hot spots" in the image record. In many instances, this analysis is purely visual (i.e. the human inspector views a display of the image output on a monitor and identifies regions that appear "hot” compared to surrounding areas. More sophisticated methods attempt to use numerical processing of the data by generating contrast curves relative to a reference specimen of known quality and composition (a so-called "gold standard"). This reference specimen, which is known to be defect free, is typically placed in the field of view of the imaging camera.
  • the "gold standard" is not a reference specimen at all, but rather it is an image that has been derived from a physical model.
  • the time history of the cooling of the sample is not viewed as a whole (i.e. a contiguous sequence), but rather as a collection of individual frames acquired from the infrared camera.
  • These methods work adequately for large, or near surface, defects.
  • manufacturing processes and safety standards requirements place higher demands regarding smaller/more subtle defect detection, these traditional methods become less effective because the small signal levels associated with subtle defects are lost in the noise, drift, and instability that is inherent to infrared cameras.
  • visual defect identification methods tend to be subjective, and they do not readily and easily lend themselves to the automatic defect detection process. Further, it is not possible to measure the depth of the defects simply by viewing the infrared images.
  • infrared data sequences of thermal decay typically used in non-destructive testing tend to be difficult to manipulate mathematically due to their low signal-to-noise ratios and large dynamic range and also require a great deal of computer processing power, memory and storage space.
  • One attempt at automating the defect detection process involves analyzing the contrast between each pixel in the image and a reference to generate a curve representing the amount of contrast between each pixel and the reference.
  • the reference can be established any number of ways including using a reference pixel (from the sample image), a pixel group (from the sample image). If a pixel, or a pixel group is used, a reference point or reference area of the sample must be defined.
  • the reference can be a defect-free area of the sample, or the mean of the entire field of view of the camera (when viewing the sample).
  • the temperature-time history of this reference pixel or pixel group is subtracted from the time history of each pixel in the image to generate a contrast vs. time plot. Any significant temperature difference between any given pixel and the reference indicates the presence of a defect which will exhibit itself as a peak in the contrast vs. time plot.
  • the contrast vs. time plot can be measured with respect to the time at which the peak occurs, the time at which a maximum ascending slope occurs, and/or a moment of the curve for each pixel. Other options, such as generating and displaying the contrast vs. time plot with a reference plot and checking the point at which the two plots separate, have also been applied.
  • contrast-based methods tend to have significant shortcomings, however.
  • contrast-based methods require the identification of a defect-free region on the sample as a reference point. This requirement is often not realistic for some samples if, for example, the size of the defect is larger than the infrared camera's field of view. In such a case, there is no defect-free area available that can act as a reference for a given region. Further, if the entire sample exhibits a defect (e.g., a large delamination running underneath the entire surface of the sample), there is no contrast between any region of the sample because the whole sample is equally or nearly equally defective.
  • a defect e.g., a large delamination running underneath the entire surface of the sample
  • Contrast-based methods that rely on the mean of the entire field of view as a reference have also been used, but this method assumes that the defect area in the field is small enough so that it will not appreciably influence the mean. If a defect (or group of defects) occupies a large portion of the field of view, the contrast method is ineffective because a significant portion of the mean value result is composed of data derived from defective sample points which acts to reduce any appreciable difference between the defect area and the mean when the contrast value is calculated.
  • the results obtained using contrast-based methods depend strongly on the choice of reference region on the sample. More particularly, the results obtained in contrast-based methods can be altered by simply changing the location of the reference region.
  • the contrast based method inherently must be capable of discriminating between pixels associated with defects and pixels associated with noise.
  • the peak slope (of the temperature vs. time relationship) is a useful indicator of defect depth, the peak slope inherently must occur earlier than the peak contrast and may be obscured by the heating event, or by lingering heat from the equipment after flash heating the sample. The peak slope may also be obscured if the instantaneous temperature of the sample exceeds the camera's peak temperature detection capabilities, causing an initial, highly nonlinear response from the camera due to camera saturation.
  • thermographic data A common approach to improving the signal-to-noise content of thermographic data is to replace the amplitude of each pixel with the mean or median value of that pixel and its surrounding nearest neighboring pixels as defined by an N x N matrix, where N is a selected integer. This approach, however, sacrifices spatial resolution to lessen temporal noise.
  • Another approach for reducing temporal noise is to average data over a selected number of consecutive frames, but this approach sacrifices temporal precision.
  • known techniques for reducing temporal and spatial noise necessarily degrade temporal and/or spatial resolution and precision.
  • thermographic data Another technique which may be used in attempt to filter noise from thermographic data is to simply fit the raw temperature-time history of each data point of the sample, with a polynomial or a set of orthogonal functions.
  • thermal imaging when one understands the underlying physical process of thermal imaging as well as the nuances of using all but the most expensive thermal imaging cameras, these approaches prove unsuccessful for several reasons: A. Thermographic data (when generated using a pulse of energy to heat the sample), presents an extremely large dynamic range thereby making it extremely difficult to accurately fit both the data occurring early in the sampling process (large amplitude) and later in the sampling process (small amplitude).
  • the very steep, early post- excitation behavior of the temperature-time history of a point requires a high order polynomial or other similar expansion to accurately model the data.
  • high order terms introduce undesirable errors (such as oscillations) in the polynomial fit later in the time-temperature sequence when in fact the data is not oscillatory but rather stable.
  • thermographic data it is an object of the invention to provide for a non-visual interpretation of thermographic data and to permits the objective, non-destructive evaluation of samples.
  • Another object of the invention is to provide a non-destructive defect detection system and method that reduces the size and complexity of the temperature-time history of image data without compromising the usefulness of the data in detecting the location and physical characteristics of sub-surface defects of a sample.
  • Still another object of the invention is to provide a non-destructive defect detection system that does not require obtaining a reference value to detect defects by locating areas in which there is a contrast between the reference and the sample being evaluated.
  • Yet another object of the invention is to provide to improve both the temporal and spatial signal-to-noise ratio of an infrared camera output without sacrificing temporal or spatial resolution of the data generated therefrom.
  • An additional object of the invention is to image multiple segments of a sample and then to assemble the multiple segments to form an integrated, mosaic image.
  • the present invention is directed to a system for determining a time response of a monotonically changing characteristic of a sample, including a camera that obtains at series of sample images over time, wherein each image includes a plurality of pixels, and each pixel includes an amplitude corresponding to the monotonically changing characteristic of a portion of the imaged sample, and a processor that receives the series of sample images and generates therefrom a data array for each portion of the imaged sample, wherein the data array is a reconstructed version (model) of the pixel amplitude image data, as a function of time, and wherein the processor generates the reconstructed version of the raw image data by fitting a polynomial (or similar mathematical expansions or decompositions - such as those used with orthogonal functions) to at least some of the pixel amplitude image data, the polynomial having at least one polynomial coefficient such that each portion of the imaged sample is represented by a coefficient array containing said at least one polynomial coefficient,
  • the invention is also directed to a method for determining a time response to a monotonic change in a thermal characteristic of a sample, comprising the steps of obtaining a plurality of spatially distinct images (images taken over different regions of the sample) of the sample over time, each spatially distinct image having a plurality of pixels each pixel having an amplitude corresponding to the monotonically changing characteristic of a portion of the imaged sample, the sample, generating a data array for each pixel amplitude respectively corresponding to a portion of each spatially distinct image of the sample, the data array corresponding to a scaled version of the pixel amplitude at a given time or to a scaled version of the given time, fitting a polynomial to the data array associated with at least a portion of the plurality of pixels, the polynomial having at least two polynomial coefficients, such that each pixel amplitude respectively corresponding to a portion of each spatially distinct image of the sample is represented by a coefficient array containing said at least two polynom
  • FIG. 1 is a block diagram of one embodiment of the present invention wherein the thermal excitation source is a light source and the sample 104 is a generic sample;
  • Figure 2 is a flowchart illustrating an embodiment of the data reconstruction method of the present invention
  • Figures 3A and 3B are thermal decay graphs illustrating a temperature-time decay characteristic of an imaged sample in a linear domain ( Figure 3A) and in a logarithmic domain ( Figure 3B);
  • Figure 4 is an image (formed from reconstructed data) of a front view of a control sample, wherein the control sample contains a plurality of flat bottom holes drilled from the back of the sample at various depths.
  • Figure 5 is an image (formed from raw data, i.e. data that has not been conditioned using the reconstruction techniques of the present invention) of a front view of a control sample
  • Figures 6 and 7 are images created by respectively taking the first and second derivative of the reconstructed data used to form the image of Figure 4.
  • Figure 8 A is a block diagram of a test set up of the system of Figure 1, wherein the sample 104' is a control sample.
  • Figure 8B is a temperature-time graph of raw data generated by regions A, B, and C of the control sample 104' of Figure 8A as it cools, wherein the data is displayed in the linear domain.
  • Figure 8C is an enlargement of a portion of the graph of Figure 8 A.
  • Figure 8D is an enlarged view of the control sample 104' of Figure 8A.
  • Figure 9A is a temperature-time graph of raw data generated by regions A, B, and C of the control sample 104' of Figure 8A wherein the raw data has been acted on by steps 202-220 of Figure 2, but before it has been fit to a low order polynomial.
  • Figure 9B is a graph of Figure 9A after it has been acted upon by step 222.
  • Figure 9C-9E are graphical representations of the first, second, and third derivatives of the graph of Figure 9B.
  • Figure 10 is a graph of the same data used to generate Figures 8B and 8C except it is scaled using a T "2 scaling scheme.
  • Figure 11 is a flowchart illustrating one way in which the inventive system can be calibrated to detect and quantify the depth of a defect in a sample.
  • Figure 12 is a graph illustrating an example of how the inventive system can be used to construct a color defect map.
  • Figure 13 is a flow chart of the present invention as it applies to generating a reconstructed defect map using pulse phase information.
  • Figure 14 is a reconstructed defect map image fabricated by using pulse phase thermography techniques in conjunction with the data reconstruction technique of Figure 2.
  • Figure 15 is a defect map constructed using pulse phase thermography and raw data (i.e. data that has not been reconstructed using the present invention).
  • Figure 16 is a graph illustrating how the present system can be used to fit two or more polynomials to image data.
  • Figure 17 is a flowchart illustrating one embodiment of the disclosed invention and its application to integrating two or more image segments.
  • Figure 18 is a representative diagram of an integrated mosaic image generated according to the method shown in Figure 17.
  • Figure 19 is a schematic depiction of the hardware embodiment of the present invention used to implement a vibro thermography application.
  • Figure 20 is a temperature-time graph of a portion of sample 102 of Figure 19 which receives acoustic energy.
  • Figure 21 is a schematic depiction of a hardware embodiment of the present invention used to implement a scanned thermography application of the present invention (using a moving sample and fixed camera arrangement).
  • Figure 22 is a schematic depiction of the hardware embodiment of the present invention used to implement a scanned thermography application of the present invention (using movable camera and stationary sample arrangement).
  • Figure 23 is a depiction of a sequence of images frames captured by a scanning application of the present invention.
  • Figure 24 is a depiction of the digital manipulation of images captured in Figure 23 in order to generate the temperature-time history of an image segment according to the reconstruction technique of the present invention.
  • the system of the present invention operates on a reconstructed representation of the entire temperature-time history of acquired thermographic data rather than the raw thermographic data. This approach is beneficial because:
  • thermographic data is typically an order of magnitude smaller than the raw thermographic data in terms of the amount of computer memory it requires for storage.
  • thermographic data is almost entirely free of temperal noise (typically introduced from the infrared camera) and mechanical instability.
  • temperal noise typically introduced from the infrared camera
  • mechanical instability There are several possible sources of mechanical instability associated with using infrared cameras. Infrared cameras require cooling to very low temperatures (typically infrared cameras are cooled with liquid nitrogen using a Stirling engine).
  • a Stirling engine employs, amongst other components, a small oscillating piston. Because the piston oscillates, it gives rise to camera vibrations. It is difficult to completely eliminate these vibrations even with the most steadfast of mounting apparatus (which is impractical to use in some applications). Moreover, the vibrations can become amplified if the camera is mounted to a tripod or lever arm. In practice, some applications can not be carried out unless the inspection device (infrared camera), is held in place by the operator while data is acquired. Any shaking or movement by the operator will be reflected in "mechanical instability" of the data.
  • thermographic data is, in a preferred embodiment, based on an analysis of derivatives (rather than contrast relative to nearby points) of the time evolution of each point in the image. Analyzing derivatives lends itself to directly automating the image inspection task because they can be objectively analyzed for characteristic features (zero crossings, extrema, etc.) without visual confirmation by an operator.
  • thermographic image taken from defect free samples
  • scale from sample to sample (there is no deviation in shape from sample to sample).
  • the present system is based on a well-known physical model that allows analysis of a sample response to excitation as a deterministic phenomenon and not a phenomenon which is linked to thermographic data collected from neighboring points.
  • the present invention detects subsurface defects of a sample using an infrared camera by observing and analyzing selected characteristics of a thermal decay process of the sample.
  • the basic foundation in which the inventive system operates assumes that the field of view is limited to the portion of interest on the sample and that inspection of the total sample surface may require interrogation of multiple regions (also called image segments herein).
  • the inventive system and method recognizes that a thermally excited (heated) region of the sample cools monotonically after the excitation source removed until the sample reaches thermal equilibrium with its surroundings, and that the thermal response of any point on the sample surface during the time interval immediately after heating, decays in such a manner that the natural logarithm of the temperature-time response of a defect-free sample, as it cools, is a function that can be approximated by a straight line.
  • FIG. 1 illustrates one possible embodiment of the apparatus used to carry out the invention
  • Figure 2 is a flowchart illustrating one embodiment of the inventive method.
  • a system 100 for obtaining the data to be analyzed in the inventive method includes at least one heat source 102, and preferably a pulsed heat source, that heats a sample 104 to be evaluated with a pulse.
  • the heat source itself can be any source, such as flashlamps, heat lamps, electric current, heated air, electromagnetic induction, ultrasonic energy, etc., but the specific choice of heat source does not matter for purposes of the invention as long as there is a heating of the sample and then a monotonic, deterministic decrease in the sample's temperature.
  • An infrared camera 106 captures a series of images of the sample, and is coupled to a computer 108 having digital image acquisition or analog frame-grabbing capabilities to convert the data from the infrared camera 106 to a format that can be analyzed and mathematically manipulated by the computer 108.
  • the computer 108 does not necessarily need to be separate from the camera 106 and that the functions in the computer 108 can be incorporated into the camera itself as, for example, an on-board integrated circuit.
  • the methods set forth herein greatly reduce the data volume and manipulation normally associated with image thermography, the methods set forth in this present invention make it particularly well suited to employ the image processing techniques of the present invention directly within a dedicated processor located within camera 106.
  • the computer 108 may also have an optional acquisition module 110 that is used if the camera 106 obtains multiple spatially different images to generate a complete mosaic image of the sample, particularly when the sample is too large to fit in a single image frame.
  • the inventive method 200 first involves starting acquisition of a sequence of infrared images from the sample at step 202 and then thermally exciting the sample 204.
  • the image sequence can be stored in computer memory, videotape, or any other electronic storage means.
  • the acquisition process is terminated after a predetermined time 206 and digital data corresponding to the image sequence is transferred 208 to a computer or dedicated hardware for mathematical analysis.
  • the data is in analog format, it is first digitized at step 210.
  • the length of the image sequence will depend on the type of material being inspected and the depth at which suspected defects are located. If the material has low thermal conductivity and/or if the suspected defects are relatively deep inside the sample, the image sequence may be lengthened.
  • a typical image sequence from an infrared camera operating at 60 frames per second will contain several hundred frames. In extreme cases, the image sequence may contain as many as several thousands of frames.
  • the time over which the data acquisition step 201 takes place can range over several seconds as the sample temperature returns to equilibrium, but the specific length of time will vary depending on the thermal properties of the sample. Further, the output image sequence (or defect map sequence) can be generated over any time duration bounded between the heating flash event and the last image sequence acquisition event, independent of the sampling rate of the infrared camera 106.
  • step 212 the pre-excitation temperature amplitude of each pixel is subtracted from the post-excitation history temperature for that pixel.
  • the process is applied to every pixel in the field of view of every image in the image sequence.
  • the result of subtracting the pre-excitation temperature is that the resulting signal indicates the sample's response to the thermal excitation event and negates any influence that the sample's ambient temperature prior to excitation might otherwise have on the data.
  • step 214 the subtracted data is smoothed using any number of smoothing techniques. Smoothing is necessary because although the overall trend of the surface temperature of the sample is monotonically decreasing, consecutive data points in the post- excitation time history may not behave as expected due to undesirable noise artifacts. These undesirable noise artifacts typically are composed of high frequency components and are easily removed by fitting a straight line segment (or second order polynomial) to groups of adjacent points and replacing a particular point with the value on the straight line. This process is preferably repeated for every point in the time history; however, the number of points chosen in each grouping should increase as the latter occurring data is smoothed. This allows each line segment to become longer as later points occurring later in the time history are smoothed. This approach accurately models the later occurring data primarily because as time extends further away from the onset of the thermal pulse, the image data tends to change less than it did earlier in time and accordingly behaves more linear.
  • the data is scaled.
  • the data is scaled in a way which reduces the dynamic range of the post-flash time history and causes it to behave in a linear, or near linear, manner if no sub-surface defects are present.
  • One such preferred scaling operation is using the natural logarithm of the temperature versus natural logarithm of time plot (see Figure 3A of prescaled data and Figure 3 B of post scaled data). This approach is preferable because it results in a temperature versus time plot of a defect free sample to be a straight line with a slope of -0.5 (the slope is the same irrespective of the sample composition or hardware used in the imaging process).
  • other scaling operations are possible. For example, scaling by using the inverse square of the temperature ( ) versus time results in an ascending straight-line result for a defect-free sample. In either case, the behavior follows the predictions of a one-dimensional solution of the heat diffusion equation.
  • step 220 the post-excitation response of the sample is governed by diffusion of heat into the sample and this diffusion of heat is described by the well-known diffusion equation.
  • the surface temperature changes rapidly immediately after excitation, but the rate of change decreases as time progresses (see Figure 3A).
  • the abrupt decay occurring in the early stages of the sample cool down causes there to be too few early time data points and an excessive number of later data points (this is clearly seen in the plot of temperature decay versus time of Figure 3A).
  • a more accurate way to model the true thermal behavior of the sample is to add reconstructed points by interpolation between early raw data points in order to increase the influences of early behavior in the fit.
  • improved fidelity to the underlying data is achieved if latter data points are sampled in a way which reduces the influence of the latter occurring data points (typically this is accomplished by thinning later occurring data points).
  • Step 222 involves fitting the data generated in step 220 using a low order polynomial (preferably sixth order or less) using a leased squares fit technique.
  • a low order polynomial preferably sixth order or less
  • the low order polynomial serves as a low pass filter to ensure that only the information content of the data representing the thermal response of the sample is preserved and that the noise content of the data is rejected.
  • the use of as low order polynomial as possible is counter intuitive but nonetheless it is the preferred method. Generally speaking, a higher order polynomial will allow you to fit the data with less error.
  • any high frequency information contained in the data can be confidently dismissed as noise and such high frequency noise can be easily filtered out using the lowest order polynomial which still permits reasonable fidelity to the underlying thermal information contained in the data.
  • step 224 the scaling is inverted to create a reconstructed version of the new data.
  • the polynomial resulting from step 222 is a continuous function obtained from the discrete data, and thereby allows the method of the present invention to generate pixel amplitude values for all time values (even for time values that fall between frame acquisitions).
  • each pixel is represented by an array of n polynomial coefficients, which will typically be six coefficients or less making it unnecessary to thereafter store the actual data sequence which can be several hundreds or even several thousands of frames generated by the infrared camera.
  • the polynomial representation includes only an array of coefficients, and because the polynomial representation of the pixel temperature-time characteristic is independent of the length of the data sequence, the amount of data that must be stored for any given pixel is tremendously reduced by the polynomial representation and accordingly, much simpler to manipulate mathematically than raw camera data.
  • the resulting file size for storing the pixel data is independent of the number of images taken by the camera, further reducing the memory needed to store or manipulate the image data.
  • the file size is equal to the number of pixels being imaged multiplied by the number of coefficients in the polynomial multiplied by the number of bytes per coefficient, regardless of the number of images.
  • the reconstructed data is analyzed to determine if any sub-surface defects are present. This determination can be done in any number of ways. Firstly, the reconstructed data for each pixel can be assembled into an image which is displayed graphically to a user. Such an image is known as a defect map and an example is depicted in Figure 4.
  • Figure 4 is a front view of a control sample which has a plurality of flat bottom holes drilled into the sample from the back side. The holes are drilled at various depths (none of which pass through the sample) and accordingly manifest themselves in a reconstructed image as circular elements of various light intensities. These bright spots are also called "hot spots".
  • Figure 5 is a depiction of the same sample shown in Figure 4; however, the depiction in Figure 5 is constructed from raw thermographic image data wherein the image of Figure 4 is assembled using reconstructed thermographic image data derived from the process described in Figure 2. Rather than simply visually analyzing the reconstructed data, in some applications is far more convenient to examine the first, second, and even third time derivatives of the reconstructed data.
  • Images of the first and second derivatives can be generated from Equations 4 and 5 through any means, if desired, by entering time information into the polynomial or its derivatives. Note that because the derivatives of the image data are calculated analytically rather than by fitting a straight line to the tangent of the noisy image data, the results obtained from the calculated derivatives yields more accurate results than attempts to compute the average over many noisy data points. Further, analytical calculation of the derivatives yields results that are true instantaneous derivatives rather than differentials over an interval spanning several image frames.
  • the first and second derivatives are obtained by manipulating the polynomial expression rather than conducting linear regression or curve fitting, the derivatives do not themselves contribute any noise to the final result.
  • the invention uses noise-reduced, analytically differentiated data obtained from scaled data, the noise reduction provided by the invention allows more accurate detection of deeper and weaker defects as well as large defects encompassing the entire field of view.
  • Figure 6 is an image formed from the first derivative of the reconstructed data shown in Figure 4.
  • Figure 7 is an image formed from the second derivative of the reconstructed data of Figure 4.
  • Third and higher order derivatives can be calculated and displayed using the identical techniques.
  • One primary advantage for using derivatives of reconstructed data is that inflection points (or extrema) which occur as a result of the interaction of heat flow with sub-surface features are significantly enhanced in the derivatives despite the fact that they may be largely unnoticeable in the raw signal. This feature of the present invention is best explained in conjunction with Figures 5, and 8A-13.
  • Figure 5 depicts (as seen on a graphical display device such as a cathode ray tube) a defect map constructed from raw data (data which has not been acted on by the method set forth in Figure 2).
  • the raw data of Figure 5 has been collected from the control sample 104' as shown in Figure 8D.
  • the temperature - time history associated with three distinct points A, B, and C having cartesian coordinates (9, 12.5); (12.5, 12.5); and (19, 12.5) respectively, is shown in Figure 8B and a portion of Figure 8B is enlarged in Figure 8C.
  • the numerous "wiggles" in the graph of Figure 8C are examples of noise in the data and do not represent a thermal event in the control sample 104'.
  • Figure 8B is a temperature-time graph of the raw data generated by points A, B, and C as they cooled.
  • Figures 9C-9E show the first derivative, second derivative, and third derivative respectively of the reconstructed data of Figures 9B.
  • the third derivative of Figure 9E makes it extremely easy to detect the occurrence of a flaw because such occurrences take place every time that a third derivative of points B and C makes a negative going zero crossing with the third derivative of point A.
  • Figure 10 contains the same data as that contained in Figure 9 A except the data in Figure 10 is scaled according to the T ⁇ 2 scaling method discussed in conjunction with method step 218 of Figure 2 whereas the scaling in Figure 9A is constructed with respect to the ln/ln scaling step previously discussed in conjunction with step 218 of Figure 2.
  • the present invention is effective for finding defects in samples without the use of a control sample or some other reference, it is acknowledged that in some applications, it might be convenient for control samples or other references to be used. The method set out herein can be used in such applications; however, such an approach is wholly optional.
  • the inventive system and method of generating polynomial equations from the image data may also be used to generate a contrast curve by identifying a defect- free reference region of the sample or using a separate reference sample and deriving the polynomial equation associated with the reference, if desired.
  • a contrast curve can then be generated by subtracting the polynomial expression for the reference from the polynomial expression for each pixel; a large difference between the two would indicate the presence of a defect. If no reference is available, one can be created by extrapolating a straight line with a slope of -.5 from the beginning of the reconstructed data curve.
  • an image representation 227 of the behavior of the sample at that time can be scaled to match the dynamic range of the display device.
  • This scaling operation can be conducting using any common statistical scaling algorithm.
  • the image 227 or images based on the polynomial and/or its derivatives can be displayed on an output device, such as on a computer display screen.
  • the display screen can be one or more discrete points on the sample ( Figure 9B), a single reconstructed image at a selected time t ( Figure 4) or a sequence of reconstructed images displayed as a movie (not shown).
  • the temporal resolution of the movie can be different than the actual data acquisition frame rate, if desired, to show the changes in the sample temperature more clearly; this can be conducted easily because the derived polynomial is a continuous function, as noted above.
  • the data can be obtained from temperature-time data in an image that is scanned (e.g., systems that acquire image data as the sample is moved relative to a heat source and an IR camera at a constant velocity, systems that move the camera and heat source relative to the sample, etc.).
  • Figure 11 is a flowchart illustrating how the generated polynomial is used in quantifying defect depth. Using a calibration standard (constructed from the same material as the sample of interest and possessing defects at known depth), the third derivative zero cross times are measured. For each known defect, the square of the depth is plotted against its zero crossing time and the second order polynomial that intersects the origin is fit to the data using a least squares algorithm. The net result is an expression for depth as a function of zero crossing time:
  • the coefficients a 0 , ai and a 2 can be used for subsequent depth measurements on samples made from the same material as that of the calibration standard.
  • the invention can then determine the defect depth.
  • the system uses ao , a 1 and a 2 corresponding to the material composition of the sample. Constants , a 1? a 2 , and a 3 are calculated from the temperature-time information of a defect having known dimensions (i.e., ao , & ⁇ and a 2 can be readily calculated from Equation 6 if the second-derivative zero crossing time and the depth are known for a reference defect).
  • the defect area the total number of pixels having third-derivative zero-crossing values are counted and multiplied by the single pixel area. The ability to accurately calculate defect area value can be of significant value because the criteria for rejecting a sample is often based on the defect area.
  • the above process can be used to pre-process any images from an infrared camera for further analysis, such as peak slope or peak contrast time measurements, breakpoint analysis, pulse phase lock-in, etc.
  • the pre-processing steps described above generate an image signal with much of its temporal noise removed, yielding more accurate results in any additional processes.
  • FIG. 13 The flowchart of Figure 13 shows how the polynomial reconstruction of the present invention can be easily applied to pulse phase thermography.
  • Pulse phase thermography is a well known technique wherein a thermographic image is constructed from a pulse phase image and a pulse magnitude image.
  • the first four steps (steps 302-308) of Figure 13 closely track steps 210-222 of Figure 2.
  • F* is the complex conjugate of the FFT.
  • the FFT of the time history is a function of frequency, rather than time, the phase varies with frequency. It is particularly useful to find the maximum phase value for each pixel, and create a maximum phase image, as this provide a map of subsurface defects, which are typically out of phase with defect free areas. However, the presence of noise in the signal typically makes discrimination of maximum phase difficult for all but very shallow or gross defects.
  • Figure 14 is a phase image created from reconstructed data. This stands in stark contrast to the image of Figure 15 which is a phase image constructed from raw data.
  • the reason why the reconstructed phase image of Figure 14 is vastly superior to that of Figure 15 is that whenever FFT's are involved in data manipulation, they are very sensitive to noise. Because the reconstructed method of the present invention eliminates most, if not all, of the noise from the data, the end result is vastly superior to that which is achievable using traditional pulse phase techniques in conjunction with raw data.
  • the above examples focus on using a single polynomial expression as the reconstructed function to describe the temperature-time characteristic for a given sample
  • more than one polynomial expression may be desired to address the thermal behavior at the extremes of the temperature time characteristic and prevent the extremes from skewing the analysis of the temperature-time behavior of the sample.
  • the polynomial fit when using one polynomial may be adversely affected by the temperature-time curve behavior at the very early and very late stages.
  • the infrared camera data may become briefly saturated and may initially display non-linear behavior that does not reflect the thermal characteristics of the sample accurately.
  • the example shown in Figure 16 uses more than one polynomial equation to describe the complete temperature-time history of each pixel.
  • the temperature-time characteristic is divided into early, intermediate, and late behavior regions, 600, 602 and 604 respectively, each of which exhibit slightly different temporal behavior, and each region is described using a different low-order polynomial.
  • each separate region 600, 602, 604 behaves more like a linear function than a single plot of the entire time sequence. As a result, each separate region is more easily approximated by a low-order polynomial than the entire temperature-time plot.
  • the processor can calculate first, second, or higher derivatives of one or more of the polynomials. Further, as explained above, the zero crossing behavior of the second derivative can be used to determine the depth of a defect. Note that the defect depth can also be determined by finding the point in time at which the first derivative of the polynomial representing the reconstructed function deviates from -0.5 by a predetermined threshold. The -0.5 value is generated based on the known temperature characteristic of a semi-infinite solid that has been instantaneously flash-heated, which can be described as:
  • T is the temperature change relative to the initial temperature
  • e is the thermal effusivity of the material (the square root of the product of the density, thermal conductivity and heat capacity)
  • Q is the energy input to the sample by the flash-heating
  • t is the elapsed time after flash-heating.
  • the above two equations are useful because a sample will behave like a semi-infinite sample, such that the natural logarithm of the temperature-time data has a slope of -0.5, as heat propagates from the surface into the bulk of the sample until a defect or boundary is encountered. If there is a defect or boundary in the sample, the temperature-time data will deviate from the -0.5 slope.
  • the first derivative expression can be used to detect defects by checking whether the first derivative for a given pixel deviates from -0.5 based on this equation.
  • Figure 17 is a flowchart illustrating one way in which the inventive system can handle samples requiring multiple segments to cover its entire surface without encountering time delay problems caused by managing raw data.
  • an inspector determines the route that will be used to cover the inspection area at step 800.
  • the inspection route 802 involves obtaining image data while moving along a path from, for example, left to right in rows, or alternating left-right and right-left rows, or columns, etc.
  • Each frame within each image segment is stored along with time and position indexes associated therewith.
  • this time sequencing does not have to be measured on an absolute time scale (although it can be measured as such). It can be measured in terms of relative start time (time elapsing since the first frame in the current image segment), or it can also be measured in terms of frame sequence (if the frame rate is stable, frame sequence is simply measured in the sequence of raw frames - i.e. frame 1, frame 2, . . . frame n).
  • the image data from each image segment is grouped together and converted from its raw digital format to a matrix of polynomial coefficients in the manner described above at step 808.
  • the conversion step 808 can be conducted for each sample segment (i.e. position) as the acquisition each image segment is complete (but prior to collecting data for the next image segment). Alternatively, the data from all of the frames of all of the image segments can be acquired and stored in the raw digital format for later conversion.
  • a reconstructed image for each image segment 809, which is generated from the coefficient matrices can be placed automatically in the appropriate position at step 812 based on each image segment position in the inspection route 802.
  • the reconstructed mosaic comprised of multiple image segments is assembled using the spatial information from step 806 to position each image segment and to form a completed single mosaic image to be displayed at step 810.
  • An example of a mosaic 805 image is shown in Figure 18.
  • the system can provide the user with the option to select a particular display mode (e.g. reconstructed image, 1 st or 2 nd or 3 rd or higher derivatives, reconstructed pulse phase image derivative, or depth map) through a user interface (not shown) such as a computer keyboard.
  • a user interface such as a computer keyboard.
  • the maximum number of images that can be loaded into the program depends on the amount of RAM available to the program.
  • the mosaic image will have the same image characteristics as single image sequences and can be updated quickly as the user views images over time or conducts mathematical operations on the image data. More particularly, any changes in successive images or mathematically manipulated images can be generated nearly instantaneously because the invention manipulates reconstructed data (the polynomial coefficients) and not the raw data, simplifying the calculation process.
  • the inventive system and method generates a data structure, which is based on the original data sequence obtained from the infrared camera, that is more compact, easier to manipulate mathematically, and less prone to temporal noise than the original data sequence but that still preserves the characteristics that indicate the presence of sub-surface defects.
  • the inventive system allows the inventive system to provide a significant signal to noise improvement allowing relatively inexpensive infrared cameras (e.g. uncooled microbolometer cameras such as the Indigo Systems Alpha TM which are available at a fraction of the cost of high performance cameras) to be used.
  • the data structure generated by the invention is much smaller than the image data structure obtained from the camera, the stored data can be differentiated and integrated with respect to time more easily than the original data generated by the camera.
  • the analysis and manipulation of the data from the camera can be conducted in an automated fashion, without any user intervention or adjustment.
  • Assembling a total image mosaic from individual image frames according to the invention allows large structures to be inspected quickly using equipment that covers a smaller field of view than the structure's entire area. This advantage applies to viewing microscopic images also, where regions of the microscopic subject must be assembled into a composite.
  • the mosaic capability of the present application not only extends to macroscopic applications but extends to microscopic applications as well.
  • the mosaic method of the present invention is capable of manipulating an entire data image as a single entity.
  • the inventive system can be used alone or as a pre-processing step in conjunction with other methods for measuring, characterizing, and/or recognizing defects or sample material properties.
  • the above-described configuration uses an infrared camera to acquire the data and transfers the data to a computer for further processing, the entire system can be incorporated into the camera itself without a separate computer.
  • the inventive system and method can be applied to any data set that is in response to a stimulus that causes a monotonically increasing or decreasing response and where there is no random motion in the field of view in which the data is generated.
  • Vibrothermography was first developed in the late 1970s and early 1980s as a means for nondestructively characterizing and evaluating solid materials. The method was used to identify cracks and subsurface anomalies such as disbonds and delaminations in metals, ceramics, and composite materials.
  • the basic technique involves generating acoustic energy in the range of 10kHz to 30kHz and applying that energy (either continuously or in a time varying mode) to a sample 902.
  • the acoustic energy is absorbed by the sample which causes internal frictional heating between the faces of the crack or disbond 904. This internal heating results in a transient local temperature rise 903 on the surface of the sample near the anomaly. This local temperature rise can be detected using an infrared camera 906.
  • the acoustic energy is created by a ultrasonic transducer 910 which is powered by ultrasonic transducer power supply 900.
  • the ultrasonic transducers used to excite the sample are high powered devices (1 kW or greater), typically designed for ultrasonic welding applications. Such high levels of ultrasonic energy pose a safety hazard to inspectors in the vicinity of the inspection station, and often the ultrasonic energy causes damage to surface of the sample. Moreover, the tendency of practitioners of vibrothermography tend to be moving to even higher powered devices as more challenging applications are confronted.
  • thermographic results generated from the vibrothermography technique are almost entirely visual, making automation of the inspection process difficult, if not impossible.
  • the resulting images would be essentially free of high frequency temporal noise. Artifacts due to acoustic wave mode effects may remain; however, these are easily removed because they have slower rise times and different time-derivative behavior than actual cracks.
  • the process of viewing the infrared "movie" of the excitation can be eliminated by calculating the integral of the polynomial representation of the surface temperature for each pixel over the duration of the heating period. Since frictional heating will only occur in those areas where cracks or subsurface defects occur, the integral of a defect free point will be small compared to a point near a crack (regardless of whether that crack is a surface crack or a subsurface crack). However, integration of the noise-reduced reconstruction data allows detection of more subtle features and the use of lower excitation energy than simple addition schemes which employ noisy data.
  • the final result is a sequence of images of cracks or subsurface features that offers far more detail than current vibrothermography techniques allow.
  • the increased sensitivity of detection due to the processing scheme disclosed herein allows the use of far less excitation energy (approximately 50% or less) than currently use "brute force" methods such as those described in the introduction.
  • Post-flash saturation for many applications, particularly detection and measurement of corrosion, it is essential to measure the thermal response of the test area immediately after flash heating is applied. Unfortunately, as much as 30 millisec of this early-time data is often lost because the energy from the flash is reflected from the sample surface causing the detectors in the infrared camera to saturate. If the flash energy is reduced to compensate, the sensitivity of the system is reduced proportionately.
  • the lamps used in scanned systems operate continuously and deliver a constant power output (rather than the high peak energy output that gives rise to saturation in pulsed thermography systems). Furthermore, the primary source of saturation is the flash energy reflected off of the sample surface. Since the scanned lamp passed over the target area prior to the camera passing over the same target area, it is possible to shield the camera from the direct lamp reflection in a scanned system, thus minimizing the possibility of detector saturation.
  • pulse thermography has been most effective on relatively thin structures (e.g. for metal aircraft skins approximately less than 0.050 inches, for composite materials approximately less than 8 plies). For many structures where skin thickness may exceed 0.100 inches, pulse thermography is only capable of detecting relatively large defects. As input power is increased in order to increase sensitivity toward accommodating thicker structures, the saturation problem is exacerbated, and the size of the thermal imaging unit becomes impractical for use in the field.
  • D. Scan advantage it is relatively simple to control the amount of energy delivered to a sample by a scanning system by simply adjusting the scan speed and/or the lamp aperture size (the portion of the lamp that is exposed to the surface). A large amount of energy can be deposited using inexpensive halogen lamps housed in a small reflector.
  • E. Optimization for inspection of large structures for inspection of large structures (e.g. control surfaces, radomes, fuselage or masts), pulsed thermography requires that the inspection unit remain stationary over the target area for a fixed interval of time (typically 5 to 10 seconds) before it is moved to the next portion of the sample to be imaged.
  • a typical inspection may generate hundreds of image files, which can be stitched together into a single mosaic image after the entire area has been scanned.
  • the start stop acquisition process requires the use of markers or similar means to be placed on the part. This complicates implementation using a robot or a creeper.
  • scanning thermography systems are qualitative, depend highly on the operator's experience and training, and tend to have poor spatial resolution compared to pulsed systems. Additionally, when imaging large structures, the amount of data acquired, can be prohibitively large. In addition, the precise control of the velocity of the camera or the sample must be maintained.
  • the scanning embodiment of the present invention combines the strength of pulse in scanned thermography to detect and characterize deeper defects and more massive structures than were heretofore possible using traditional scanned thermography techniques. Using the enhanced scanning system of the present invention, large scale structures can be inspected quickly and relatively inexpensively. This improvement is made possible by the present invention because of the significant signal to noise improvement and data compression that is made possible by applying the signal processing methods disclosed in conjunction with Figure 2 et seq.
  • the scanning embodiment disclosed herein can be implemented in a fully automated fashion and applied to thick or massive composite structures (e.g. spars and pressure vessels) - structures that are currently beyond the capability of pulsed systems. It can also be applied to thin, reflective structures (e.g. aluminum alloy aircraft skins) that are not particularly well suited for examination by existing scanning systems.
  • the present scanning embodiment of the present invention extends the state of the art beyond the capabilities of existing pulsed or scanned systems.
  • heat source 924 is used to continually heat sample 921 as sample is carried along direction A by translator (or conveyor belt 925).
  • Heat source 924 can be comprised of any number of heating devices including, but not limited to, a quartz halogen lamp, a hot wire, ultrasonic energy, hot air, or hot water.
  • Translator 925 is used to move the sample past heat source 924.
  • the heat source 924 is moved past the stationary sample 921.
  • Computer 926 is equipped with a digital or analogue frame grabber capable of acquiring continuous image data from camera 922.
  • Camera 922 and heat source 924 are separated by shield 928.
  • Shield 928 can optionally be fitted with gasket 929 to prevent excessive leakage of infrared energy from source 924 into the field of view of camera 922.
  • the side surface 927 of shield 928 which is closest to heat source 924 can be optionally coated or otherwise be made reflective to minimize heating of the shield 928.
  • Side 931 of shield 928 which faces camera 922 can be optionally coated with, or otherwise be made to have a low infrared emissivity to minimize emission into the field of view of camera 922.
  • the infrared signature is captured by camera 922 and converted into an electronic data format, it is transferred to computer 926 where it is processed.
  • the data undergoes a sequence of steps to spatially rearrange it into a coherent image, remove scanning and illumination artifacts, and enhance subsurface features in the data.
  • the first step is a relatively simple "bookkeeping" task in which the incoming scanned data ( Figure 23) is rearranged into "data cubes" (i.e. a collection of spatially stationary temperature-time plot representing each pixel in the field of view, see Figure 24).
  • the data is transformed using the method set forth in Figure 2, steps 212 through 227.
  • All of the benefits provided by the reconstruction method of the present invention apply equally to this scanning embodiment.
  • the reconstruction method of the present invention is particularly beneficial for the scanning embodiment because of the massive amounts of data which are generated from long, continuous samples (long continuous samples are particularly well suited for imaging using the scanning embodiment). It is not unusual in a typical scanning application to generate several hundred frames of data, each frame containing thousands of pixels.
  • the reconstruction technique of the present invention works with coefficients of polynomials (and not the raw data itself) only the coefficients need to be stored, manipulated, and displayed regardless of the length of the original raw data sequence.
  • the present invention can easily reduce a 50MB (megabyte) datacube of the image sequence to 4.5MB.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Quality & Reliability (AREA)
  • Investigating Or Analyzing Materials Using Thermal Means (AREA)

Abstract

Cette invention se rapporte à un procédé et à un système servant à la détection thermographique non destructrice et sans référence de défauts sous-superficiels, en utilisant une caméra infrarouge (106) destinée à capturer de multiples images spatialement différentes d'un échantillon (104) qui a été chauffé (102) et pour permettre à l'échantillon de refroidir à sa température d'équilibre. Les données temps-température obtenues pour chaque pixel de chaque image sont converties dans le domaine logarithmique (218) et un ajustement par les moindres carrés est exécuté sur les données, afin de générer une expression polynomiale correspondant aux données temps-température pour un pixel donné.
PCT/US2002/011971 2001-04-13 2002-04-15 Systeme pour generer des images thermographiques en utilisant la reconstruction de signaux thermographiques WO2002089042A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US28356601P 2001-04-13 2001-04-13
US60/283,566 2001-04-13
US28464901P 2001-04-18 2001-04-18
US60/284,649 2001-04-18

Publications (1)

Publication Number Publication Date
WO2002089042A1 true WO2002089042A1 (fr) 2002-11-07

Family

ID=26962117

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2002/011971 WO2002089042A1 (fr) 2001-04-13 2002-04-15 Systeme pour generer des images thermographiques en utilisant la reconstruction de signaux thermographiques

Country Status (1)

Country Link
WO (1) WO2002089042A1 (fr)

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1526377A1 (fr) * 2003-10-21 2005-04-27 Paolo Benedetti Méthode la détection de défauts dans des produits essentiellement similaires au bois, particulièrement panneaux et analogues, et appareil associé
WO2005026404A3 (fr) * 2003-07-16 2005-08-18 Cabot Corp Procede et appareil d'essai thermographique pour l'evaluation de la liaison de cibles de pulverisation
WO2006037359A1 (fr) * 2004-10-04 2006-04-13 Siemens Aktiengesellschaft Procede pour determiner des parametres materiels d'un objet a partir de donnees temperature-contre-temps
US7084402B2 (en) 2003-11-25 2006-08-01 The Boeing Company Liquid coupled defect detection systems and methods
EP1852697A1 (fr) * 2004-10-04 2007-11-07 Siemens Aktiengesellschaft Procédé pour déterminer les paramètres matériaux d'un objet à partir de données de température en fonction du temps (t-t)
CN104200478A (zh) * 2014-09-12 2014-12-10 广东财经大学 一种基于稀疏表示的低分辨率触摸屏图像缺陷检测方法
EP2902277A1 (fr) * 2014-01-23 2015-08-05 Huf Hülsbeck & Fürst GmbH & Co. KG Procédé d'évaluation pour un capteur de proximité capacitif sur un véhicule automobile
US9261473B2 (en) 2011-12-12 2016-02-16 Honda Motor Co., Ltd. Non-destructive testing system
ITUB20152385A1 (it) * 2015-07-22 2017-01-22 Alenia Aermacchi Spa Metodo e sistema di ispezione termografica non distruttiva per il rilevamento e la misura di difettosita' in strutture in materiale composito
CN113884538A (zh) * 2021-10-18 2022-01-04 沈阳工业大学 大型风力机叶片内部微小缺陷的红外热像检测方法
CN115047022A (zh) * 2022-08-11 2022-09-13 合肥锁相光学科技有限公司 一种热扩散过程的时域重构方法及系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4719350A (en) * 1986-01-30 1988-01-12 Hughes Aircraft Co. Radiation imaging enhancement
US5032727A (en) * 1990-09-14 1991-07-16 Digital Equipment Corporation Product defect detection using thermal ratio analysis
US5631465A (en) * 1996-02-29 1997-05-20 Shepard; Steven M. Method of interpreting thermographic data for non-destructive evaluation
US5711603A (en) * 1996-10-30 1998-01-27 United Technologies Corporation Nondestructive testing: transient depth thermography
US5963662A (en) * 1996-08-07 1999-10-05 Georgia Tech Research Corporation Inspection system and method for bond detection and validation of surface mount devices
US6033107A (en) * 1997-07-15 2000-03-07 Temptronic Corporation Temperature mapping system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4719350A (en) * 1986-01-30 1988-01-12 Hughes Aircraft Co. Radiation imaging enhancement
US5032727A (en) * 1990-09-14 1991-07-16 Digital Equipment Corporation Product defect detection using thermal ratio analysis
US5631465A (en) * 1996-02-29 1997-05-20 Shepard; Steven M. Method of interpreting thermographic data for non-destructive evaluation
US5963662A (en) * 1996-08-07 1999-10-05 Georgia Tech Research Corporation Inspection system and method for bond detection and validation of surface mount devices
US5711603A (en) * 1996-10-30 1998-01-27 United Technologies Corporation Nondestructive testing: transient depth thermography
US6033107A (en) * 1997-07-15 2000-03-07 Temptronic Corporation Temperature mapping system

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005026404A3 (fr) * 2003-07-16 2005-08-18 Cabot Corp Procede et appareil d'essai thermographique pour l'evaluation de la liaison de cibles de pulverisation
US7425093B2 (en) 2003-07-16 2008-09-16 Cabot Corporation Thermography test method and apparatus for bonding evaluation in sputtering targets
EP1526377A1 (fr) * 2003-10-21 2005-04-27 Paolo Benedetti Méthode la détection de défauts dans des produits essentiellement similaires au bois, particulièrement panneaux et analogues, et appareil associé
US7084402B2 (en) 2003-11-25 2006-08-01 The Boeing Company Liquid coupled defect detection systems and methods
WO2006037359A1 (fr) * 2004-10-04 2006-04-13 Siemens Aktiengesellschaft Procede pour determiner des parametres materiels d'un objet a partir de donnees temperature-contre-temps
EP1852697A1 (fr) * 2004-10-04 2007-11-07 Siemens Aktiengesellschaft Procédé pour déterminer les paramètres matériaux d'un objet à partir de données de température en fonction du temps (t-t)
US9261473B2 (en) 2011-12-12 2016-02-16 Honda Motor Co., Ltd. Non-destructive testing system
EP2902277A1 (fr) * 2014-01-23 2015-08-05 Huf Hülsbeck & Fürst GmbH & Co. KG Procédé d'évaluation pour un capteur de proximité capacitif sur un véhicule automobile
CN104200478A (zh) * 2014-09-12 2014-12-10 广东财经大学 一种基于稀疏表示的低分辨率触摸屏图像缺陷检测方法
CN104200478B (zh) * 2014-09-12 2017-03-22 广东财经大学 一种基于稀疏表示的低分辨率触摸屏图像缺陷检测方法
ITUB20152385A1 (it) * 2015-07-22 2017-01-22 Alenia Aermacchi Spa Metodo e sistema di ispezione termografica non distruttiva per il rilevamento e la misura di difettosita' in strutture in materiale composito
EP3121579A1 (fr) * 2015-07-22 2017-01-25 Leonardo S.p.A. Procede et systeme d'inspection non destructive thermographique destines a la detection et a la mesure de defauts volumetriques dans des structures en materiau composite
US10444173B2 (en) 2015-07-22 2019-10-15 Leonardo S.P.A. Method and system of thermographic non-destructive inspection for detecting and measuring volumetric defects in composite material structures
CN113884538A (zh) * 2021-10-18 2022-01-04 沈阳工业大学 大型风力机叶片内部微小缺陷的红外热像检测方法
CN115047022A (zh) * 2022-08-11 2022-09-13 合肥锁相光学科技有限公司 一种热扩散过程的时域重构方法及系统
CN115047022B (zh) * 2022-08-11 2022-11-08 合肥锁相光学科技有限公司 一种热扩散过程的时域重构方法及系统

Similar Documents

Publication Publication Date Title
US6751342B2 (en) System for generating thermographic images using thermographic signal reconstruction
US7724925B2 (en) System for generating thermographic images using thermographic signal reconstruction
JP4467862B2 (ja) 圧縮された画像データを使用して参照なしでサーモグラフィックに表面近傍の欠陥を検出するシステム及び方法
US8692887B2 (en) Thermal imaging method and apparatus for evaluating coatings
US6367969B1 (en) Synthetic reference thermal imaging method
EP1173724B1 (fr) Mesure thermographique transitoire de l'epaisseur d'une couche metallique
KR100464737B1 (ko) 대상물내의결함검출방법및장치
US8393784B2 (en) Characterization of flaws in composites identified by thermography
US7591583B2 (en) Transient defect detection algorithm
US7822268B2 (en) Advanced processing of active thermography signals
US6367968B1 (en) Thermal resonance imaging method
US8055054B2 (en) Method and apparatus for thermographic nondestructive evaluation of an object
WO2002089042A1 (fr) Systeme pour generer des images thermographiques en utilisant la reconstruction de signaux thermographiques
US7060991B2 (en) Method and apparatus for the portable identification of material thickness and defects along uneven surfaces using spatially controlled heat application
Revel et al. Defect detection in ceramic materials by quantitative infrared thermography
JP4517044B2 (ja) 欠陥検査方法およびその装置
JP2003083923A6 (ja) 欠陥検査方法およびその装置

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): JP US

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR

121 Ep: the epo has been informed by wipo that ep was designated in this application
122 Ep: pct application non-entry in european phase
NENP Non-entry into the national phase

Ref country code: JP

WWW Wipo information: withdrawn in national office

Country of ref document: JP