Device and method for detecting thickness of modified waterproof coating

Abstract

The invention relates to the technical field of thickness measurement, in particular to a modified waterproof coating thickness detection device and method, and the method comprises the steps: obtaining the ultrasonic data and vibration data of each position of a modified waterproof coating, and obtaining the disturbance correlation degree of the vibration data at each position and the ultrasonic purity coefficient of the ultrasonic data at each position; analyzing the local ultrasonic data difference degree of each significant extreme value, and obtaining the ultrasonic disorder coefficient of the ultrasonic data at each position according to the similarity degree between each significant extreme value and the significant extreme values of other regions about the local ultrasonic data, so as to adjust the filtering window size of the ultrasonic data at each position, and obtain the ultrasonic disorder coefficient of the ultrasonic data at each position. And on the basis of the filtered ultrasonic data, the aliasing phenomenon of the ultrasonic data at each position is judged, and then the coating thickness is detected. According to the invention, the accuracy of waterproof coating thickness detection can be improved.

Description

Technical Field

[0001] This application relates to the field of thickness measurement technology, specifically to a device and method for detecting the thickness of modified waterproof coatings. Background Technology

[0002] In the field of construction engineering, modified waterproof coatings are widely used due to their excellent waterproof performance and weather resistance. However, when applying modified waterproof coatings to objects to achieve a waterproof effect, if the coating is too thin, it is prone to waterproofing failure; if the coating is too thick, the waterproof layer is prone to cracking due to the physical effects of thermal expansion and contraction, thus affecting the waterproofing effect.

[0003] Existing technologies typically employ ultrasonic testing to measure the thickness of waterproof coatings. However, since waterproof coatings are generally thin, and their thickness may vary at different locations, the acquired ultrasonic data may exhibit signal aliasing. Current ultrasonic data processing techniques do not adequately consider the coupling effect of external interference with signal aliasing characteristics, potentially confusing noise interference with signal aliasing, thus affecting the accuracy of subsequent thickness measurements and resulting in poor waterproof coating thickness detection. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a modified waterproof coating thickness detection device and method, the specific technical solution of which is as follows: This application provides a method for detecting the thickness of a modified waterproof coating, including the following steps: Acquire ultrasonic and vibration data at various locations of the modified waterproof coating, and assemble ultrasonic and vibration sequences for each location; By analyzing the dispersion of vibration data changes and ultrasonic data changes at each location, and combining the correlation between vibration and ultrasonic data changes, the disturbance correlation of vibration data at each location is obtained. The significant extreme values ​​of ultrasound data at each location are extracted based on the differences in the distribution of extreme values ​​in the ultrasound sequences at each location. The ultrasound purity coefficient of the ultrasound data at each location is obtained based on the difference between the distribution range of significant extreme values ​​and the amount of data in the ultrasound sequence, as well as the significance of the largest peak value among the significant extreme values. The degree of difference in local ultrasound data for each significant extreme value is analyzed, and the similarity between each significant extreme value and other significant extreme values ​​in terms of local ultrasound data is obtained by combining the perturbation correlation of vibration data at each location and the ultrasound purity coefficient of ultrasound data. The filter window size of the ultrasonic data at each location is adjusted according to the ultrasonic disturbance coefficient to filter the ultrasonic data. Based on the filtered ultrasonic data, the aliasing phenomenon of the ultrasonic data at each location is judged, and then the coating thickness is detected.

[0005] Preferably, vibration mutation points in the vibration sequence at each position are detected, and each ultrasound mutation point in the ultrasound sequence is extracted using the positional order of each vibration mutation point. A window is constructed with each ultrasound mutation point as the center, and the significance of the mutation in the data within the window is obtained by using the sliding t-test algorithm and recorded as the mutation index of each ultrasound mutation point. Based on the dispersion of the mutation index and the dispersion of the vibration data corresponding to the vibration mutation point, the first ratio at each location is calculated. Then, the first correlation value at each location is calculated based on the correlation between the vibration data corresponding to the vibration mutation point and the mutation index of the ultrasonic mutation point. Therefore, the method for calculating the disturbance correlation degree of the vibration data at each location is as follows: In the formula, Let be the disturbance correlation degree of the vibration data at the i-th position. The first ratio at the i-th position; This is the first correlation value at the i-th position.

[0006] Preferably, the dispersion a1 of all mutation indices and the dispersion a2 of vibration data corresponding to all vibration mutation points are statistically analyzed, and the ratio of a1 to a2 is used as the first ratio for each position; and the correlation coefficient between the vibration data corresponding to the vibration mutation point and the mutation index of the ultrasonic mutation point is calculated as the first correlation value for each position.

[0007] Preferably, the extreme values ​​in the ultrasound sequence at each location are obtained, the absolute difference between each extreme value and the previous ultrasound data is calculated, and all absolute differences are thresholded, with the extreme values ​​whose absolute difference is greater than the threshold being taken as significant extreme values. The range between all significant extreme values ​​is calculated, and the ratio of this range to the length of the ultrasound sequence data is used as the second ratio for ultrasound sequences at each location. The ratio of the largest peak value among the significant extreme values ​​to the sum of all other peak values ​​is used as the third ratio for ultrasound sequences at each location.

[0008] Preferably, the method for calculating the ultrasonic purity coefficient of the ultrasonic data at each location is as follows: In the formula, Let be the ultrasonic purity coefficient of the ultrasonic data at the i-th position; This is the second ratio of the ultrasound sequence at the i-th position; The number of all significant extreme values ​​in the ultrasound sequence at the i-th position; is the third ratio of the ultrasound sequence at the i-th position.

[0009] Preferably, the method for calculating the ultrasonic disturbance coefficient of the ultrasonic data at each location is as follows: In the formula, Let F be the mean of all significant extreme values ​​in the ultrasound sequence at position i; The degree of perturbation correlation of the vibration data at the i-th position; The mean of the first similarity of all significant extreme values ​​in the ultrasound sequence at the i-th position; Let be the ultrasonic purity coefficient of the ultrasonic data at the i-th position; To avoid constants with a denominator of 0.

[0010] Preferably, a window is constructed with each significant extreme value in the ultrasound sequence as the center. The center element of the window divides the window data into two parts. The F test result between the two parts of the data is obtained through F test and used as the F value of each significant extreme value. The average of the absolute values ​​of the similarity between each significant extreme value and other significant extreme values ​​of the window data is calculated and recorded as the first similarity of each significant extreme value.

[0011] Preferably, the method for calculating the approximate value of the ultrasonic data filtering window at each location is as follows: In the formula, This is an approximate value of the ultrasonic data filtering window at the i-th position; This is the rounding function; T is the preset window size; This is the normalization function; Let be the ultrasound disturbance coefficient of the ultrasound data at the i-th location; where is the closest to . The odd number is used as the filter window size for the ultrasound data at the i-th position.

[0012] Preferably, after filtering the ultrasonic data at each location, the ultrasonic purity coefficient corresponding to the ultrasonic data at each location is re-acquired and normalized. If the normalized ultrasonic purity coefficient is greater than or equal to a preset threshold, then the ultrasonic data at the corresponding location does not exhibit aliasing, and the coating thickness is measured based on the ultrasonic data. Otherwise, it is determined that the ultrasonic data exhibits aliasing, and the aliasing problem is resolved by using the acoustic pressure reflection coefficient amplitude spectrum analysis method, and the coating thickness is measured.

[0013] This application also provides a modified waterproof coating thickness detection device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described modified waterproof coating thickness detection methods.

[0014] As can be seen from the above, the modified waterproof coating thickness detection device and method provided in this application have at least the following beneficial effects: This application constructs a perturbation correlation coefficient to reflect the perturbation effect of vibration data on ultrasonic data, constructs an ultrasonic purity coefficient to reflect the purity of ultrasonic data and its potential impact from noise or signal aliasing interference, and further obtains an ultrasonic disturbance coefficient, thereby effectively distinguishing the characteristic differences between noise interference and signal aliasing, so as to determine the degree of disorder of ultrasonic data caused by external interference. To address the problem that existing technologies do not fully consider the coupling effect of external interference and signal aliasing, which leads to confusion between noise interference and signal aliasing, this application constructs an ultrasonic disturbance coefficient and comprehensively evaluates the local regularity and consistency of changes in ultrasonic data. This enables a more accurate assessment of the interference affecting ultrasonic data, thereby improving the accuracy of waterproof coating thickness detection. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a modified waterproof coating thickness detection method provided in this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a modified waterproof coating thickness detection device and method proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the modified waterproof coating thickness detection device and method provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a modified waterproof coating thickness detection method according to an embodiment of this application, including the following steps: Step 1: Obtain ultrasonic and vibration data at various locations of the modified waterproof coating, and assemble ultrasonic and vibration sequences for each location.

[0021] This application applies to scenarios involving thickness testing of waterproof coatings on surfaces such as roads, rooftops, and basement walls. This embodiment uses basement walls as an example for analysis. Details are as follows: An ultrasonic thickness gauge is a device for detecting the thickness of modified waterproof coatings. It consists of a probe, transmitting and receiving circuits, and a processing unit. It detects the thickness of the waterproof coating by calculating the time difference between the transmitted wave and the echo.

[0022] An ultrasonic thickness gauge probe is attached to the surface of the waterproof coating using a coupling agent to collect ultrasonic data at a frequency of 10 MHz. Due to the large wall area, in this embodiment, the wall is divided into nine identical rectangles, and the thickness of the waterproof coating at the center of each rectangle is measured. The coupling agent is not limited to water or glycerin; water is used in this embodiment.

[0023] During ultrasonic data acquisition, a vibration acceleration sensor is placed within the same rectangle of the thickness gauge to collect vibration data of the waterproof coating wall. The acquisition frequency is 1KHz, and the acquisition time for each acquisition is consistent with the acquisition time of the thickness gauge.

[0024] Let's take the i-th position on the wall as an example for analysis. Since the collected signal contains too much data, it will affect the calculation efficiency. Therefore, the ultrasonic data and vibration data collected at the i-th position are sampled. In this embodiment, the number of samples is 1000. The sampled data at each position are arranged in time sequence to construct the ultrasonic sequence and vibration sequence of each position.

[0025] To eliminate the influence of dimensions between data, all data are normalized. Normalization methods include Z-score, maximum value normalization, and maximum-minimum value normalization. This embodiment uses maximum-minimum value normalization, and the specific process is existing technology, which will not be described in detail in this embodiment.

[0026] Step 2: By analyzing the dispersion of vibration data changes and ultrasonic data changes at each location, and combining the correlation between vibration and ultrasonic data changes, the disturbance correlation of vibration data at each location is obtained.

[0027] During the overall construction process, the application of modified waterproof coatings is often not fully completed. Therefore, when the waterproof coating is applied and its thickness is measured, structural vibrations generated by construction machinery, vehicle and personnel movement, and ventilation systems can affect the accuracy of ultrasonic signal acquisition, resulting in significant errors in the ultrasonic data. Therefore, the impact of wall vibrations during ultrasonic data acquisition can be initially assessed to reflect the interference with the ultrasonic data.

[0028] A mutation point detection algorithm is used to obtain all vibration mutation points in the vibration sequence. Based on the position of the vibration mutation points in the vibration sequence, the corresponding ultrasound mutation points are located in the ultrasound sequence. A window of size 1×M is constructed with each ultrasound mutation point as the center. In this embodiment, M is set to 9. A sliding t-test algorithm is used to output the significance of the mutation (the absolute value of the t-value) of the data within the window centered on each ultrasound mutation point, which is denoted as the mutation index of each ultrasound mutation point. Compared with the element values ​​of ultrasound mutation points, the mutation index can more accurately reflect the significance of local changes in ultrasound data, avoiding misjudgments of the degree of signal mutation due to noise or random fluctuations.

[0029] It should be noted that when the center of the window is near the ends of the sequence, causing the window to exceed the sequence range, the portion of the window that exceeds the sequence range will be filled with the mean of the data in the window that does not exceed the sequence range.

[0030] Furthermore, for the i-th position, the dispersion a1 between all mutation indices and the dispersion a2 between the vibration data corresponding to all vibration mutation points are calculated respectively; the ratio of dispersion a1 to a2 is recorded as the first ratio at the i-th position. The first ratio reflects the degree of interference of vibration data on ultrasound data; the larger the value, the greater the interference of vibration data on ultrasound data, indicating that even small changes in vibration data can cause significant fluctuations in ultrasound data. The calculation of dispersion is not limited to variance, coefficient of variation, or root mean square error; this embodiment uses variance calculation.

[0031] Simultaneously, the correlation coefficient between the element values ​​of the vibration mutation point and the mutation index of the ultrasonic mutation point is calculated using a correlation coefficient algorithm, denoted as the first correlation value at the i-th position. This first correlation value reflects whether the mutation index of the ultrasonic mutation changes with the element values ​​of the vibration mutation point. A larger value indicates a greater likelihood that the ultrasonic data at the corresponding moment also undergoes a drastic change when the vibration mutation point changes drastically; a smaller value, or even a negative value, reflects a smaller interference from vibration factors on the ultrasonic data.

[0032] The mutation point detection algorithm is not limited to PELT, Pettitt, or MK algorithms; this embodiment uses the PELT algorithm. The calculation of the correlation coefficient is not limited to Pearson or Spearman correlation coefficients; this embodiment uses the Pearson correlation coefficient.

[0033] Therefore, in this embodiment, based on the first ratio and the first correlation value, the disturbance correlation degree of the vibration data at the i-th position is constructed. The specific calculation relationship is as follows: In the formula, Let be the disturbance correlation degree of the vibration data at the i-th position. The first ratio at the i-th position; This is the first correlation value at the i-th position.

[0034] Among them, the disturbance correlation degree can reflect the disturbance effect of vibration factors on the acquired ultrasonic data at the i-th position; the larger the value, the more significant the influence of structural vibration on the ultrasonic data, which leads to a larger error in the ultrasonic data acquired at this position and a lower accuracy in detecting the thickness of the waterproof coating.

[0035] Step 3: Extract the significant extreme values ​​of ultrasound data at each location based on the differences in the distribution of extreme values ​​in the ultrasound sequences at each location. Based on the differences in the distribution range of significant extreme values ​​and the amount of data in the ultrasound sequences, as well as the significance of the largest peak value among the significant extreme values, obtain the ultrasound purity coefficient of the ultrasound data at each location.

[0036] Furthermore, since waterproof coatings are typically very thin, and the thickness may vary at different locations, signal aliasing may occur in the acquired ultrasonic data. If signal aliasing occurs, it will result in numerous peaks and troughs in the ultrasonic data, potentially leading to errors in the calculated perturbation correlation. Therefore, further analysis of the original signal to determine if signal aliasing has occurred is necessary to further assess the degree of interference to the ultrasonic data.

[0037] If no signal aliasing occurs and the vibration impact is minimal, the acquired signal will be a standard ultrasound data waveform, resulting in fewer peaks and valleys that are densely distributed. However, if the ultrasound data is subject to significant noise interference or signal aliasing, the peaks and valleys will increase significantly and be distributed over a wider range.

[0038] However, considering that even under normal circumstances, ultrasonic data may exhibit minute peaks and valleys due to the internal inhomogeneity of the material, further processing of the detected extreme values ​​is necessary. For the i-th position, an extreme value detection algorithm is used to obtain all extreme values ​​in the ultrasonic sequence at the i-th position. The absolute difference between each extreme value in the ultrasonic sequence and its preceding ultrasonic data is then obtained. All absolute differences are used as input to the Otsu thresholding method to obtain a segmentation threshold. Extreme values ​​with an absolute difference greater than the segmentation threshold are recorded as significant extreme values.

[0039] Then, the range between all significant extrema positions is calculated, and the ratio of the range to the data length of the ultrasound sequence is denoted as the second ratio of the ultrasound sequence at the i-th position. The range between positions reflects the maximum range of the significant extrema distribution, while the second ratio reflects the span of the significant extrema distribution in the ultrasound sequence. The smaller the value, the greater the probability that the significant extrema are concentrated in the true echo region.

[0040] Furthermore, since pure ultrasound data should have a very significant main echo, the purity of the ultrasound data can be assessed by analyzing the significance of the main echo.

[0041] Since significant extrema include both peaks and troughs, all peaks are extracted from the significant extrema, and the ratio of the maximum value among all peaks to the sum of all other peaks is calculated, denoted as the third ratio of the ultrasound sequence at the i-th position. This third ratio reflects the significance of the main echo in the ultrasound data; a larger value indicates that the energy in the ultrasound data is more concentrated on the main echo, and thus indicates a higher degree of signal purity.

[0042] Construct the ultrasonic purity coefficient of the ultrasonic data at the i-th location. In this embodiment, the specific calculation relationship is as follows: In the formula, This is the second ratio of the ultrasound sequence at the i-th position; The number of all significant extreme values ​​in the ultrasound sequence at the i-th position; is the third ratio of the ultrasound sequence at the i-th position.

[0043] The ultrasonic purity coefficient reflects the purity of the ultrasonic data acquired at the i-th location. A larger value indicates purer ultrasonic data and less noise interference. Conversely, a smaller value indicates a greater likelihood that the ultrasonic data acquired at the i-th location may experience significant signal fluctuations due to severe noise interference or signal aliasing. In such cases, directly measuring the thickness based on the ultrasonic data would result in a large measurement error, thus requiring further analysis and processing.

[0044] Step 4: Analyze the degree of difference in local ultrasound data for each significant extreme value, and obtain the ultrasound disturbance coefficient of ultrasound data at each location by combining the perturbation correlation of vibration data at each location and the ultrasound purity coefficient of ultrasound data through the similarity between each significant extreme value and the significant extreme values ​​of other regions with respect to local ultrasound data.

[0045] Although both noise and signal aliasing can cause significant fluctuations in ultrasound data, noise causes disordered and irregular random fluctuations in ultrasound data; while signal aliasing is formed by the superposition of multiple echoes in the time domain. Although the waveform is complex, it has certain regularities, such as relatively smooth and consistent local data changes on both sides of the extreme values, and similar data change trends at each extreme value. Therefore, noise and signal aliasing can be further distinguished based on the data characteristics represented by the ultrasound data.

[0046] Centered on the u-th significant extreme value in the ultrasound sequence, a window of size 1×M is constructed. The window data is divided into two parts based on the central element. An F-test is then performed to calculate the difference between the two parts, assessing the degree of difference between the two sets of data. The F-test result is denoted as the F-value of the u-th significant extreme value. The F-value reflects whether there is a significant difference in the local data on both sides of the u-th extreme value. A larger F-value indicates a greater difference between the data on both sides of the u-th extreme value, and it is less likely to deviate from the characteristic of local smoothness and uniformity of signal aliasing, suggesting that the u-th extreme value is caused by interference.

[0047] Furthermore, the average absolute value of the similarity between the window data of the u-th significant extremum and the window data of all other significant extremums is calculated using a data similarity analysis algorithm and denoted as the first similarity of the u-th significant extremum. The first similarity reflects the waveform similarity between the u-th significant extremum and the other significant extrema; the smaller the value, the greater the difference in the data change trend between the u-th significant extremum and the other extrema. The data similarity analysis algorithm is not limited to cosine similarity or mutual information; this embodiment uses cosine similarity.

[0048] Therefore, the ultrasonic disturbance coefficient of the ultrasonic data at the i-th location is constructed. In this embodiment, the specific calculation relationship is as follows: In the formula, Let F be the mean of all significant extreme values ​​in the ultrasound sequence at position i; The degree of perturbation correlation of the vibration data at the i-th position; The mean of the first similarity of all significant extreme values ​​in the ultrasound sequence at the i-th position; Let be the ultrasonic purity coefficient of the ultrasonic data at the i-th position; To avoid constants with a denominator of 0, the value range is (0.001, 0.01). This is intended to avoid the situation where the denominator is zero. The value has little impact on the calculation and can be ignored. The implementer can choose the value as they see fit. In this embodiment, the value is 0.002.

[0049] The ultrasonic disturbance coefficient reflects the degree of disorder in the ultrasonic data acquired at the i-th location caused by noise or other external interference. A larger value indicates stronger local irregularity in the ultrasonic data, greater overall disturbance, and thus, greater interference affecting the ultrasonic data and lower reliability.

[0050] Step 5: Adjust the filter window size of the ultrasonic data at each location according to the ultrasonic disturbance coefficient to filter the ultrasonic data. Based on the filtered ultrasonic data, determine the aliasing phenomenon of the ultrasonic data at each location, and then detect the coating thickness.

[0051] According to the above process in this embodiment, the ultrasonic disturbance coefficients of the ultrasonic data at each location can be obtained. Furthermore, this embodiment uses the Savitzky-Golay filtering algorithm to filter the ultrasonic data. If the ultrasonic data is less affected by interference and the ultrasonic data is purer, then the filtering window needs to be smaller to avoid overfitting and filtering out signal features; if the interference is greater and the error is more severe, then a larger window is needed to filter out the interference.

[0052] Therefore, in this embodiment, the window size for filtering the ultrasound data at each location is adjusted according to the ultrasound disturbance coefficient at each location. Specifically, in this embodiment, the calculation method for the ultrasound data filtering window size at each location is as follows: In the formula, This is an approximate value of the ultrasonic data filtering window at the i-th position; This is the rounding function; T is the preset window size, which is 7 in this embodiment. The normalization function is not limited to the tanh function or the sigmoid function; this embodiment uses the sigmoid function.

[0053] It should be noted that, since the filtering window needs to be odd, in this embodiment, the closest... The odd number is used as the filtering window size for the ultrasound data at the i-th position. Furthermore, the polynomial order is set to 3 so that the ultrasound data at the i-th position can be filtered using the Savitzky-Golay filtering algorithm.

[0054] Furthermore, after filtering the ultrasound data collected at the i-th position, the ultrasound purity coefficient is calculated again for the ultrasound data at the i-th position. According to the above process in this embodiment, the ultrasound purity coefficient corresponding to each position after ultrasound filtering can be obtained, and the ultrasound purity coefficient corresponding to the ultrasound filtering is normalized by the sigmoid function.

[0055] Therefore, if the normalized ultrasonic purity coefficient is greater than or equal to the preset threshold of 0.75, it is determined that no signal aliasing has occurred in the ultrasonic data at the i-th position. In this case, the ultrasonic transit time method is directly used and the coating thickness is measured based on the ultrasonic data. The specific process is a well-known prior art and will not be described in detail in this embodiment. If the normalized ultrasonic purity coefficient is less than the preset threshold of 0.75, it is determined that aliasing has occurred in the ultrasonic data. In this case, the aliasing problem is solved by the acoustic pressure reflection coefficient amplitude spectrum analysis method (URCAS) and the coating thickness is accurately measured.

[0056] The formula for calculating the ultrasonic transit time method is as follows: ; The calculation formula for the URCAS algorithm is as follows: ; Among them, the ultrasonic velocity in the material and the time difference of ultrasonic round trip can be directly obtained during the detection of the thickness of the waterproof coating; the calculation of the resonant frequency interval, the ultrasonic transit time method, and the URCAS algorithm are all well-known technologies and will not be elaborated here.

[0057] In this embodiment, the ultrasonic data collected at various locations of the waterproof coating are used to measure the thickness and assess whether it is within the set thickness range, thereby achieving accurate detection of the thickness of the waterproof coating.

[0058] Based on the same inventive concept as the above method, this application embodiment also provides a modified waterproof coating thickness detection device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described modified waterproof coating thickness detection methods.

[0059] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0060] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0061] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A method for detecting the thickness of a modified waterproof coating, characterized in that, Includes the following steps: Acquire ultrasonic and vibration data at various locations of the modified waterproof coating, and assemble ultrasonic and vibration sequences for each location; By analyzing the dispersion of vibration data changes and ultrasonic data changes at each location, and combining the correlation between vibration and ultrasonic data changes, the disturbance correlation of vibration data at each location is obtained. The significant extreme values ​​of ultrasound data at each location are extracted based on the differences in the distribution of extreme values ​​in the ultrasound sequences at each location. The ultrasound purity coefficient of the ultrasound data at each location is obtained based on the difference between the distribution range of significant extreme values ​​and the amount of data in the ultrasound sequence, as well as the significance of the largest peak value among the significant extreme values. The degree of difference in local ultrasound data for each significant extreme value is analyzed, and the similarity between each significant extreme value and other significant extreme values ​​in terms of local ultrasound data is obtained by combining the perturbation correlation of vibration data at each location and the ultrasound purity coefficient of ultrasound data. The filter window size of the ultrasonic data at each location is adjusted according to the ultrasonic disturbance coefficient to filter the ultrasonic data. Based on the filtered ultrasonic data, the aliasing phenomenon of the ultrasonic data at each location is judged, and then the coating thickness is detected.

2. The method for detecting the thickness of a modified waterproof coating as described in claim 1, characterized in that, Vibration mutation points in the vibration sequence at each location are detected. The positional order of each vibration mutation point is used to extract each ultrasound mutation point in the ultrasound sequence. A window is constructed with each ultrasound mutation point as the center. The sliding t-test algorithm is used to obtain the significance of the mutation in the data within the window, which is recorded as the mutation index of each ultrasound mutation point. Based on the dispersion of the mutation index and the dispersion of the vibration data corresponding to the vibration mutation point, the first ratio at each location is calculated. Then, the first correlation value at each location is calculated based on the correlation between the vibration data corresponding to the vibration mutation point and the mutation index of the ultrasonic mutation point. Therefore, the method for calculating the disturbance correlation degree of the vibration data at each location is as follows: In the formula, Let be the disturbance correlation degree of the vibration data at the i-th position. The first ratio at the i-th position; This is the first correlation value at the i-th position.

3. The method for detecting the thickness of a modified waterproof coating as described in claim 2, characterized in that, The dispersion a1 of all mutation indices and the dispersion a2 of vibration data corresponding to all vibration mutation points are statistically analyzed. The ratio of a1 to a2 is taken as the first ratio at each location. The correlation coefficient between the vibration data corresponding to the vibration mutation point and the mutation index of the ultrasonic mutation point is calculated and taken as the first correlation value at each location.

4. The method for detecting the thickness of a modified waterproof coating as described in claim 1, characterized in that, The extreme values ​​in the ultrasound sequences at each location are obtained, the absolute difference between each extreme value and the previous ultrasound data is calculated, and all absolute differences are thresholded. The extreme values ​​with absolute differences greater than the threshold are taken as significant extreme values. The range between all significant extreme values ​​is calculated, and the ratio of this range to the length of the ultrasound sequence data is used as the second ratio for ultrasound sequences at each location. The ratio of the largest peak value among the significant extreme values ​​to the sum of all other peak values ​​is used as the third ratio for ultrasound sequences at each location.

5. The method for detecting the thickness of a modified waterproof coating as described in claim 4, characterized in that, The method for calculating the ultrasonic purity coefficient of the ultrasonic data at each location is as follows: In the formula, Let be the ultrasonic purity coefficient of the ultrasonic data at the i-th position; This is the second ratio of the ultrasound sequence at the i-th position; The number of all significant extreme values ​​in the ultrasound sequence at the i-th position; is the third ratio of the ultrasound sequence at the i-th position.

6. The method for detecting the thickness of a modified waterproof coating as described in claim 1, characterized in that, The method for calculating the ultrasonic disturbance coefficient of the ultrasonic data at each location is as follows: In the formula, Let F be the mean of all significant extreme values ​​in the ultrasound sequence at position i; The degree of perturbation correlation of the vibration data at the i-th position; The mean of the first similarity of all significant extreme values ​​in the ultrasound sequence at the i-th position; Let be the ultrasonic purity coefficient of the ultrasonic data at the i-th position; To avoid constants with a denominator of 0.

7. The method for detecting the thickness of a modified waterproof coating as described in claim 6, characterized in that, A window is constructed with each significant extreme value in the ultrasound sequence as the center. The center element of the window divides the window data into two parts. The F test result between the two parts of the data is obtained through F test and used as the F value of each significant extreme value. The average of the absolute values ​​of the similarity between each significant extreme value and other significant extreme values ​​of the window data is recorded as the first similarity of each significant extreme value.

8. The method for detecting the thickness of a modified waterproof coating as described in claim 1, characterized in that, The method for calculating the approximate value of the ultrasonic data filtering window at each location is as follows: In the formula, This is an approximate value of the ultrasonic data filtering window at the i-th position; This is the rounding function; T is the preset window size; This is the normalization function; Let be the ultrasound disturbance coefficient of the ultrasound data at the i-th location; where is the closest to . The odd number is used as the filter window size for the ultrasound data at the i-th position.

9. The method for detecting the thickness of a modified waterproof coating as described in claim 1, characterized in that, After filtering the ultrasonic data at each location, the ultrasonic purity coefficient corresponding to the ultrasonic data at each location is re-acquired and normalized. If the normalized ultrasonic purity coefficient is greater than or equal to the preset threshold, then the ultrasonic data at the corresponding location does not exhibit aliasing, and the coating thickness is measured based on the ultrasonic data. Otherwise, it is determined that the ultrasonic data exhibits aliasing, and the aliasing problem is resolved by using the acoustic pressure reflection coefficient amplitude spectrum analysis method, and the coating thickness is measured.

10. A modified waterproof coating thickness detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the modified waterproof coating thickness detection method as described in any one of claims 1-9.

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