Method and system for detecting wall thickness of air conditioner manifold

Abstract

The application relates to the technical field of thickness detection, in particular to a wall thickness detection method and system for an air conditioner branch pipe, which comprises the following steps: obtaining echo signals received by an ultrasonic thickness gauge at preset positions on the air conditioner branch pipe and wall thickness data of the positions; obtaining a wave peak distribution coefficient of each position by the distribution of wave peaks in the echo signals of the positions; obtaining a wave peak difference coefficient and a comprehensive distribution coefficient of each position; obtaining an irregular coefficient of each position by the discrete degree of skewness of all echo components in the echo signals of the positions, and obtaining an influence factor of each position; dividing all positions into intervals, obtaining a local wall thickness difference coefficient of each position by the difference between the wall thickness data of each position and the wall thickness data of the other positions in the interval where each position is located; obtaining a wall thickness deviation degree of each position; and correcting the wall thickness data of each position. The application aims to improve the precision of wall thickness detection of the branch pipe.

Description

Technical Field

[0001] This application relates to the field of thickness detection technology, specifically to a method and system for detecting the wall thickness of air conditioning branch pipes. Background Technology

[0002] The air conditioning branch pipe is a crucial component connecting the outdoor and indoor units of an air conditioner. It is typically made of copper tubing and has a "Y" or "T" shaped structure. Its main function is to distribute refrigerant from the outdoor unit to each indoor unit, or to collect refrigerant from multiple indoor units and return it to the outdoor unit. Thickness is a key factor affecting the strength of the branch pipe. If the thickness is insufficient, the branch pipe is prone to cracking and leakage under high-pressure refrigerant pressure and during long-term use. Therefore, it is essential to test the thickness of the air conditioning branch pipe.

[0003] Currently, ultrasonic thickness measurement is commonly used to measure the wall thickness of manifolds. However, manifolds consist of a main pipe and multiple branch pipes, each branch pipe being composed of several straight and curved pipes connected together. Furthermore, the connections between these pipes have bevels, making it difficult to maintain a perpendicular relationship between the beveled surface and the bends and the thickness measuring probe during the measurement process. This results in poor coupling. Additionally, the echo signal acquired by the ultrasonic thickness gauge may exhibit irregular waveforms due to the unevenness of the manifold's inner wall, easily affecting the accuracy of ultrasonic thickness measurement. Existing methods fail to adequately consider the coupling effect between the probe and the workpiece, as well as the influence of inner wall unevenness, leading to insufficient accuracy in measuring the wall thickness of air conditioning manifolds. Summary of the Invention

[0004] In view of the above, it is necessary to provide a method and system for detecting the wall thickness of air conditioning branch pipes, which improves the accuracy of wall thickness detection compared to traditional methods for detecting the wall thickness of air conditioning branch pipes.

[0005] In a first aspect, embodiments of this application provide a method for detecting the wall thickness of an air conditioning branch pipe, the method comprising the following steps:

[0006] Acquire the echo signals received by the ultrasonic thickness gauge at each preset position on the air conditioning branch pipe, as well as the wall thickness data at each position;

[0007] By analyzing the temporal distribution of peaks in the echo signals at each location, the peak distribution coefficient for each location is obtained; the fitted straight line of all peaks in each echo signal is obtained, and the peak difference coefficient for each location is obtained by analyzing the difference between each peak in the echo signal at each location and its fitted value; the comprehensive distribution coefficient for each location is obtained by combining the peak distribution coefficient and the peak difference coefficient.

[0008] The irregularity coefficient of each location is obtained by measuring the dispersion of the skewness of all echo components in the echo signal at each location. The influence factor of each location is obtained by combining the correlation between the echo signals of each location and its neighboring locations, as well as the comprehensive distribution coefficient.

[0009] By analyzing the abrupt changes in wall thickness data at all locations, all locations are divided into intervals. The local wall thickness difference coefficient is obtained for each location based on the difference in wall thickness data between each location and the other locations within its interval. The wall thickness deviation at each location is obtained by combining the influencing factor with the local wall thickness difference coefficient.

[0010] By combining the numerical distribution range of wall thickness data at all locations within the interval where each location is located with the wall thickness deviation, the wall thickness data at each location is corrected.

[0011] In one embodiment, the peak distribution coefficient is the dispersion of the time interval between any two adjacent peaks in the echo signal at each location.

[0012] In one embodiment, the process of obtaining the peak difference coefficient is as follows:

[0013] Calculate the difference between each peak in the echo signal at each location and its fitted value;

[0014] The peak difference coefficient is the arithmetic mean of the difference values ​​corresponding to all peaks in the echo signal at each location.

[0015] In one embodiment, the comprehensive distribution coefficient is the product of the peak distribution coefficient and the peak difference coefficient.

[0016] In one embodiment, the irregularity coefficient is the dispersion of the skewness of all echo components in the echo signal at each location.

[0017] In one embodiment, the process of obtaining the influence factor is as follows:

[0018] Calculate the correlation coefficient between each position and its adjacent preceding and following positions, and calculate the average value of the correlation coefficients between each position and all its adjacent positions; map the average value to a positive number.

[0019] Calculate the product of the comprehensive distribution coefficient and the irregularity coefficient;

[0020] The influence factor is the ratio of the product to the positive number.

[0021] In one embodiment, the process of obtaining the local wall thickness difference coefficient is as follows:

[0022] Calculate the mean value of the wall thickness data at all locations within each interval;

[0023] The local wall thickness difference coefficient is the difference between the wall thickness data at each location and the mean value within its interval.

[0024] In one embodiment, the wall thickness deviation is the product of the influence factor and the local wall thickness difference coefficient.

[0025] In one embodiment, the process of correcting the wall thickness data at each location is as follows:

[0026] Calculate the range of wall thickness data at all locations within each interval;

[0027] Calculate the product of the normalized value of the wall thickness deviation at each location and the range within its interval;

[0028] The difference between the wall thickness data at each location and the product value is used as the corrected wall thickness data at each location.

[0029] Secondly, embodiments of this application also provide a wall thickness detection system for an air conditioning branch pipe, 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 wall thickness detection methods for an air conditioning branch pipe.

[0030] This application has at least the following beneficial effects:

[0031] This application comprehensively evaluates the uniformity and smoothness of the inner wall at various locations of the manifold by analyzing the differences in echo time intervals and the linear decrease in echo peaks as they increase sequentially during ultrasonic testing. This reflects the accuracy of wall thickness data measurement at each location and helps in subsequent correction of the wall thickness data. Furthermore, by analyzing the irregularity of the echo signals at each location and the waveform differences of the echo signals at different locations, an influence factor is calculated to further measure the uniformity and smoothness of the inner wall at each location of the manifold. This improves the accuracy of the evaluation of the uniformity and smoothness of the inner wall at each location, thereby improving the accuracy of the evaluation of the wall thickness data at each location.

[0032] Furthermore, considering the difference in thickness between the connecting and non-connecting parts of the manifold, the manifold is divided into intervals at different locations. Within each interval, the overall difference between the wall thickness data at each location and the wall thickness data of the others is measured. This assesses the degree to which the wall thickness data at each location is affected by poor coupling, improving the accuracy of the analysis of the degree to which the wall thickness data at each location is affected by coupling. Then, combined with the influence factor, the wall thickness deviation is calculated, and the degree to which the wall thickness data at each location deviates from the true data is comprehensively assessed. This is beneficial for correcting the wall thickness data at each location, thereby improving the accuracy of the wall thickness data at each location.

[0033] Furthermore, by utilizing the wall thickness deviation and the numerical distribution range of the wall thickness data within the interval of each location, the wall thickness data at each location is corrected, reducing the impact of inner wall unevenness and coupling effect on the wall thickness data and improving the accuracy of wall thickness detection of the branch pipe. Attached Figure Description

[0034] 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.

[0035] Figure 1 A flowchart illustrating the steps of a wall thickness detection method for an air conditioning branch pipe according to an embodiment of this application;

[0036] Figure 2 This is a schematic diagram illustrating the calculation process for wall thickness deviation.

[0037] Figure 3 This is a schematic diagram illustrating the process of obtaining wall thickness deviation. Detailed Implementation

[0038] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0039] Unless otherwise defined, 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 belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or".

[0040] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0041] The following description, in conjunction with the accompanying drawings, details the specific scheme of the wall thickness detection method and system for air conditioning branch pipes provided in this application.

[0042] Please see Figure 1 The diagram illustrates a flowchart of a wall thickness detection method for an air conditioning branch pipe according to an embodiment of this application. The method includes the following steps:

[0043] Step 1: Obtain the echo signals received by the ultrasonic thickness gauge at each preset position on the air conditioning branch pipe, as well as the wall thickness data at each position.

[0044] During the air conditioning refrigeration cycle, refrigerant flows within the manifold, generating pressure. A suitable wall thickness ensures the manifold can withstand the high refrigerant pressure without cracking or deforming during air conditioning operation. This application uses a pulse-echo ultrasonic thickness gauge as an intelligent sensor to collect wall thickness data of the air conditioning manifold. Before measurement, a coupling agent is evenly applied to the surface of the manifold to ensure good coupling between the probe and the surface of the object being measured. During measurement, the ultrasonic probe moves along the axial direction on the surface of the manifold, collecting wall thickness data at various locations. In this embodiment, the displacement step size for each measurement is set to 3 mm. The principle of the pulse-echo ultrasonic thickness gauge is that the propagation time of ultrasonic waves in a workpiece is proportional to the workpiece thickness; the thickness of the workpiece is determined by the propagation time of the ultrasonic waves. Therefore, the waveform of the echo signal received by the pulse-echo ultrasonic thickness gauge is crucial for the accuracy of the thickness data acquisition. The application acquires the echo signals received by the pulse-echo ultrasonic thickness gauge at preset locations on the air conditioning manifold, as well as the wall thickness data at each location. The 3mm is merely one embodiment of this application, and implementers can set it according to their actual situation. This application does not impose any special restrictions.

[0045] Step 2: By analyzing the echo signals and wall thickness data at each location, assess the degree to which the wall thickness data at each location deviates from the actual data.

[0046] During production, improper mold adjustment or stretching control may result in an uneven inner wall of the manifold, causing irregular waveforms in the echo signal collected by the intelligent sensor, i.e., the pulse-echo ultrasonic thickness gauge. Furthermore, the bevel at the joint of the manifold surface can reduce the coupling effect between the probe and the workpiece being measured, thus affecting the accuracy of the manifold wall thickness detection. Therefore, this application further improves the wall thickness detection accuracy by analyzing the degree of influence of the echo signal and the variation characteristics of the collected wall thickness data during actual testing.

[0047] Step 2.1: Obtain the peak distribution coefficient of each location by analyzing the temporal distribution of peaks in the echo signals at each location; obtain the fitted straight line of all peaks in each echo signal; obtain the peak difference coefficient of each location by analyzing the difference between each peak in the echo signal at each location and its fitted value; and obtain the comprehensive distribution coefficient of each location by combining the peak distribution coefficient and the peak difference coefficient.

[0048] When the inner wall of the manifold is uniformly smooth, the echo spacing of the ultrasonic waves reflected from the inner wall is almost equal, and the amplitude of each echo shows a linear decreasing characteristic. In this case, the calculated wall thickness data is less affected by the waveform. However, when there are severe pitting corrosion or wear defects on the inner wall of the manifold, the reflected echoes from the ultrasonic waves are more disordered, increasing the difference in spacing between adjacent echoes and affecting the linear attenuation characteristics of the peak amplitude. Based on the above analysis, it can be concluded that the roughness of the inner wall of the manifold interferes with the accuracy of wall thickness detection.

[0049] The peaks in the echo signals at each location are obtained. By analyzing the temporal distribution of these peaks, a peak distribution coefficient is calculated for each location. This coefficient reflects the differences in the time intervals of the echoes reflected from the inner wall at each location. Specifically, the dispersion of the time interval between any two adjacent peaks in the echo signals at each location is used as the peak distribution coefficient. A smaller peak distribution coefficient indicates less significant differences in the echo time intervals at each location.

[0050] In this embodiment, an automatic multi-scale peak finding algorithm is used to obtain each peak in the echo signal at each location. The automatic multi-scale peak finding algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, based on the ability to obtain each peak in the echo signal at each location, the implementer may use other existing technologies, such as peak and valley detection algorithms, etc. This application does not impose any special restrictions.

[0051] In this embodiment, the dispersion of the time interval is the standard deviation. As other implementation methods, based on the ability to measure the unevenness of the distribution of time intervals, implementers may use other existing technologies, such as variance, coefficient of variation, etc. This application does not impose any special restrictions.

[0052] Furthermore, fitted straight lines for all peaks in each echo signal are obtained. The peak difference coefficient for each location is obtained by comparing the differences between each peak and its fitted value. This coefficient reflects the significance of the linear decrease in peak size as the echo signal increases sequentially. Specifically, the difference between each peak and its fitted value is calculated, and the arithmetic mean of these differences is taken as the peak difference coefficient. A smaller peak difference coefficient indicates a more significant linear decrease in peak size as the echo signal increases sequentially.

[0053] In this embodiment, the difference between each peak and its fitted value is the absolute value of the difference.

[0054] In this embodiment, the least squares method is used to obtain the fitted straight line of all peaks in each echo signal. The least squares method is a well-known technique and will not be described in detail in this application. As other implementation methods, based on the ability to obtain the fitted straight line of all peaks in each echo signal, the implementer may use other existing techniques, such as the weighted least squares method, etc. This application does not impose any special restrictions.

[0055] Furthermore, by combining the peak distribution coefficient and peak difference coefficient at each location, a comprehensive distribution coefficient is obtained for each location. Specifically, the product of the peak distribution coefficient and peak difference coefficient at each location is taken as the comprehensive distribution coefficient for each location. The larger the comprehensive distribution coefficient, the more significant the difference in echo time intervals at each location, and the less significant the linear decrease in peak values ​​as the echo signal increases sequentially. This suggests that the inner wall at each location may be more uneven, and the wall thickness data at each location may be less accurate.

[0056] Step 2.2: Obtain the irregularity coefficient of each location by measuring the dispersion of the skewness of all echo components in the echo signal at each location. Combine the correlation between the echo signals at each location and its neighboring locations, as well as the comprehensive distribution coefficient, to obtain the influence factor of each location.

[0057] Furthermore, the unevenness of the inner wall of the manifold may lead to irregular shapes in the echo signals received by the smart sensor, and significant differences in the waveforms of the echoes received at different locations. This waveform irregularity manifests in the different offset states of different echo components in the echo signal; for example, some echo components exhibit left-biased waveforms, while others show right-biased waveforms. Based on the above analysis, the skewness of each echo component in the echo signal at each location is calculated, and the dispersion of the skewness of all echo components in the echo signal at each location is used as the irregularity coefficient for each location. The larger the irregularity coefficient, the more irregular the offset state of the echo components in the echo signal. The calculation of the skewness is a well-known technique and will not be elaborated upon in this application.

[0058] In this embodiment, the dispersion of skewness is the standard deviation. As other implementation methods, based on the ability to measure the unevenness of the skewness distribution, implementers may use other existing technologies, such as variance, coefficient of variation, etc. This application does not impose any special restrictions.

[0059] Furthermore, the correlation coefficients between each position and its adjacent preceding and following positions are calculated, and the average value of the correlation coefficients between each position and all its adjacent positions is calculated. The smaller the average value, the greater the waveform difference of the echo signals between each position and its adjacent positions.

[0060] In this embodiment, the correlation coefficient between the echo signals is the Spearman correlation coefficient. The calculation of the Spearman correlation coefficient is a well-known technique and will not be described in detail here. As other implementation methods, based on the ability to calculate the correlation coefficient between the echo signals, the implementer may use other existing techniques, such as Pearson correlation coefficient, Kendall rank correlation coefficient, etc. This application does not impose any special restrictions.

[0061] Furthermore, based on the average value and the comprehensive distribution coefficient and irregularity coefficient at each location, the influence factor for each location is obtained. Specifically, the average value is mapped to a positive number; the product of the comprehensive distribution coefficient and the irregularity coefficient at each location is calculated, and the ratio of the product to the positive number is used as the influence factor for each location. The larger the influence factor, the more uneven the inner wall may be at each location, and the higher the inaccuracy of the wall thickness data at each location may be.

[0062] In this embodiment, the method for mapping the average to a positive number is as follows: the average is used as the exponent of an exponential function with the natural constant as the base.

[0063] In another embodiment, the method for mapping the average to a positive number is as follows: calculate the sum of the average and a preset positive number greater than 1, where the preset positive number greater than 1 is 1.01.

[0064] Step 2.3: Divide all locations into intervals by the abrupt change values ​​in the wall thickness data of all locations, and obtain the local wall thickness difference coefficient of each location by the difference in wall thickness data between each location and the other locations in its interval; obtain the wall thickness deviation of each location by the influence factor and the local wall thickness difference coefficient.

[0065] Because the pipes are connected in a nested manner, the thickness of the connection area is greater than that of other areas. This is because the connection area bears greater stress, including tensile and bending stresses. Thickening the pipe wall improves the tensile, compressive, and fatigue resistance of the connection area. Therefore, when continuous measurements are taken on the surface of the branch pipe, the wall thickness data collected at the connection area is greater than that at the non-connection areas, and there is a sudden change in wall thickness at the endpoints of the connection area.

[0066] Based on the above analysis, the wall thickness data of all locations are arranged in order of location to form a wall thickness data sequence. Each mutation point in the wall thickness data sequence is obtained, and the location of each mutation point is used as a dividing point to divide all locations on the branch pipe into intervals.

[0067] In this embodiment, the Pettitt mutation point detection algorithm is used to obtain each mutation point in the wall thickness data sequence. The Pettitt mutation point detection algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, based on the ability to obtain each mutation point in the wall thickness data sequence, implementers may use other existing technologies, such as the Mann-Kendall mutation point detection algorithm, the Bernaola Galvan segmentation algorithm, etc. This application does not impose any special restrictions.

[0068] The manifold consists of a tee pipe, a front straight pipe, a rear straight pipe, and a bend. There are bevels at the connection points between the different pipes, and varying degrees of bending also exist on the non-connecting surfaces of the tee pipe and bend. During ultrasonic measurement, the probe needs to maintain good contact and a specific angle with the surface of the workpiece being measured. However, when measuring locations with large bevels or bends, the coupling effect between the probe and the manifold surface is reduced, leading to significant deviations in wall thickness data at certain locations. The greater the difference in wall thickness data between each location and other locations within each interval, the greater the influence of the coupling effect during wall thickness measurement at each location.

[0069] Based on the above analysis, by analyzing the difference in wall thickness data between each location and the other locations within its interval, a local wall thickness difference coefficient is obtained for each location. This coefficient reflects the overall difference in wall thickness data between each location and the other locations within its interval. Specifically, the mean value of wall thickness data for all locations within each interval is calculated; the difference between the wall thickness data at each location and the mean value within its interval is used as the local wall thickness difference coefficient for each location. The larger the local wall thickness difference coefficient, the greater the influence of poor coupling effect on the wall thickness data at each location.

[0070] In this embodiment, the difference between the wall thickness data at each location and the mean value within its interval is the absolute value of the difference.

[0071] Furthermore, when measuring the wall thickness at any location on the manifold, the more uneven the inner wall at that location and the worse the coupling effect caused by the curvature, the more the measured wall thickness at that location deviates from the true wall thickness data. Therefore, the wall thickness deviation at each location is obtained by using the influence factor and the local wall thickness difference coefficient. Specifically, the product of the influence factor and the local wall thickness difference coefficient at each location is taken as the wall thickness deviation at each location. The larger the wall thickness deviation, the greater the degree to which the measured wall thickness data at each location deviates from the true value due to the influence of the unevenness of the inner wall and the coupling effect. A schematic diagram of the calculation process for the wall thickness deviation is shown below. Figure 2 As shown in the diagram. A schematic diagram of the process for obtaining wall thickness deviation is shown below. Figure 3 As shown.

[0072] Step 3: Correct the wall thickness data at each location by combining the numerical distribution range of the wall thickness data at all locations within the interval where each location is located with the wall thickness deviation.

[0073] The principle of ultrasonic thickness measurement is based on the fact that the propagation time of ultrasonic waves in a workpiece is proportional to the workpiece thickness. However, due to unevenness of the inner wall or the coupling effect between the probe and the workpiece surface, the measured wall thickness data may be greater than the actual data. Therefore, by combining the numerical distribution range of wall thickness data at all locations within each interval with the aforementioned wall thickness deviation, the wall thickness data at each location is corrected. Specifically:

[0074] Calculate the range of wall thickness data for all locations within each interval; calculate the product of the normalized value of the wall thickness deviation at each location and the range within its interval; use the difference between the wall thickness data at each location and the product value as the corrected wall thickness data for each location.

[0075] In this embodiment, the Min-Max normalization method is used to obtain the normalized value of the wall thickness deviation. The Min-Max normalization method is a well-known technology and will not be described in detail in this application.

[0076] Based on the same inventive concept as the above methods, this application also provides a wall thickness detection system for air conditioning branch pipes, 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 one of the above methods for wall thickness detection of air conditioning branch pipes.

[0077] In summary, this application comprehensively evaluates the uniformity and smoothness of the inner wall at various locations of the manifold by analyzing the differences in echo time intervals and the linear decrease in echo peaks as they increase sequentially during ultrasonic testing. This reflects the accuracy of wall thickness data measurement at each location and helps in subsequent correction of the wall thickness data. Furthermore, by analyzing the irregularity of the echo signals at each location and the waveform differences of the echo signals at different locations, an influence factor is calculated to further measure the uniformity and smoothness of the inner wall at various locations of the manifold, improving the accuracy of the evaluation of the uniformity and smoothness of the inner wall at each location, and thus improving the accuracy of the evaluation of the wall thickness data at each location.

[0078] Furthermore, considering the difference in thickness between the connecting and non-connecting parts of the manifold, the manifold is divided into intervals at different locations. Within each interval, the overall difference between the wall thickness data at each location and the wall thickness data of the others is measured. This assesses the degree to which the wall thickness data at each location is affected by poor coupling, improving the accuracy of the analysis of the degree to which the wall thickness data at each location is affected by coupling. Then, combined with the influence factor, the wall thickness deviation is calculated, and the degree to which the wall thickness data at each location deviates from the true data is comprehensively assessed. This is beneficial for correcting the wall thickness data at each location, thereby improving the accuracy of the wall thickness data at each location.

[0079] Furthermore, by utilizing the wall thickness deviation and the numerical distribution range of the wall thickness data within the interval of each location, the wall thickness data at each location is corrected, reducing the impact of inner wall unevenness and coupling effect on the wall thickness data and improving the accuracy of wall thickness detection of the branch pipe.

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0081] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects.

Claims

1. A method for detecting the wall thickness of an air conditioning branch pipe, characterized in that, The method includes the following steps: Acquire the echo signals received by the ultrasonic thickness gauge at each preset position on the air conditioning branch pipe, as well as the wall thickness data at each position; By analyzing the temporal distribution of peaks in the echo signals at each location, the peak distribution coefficient for each location is obtained; the fitted straight line of all peaks in each echo signal is obtained, and the peak difference coefficient for each location is obtained by analyzing the difference between each peak in the echo signal at each location and its fitted value; the comprehensive distribution coefficient for each location is obtained by combining the peak distribution coefficient and the peak difference coefficient. The irregularity coefficient of each location is obtained by measuring the dispersion of the skewness of all echo components in the echo signal at each location. The influence factor of each location is obtained by combining the correlation between the echo signals of each location and its neighboring locations, as well as the comprehensive distribution coefficient. By analyzing the abrupt changes in wall thickness data at all locations, all locations are divided into intervals. The local wall thickness difference coefficient is obtained for each location based on the difference in wall thickness data between each location and the other locations within its interval. The wall thickness deviation at each location is obtained by combining the influencing factor with the local wall thickness difference coefficient. By combining the numerical distribution range of wall thickness data at all locations within the interval where each location is located with the wall thickness deviation, the wall thickness data at each location is corrected. The peak distribution coefficient is the dispersion of the time interval between any two adjacent peaks in the echo signal at each location.

2. The method for detecting the wall thickness of an air conditioning branch pipe as described in claim 1, characterized in that, The process for obtaining the peak difference coefficient is as follows: Calculate the difference between each peak in the echo signal at each location and its fitted value; The peak difference coefficient is the arithmetic mean of the difference values ​​corresponding to all peaks in the echo signal at each location.

3. The method for detecting the wall thickness of an air conditioning branch pipe as described in claim 1, characterized in that, The comprehensive distribution coefficient is the product of the peak distribution coefficient and the peak difference coefficient.

4. The method for detecting the wall thickness of an air conditioning branch pipe as described in claim 1, characterized in that, The irregularity coefficient is the dispersion of the skewness of all echo components in the echo signal at each location.

5. The method for detecting the wall thickness of an air conditioning branch pipe as described in claim 1, characterized in that, The process for obtaining the impact factor is as follows: Calculate the correlation coefficient between each position and its adjacent preceding and following positions, and calculate the average value of the correlation coefficients between each position and all its adjacent positions; map the average value to a positive number. Calculate the product of the comprehensive distribution coefficient and the irregularity coefficient; The influence factor is the ratio of the product to the positive number.

6. The method for detecting the wall thickness of an air conditioning branch pipe as described in claim 1, characterized in that, The process for obtaining the local wall thickness difference coefficient is as follows: Calculate the mean value of the wall thickness data at all locations within each interval; The local wall thickness difference coefficient is the difference between the wall thickness data at each location and the mean value within its interval.

7. The method for detecting the wall thickness of an air conditioning branch pipe as described in claim 1, characterized in that, The wall thickness deviation is the product of the influencing factor and the local wall thickness difference coefficient.

8. The method for detecting the wall thickness of an air conditioning branch pipe as described in claim 1, characterized in that, The process of correcting the wall thickness data at each location is as follows: Calculate the range of wall thickness data at all locations within each interval; Calculate the product of the normalized value of the wall thickness deviation at each location and the range within its interval; The difference between the wall thickness data at each location and the product value is used as the corrected wall thickness data at each location.

9. A wall thickness detection system for an air conditioning branch pipe, 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 wall thickness detection method for air conditioning branch pipes as described in any one of claims 1-8.

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