A method for detecting the production quality of reinforced carbon steel seamless pipes

Abstract

The application relates to the technical field of intelligent sensor steel pipe detection, in particular to a production quality detection method for reinforced carbon seamless steel pipes, which comprises the following steps: acquiring reflection wave data of the reinforced carbon seamless steel pipes through an intelligent sensor; obtaining first abnormal values of the reflection wave data peak distribution at different positions according to the peak amplitude difference and the distribution of the peak positions of the reflection wave data and in combination with the discrete degree of the time interval between adjacent peaks; obtaining second abnormal values of noise and baseline drift in the reflection wave data at different positions through the signal-to-noise ratio of the reflection wave data and the distribution change of the reflection wave data in the peak width corresponding to each peak, and then obtaining the significant value of the reflection wave data at different positions having defect echo characteristics, so as to obtain defect abnormal evaluation values at different positions and detect the production quality of the steel pipes. The application can improve the production quality detection precision of the steel pipes.

Description

Technical Field

[0001] This application relates to the field of intelligent sensor steel pipe inspection technology, specifically to a production quality inspection method for reinforced carbon steel seamless steel pipes. Background Technology

[0002] During the manufacturing process, various defects may occur in seamless steel pipes due to factors such as material quality, production processes, and environmental conditions. These defects can reduce the structural integrity of the seamless steel pipes, leading to serious accidents such as leaks and fractures. Therefore, the detection and analysis of surface defects are crucial for ensuring the quality and reliability of seamless steel pipes.

[0003] Among various testing methods, ultrasonic testing is relatively inexpensive and fast, greatly meeting the production requirements of some enterprises for seamless steel pipes. However, in actual testing, the presence of large grains within the steel pipe can cause uneven microstructure, leading to scattering and absorption of ultrasonic waves during propagation, resulting in ultrasonic waveform distortion. Conventional ultrasonic testing methods generally infer the presence of cracks or other quality defects based on changes in sound velocity, failing to fully consider the impact of uneven microstructure on ultrasonic wave propagation, thus exhibiting low accuracy in production quality testing.

[0004] Publication number CN120121709A describes a quality inspection method and system for seamless steel pipes. It utilizes a heat treatment analysis network and a surface treatment analysis network, combined with a phased array ultrasonic module, to monitor and evaluate the quality of the seamless steel pipes. However, the ultrasonic testing process fails to adequately consider the uneven internal microstructure of the steel pipe, resulting in insufficient accuracy of the test results. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a production quality inspection method for reinforced carbon steel seamless pipes, thereby resolving the existing issues.

[0006] The production quality inspection method for reinforced carbon steel seamless pipes proposed in this application adopts the following technical solution:

[0007] One embodiment of this application provides a production quality inspection method for reinforced carbon steel seamless pipes, including the following steps:

[0008] Obtain reflected wave data at various locations in a reinforced carbon steel seamless pipe;

[0009] The detection time period of each location on the steel pipe is extracted. Based on the difference in peak amplitude and distribution of peak positions of the reflected wave data at each location during the detection time period, and combined with the dispersion of the time interval between adjacent peaks, the first outlier of the peak distribution of the reflected wave data at each location is obtained.

[0010] The second abnormal value of noise and baseline drift in the reflection wave data of each position is obtained by the signal-to-noise ratio of the reflection wave data of each position and the distribution change of the reflection wave data in the peak width corresponding to each peak, the significant value of the reflection wave data of each position having the defect echo characteristic is obtained in combination with the first abnormal value of the peak distribution of the reflection wave data of each position, and the change trend and mutation degree of the significant value corresponding to each position of the steel pipe are analyzed to obtain the defect abnormal evaluation value of each position.

[0011] The production quality of the steel pipe is detected according to the defect abnormal evaluation value.

[0012] Preferably, the detection time period of each position of the steel pipe further comprises: taking the time period between the sound wave emission time of each position and the time corresponding to the maximum value in the reflection wave data of each position as the detection time period of each position.

[0013] Preferably, the first abnormal value of the peak distribution of the reflection wave data of each position is obtained by:

[0014] In the formula, E is the first abnormal value of the peak distribution of the reflection wave data of the current position, C is the significant coefficient of the amplitude difference and the peak sharpness of the current position, and D is the regular coefficient of the peak value distribution of the current position.

[0015] Preferably, the significant coefficient of the amplitude difference and the peak sharpness is further obtained by:

[0016] The average value of the reflection wave data corresponding to all non-peak positions in the reflection wave data of the current position in the detection time period is counted, and the cumulative sum of the absolute value of the difference between the reflection wave data corresponding to each peak point and the average value is calculated.

[0017] The product between the cumulative sum of all absolute values corresponding to the current position and the kurtosis average of all peak point positions is taken as the significant coefficient of the amplitude difference and the peak sharpness of the current position.

[0018] Preferably, the regular coefficient of the peak value distribution of the current position is the standard deviation of the difference value of all adjacent peak point corresponding time corresponding to the current position.

[0019] Preferably, the second abnormal value of noise and baseline drift in the reflection wave data of each position is obtained by:

[0020] The reflection wave data of each peak in the peak width range of each position in the detection time period is taken as the non-baseline part data of each position, the remaining part of the reflection wave data is taken as the baseline part data, the baseline part data is fitted, and the data distribution change degree of the fitting curve is used to obtain the baseline offset significant value of each position.

[0021] The product of the signal-to-noise ratio of the reflection wave data at each position and the baseline drift significant value is taken as the second abnormal value of the noise and baseline drift in the reflection wave data at each position.

[0022] Preferably, the obtaining of the baseline drift significant value further comprises: calculating the curvatures of each point on the fitting curve corresponding to each position, and taking the sum of the mean value of all curvatures and the mean value of the amplitudes of all points on the fitting curve as the baseline drift significant value of each position.

[0023] Preferably, the significant value of the reflection wave data at each position having the defect echo feature is the product of the first abnormal value and the second abnormal value.

[0024] Preferably, the method for obtaining the defect abnormality evaluation value of each position is:

[0025] wherein P is the defect abnormality evaluation value of the position corresponding to the center of the current window, is the mean value of all significant values in the current window, L is the absolute value of the test statistic corresponding to the current window, and M is the maximum value in the first-order difference sequence of the data in the current window, wherein the significant values corresponding to all positions of the steel pipe are arranged in time sequence to form a defect significant sequence, and each window is obtained by presetting a sliding window in the defect significant sequence.

[0026] Preferably, the defect abnormality evaluation values of all positions of the steel pipe are normalized, and if the mean value of all normalized results is less than a preset quality evaluation threshold, the production quality of the steel pipe is qualified.

[0027] The present application has at least the following beneficial effects:

[0028] The present application analyzes the abnormal characteristics of the amplitudes and distributions of wave crests in the reflection wave data at each position of the steel pipe in depth, and obtains the abnormal characteristics of the noise and baseline drift caused by the defects in the baseline part, which has the advantage of reducing the interference of the ultrasonic reflection or absorption phenomenon caused by the uneven structure on the recognition of the defect echo feature; further, the significant degree of the defect echo shows the corresponding mutation with the movement of the probe, the defect abnormality evaluation value is calculated, and the production quality of the welded steel pipe is detected based on this, which further improves the detection precision of the production quality of the steel pipe and helps to make up for the defect of low detection precision of the production quality of the steel pipe. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the following embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creating any creative labor.

[0030] Figure 1 A flowchart illustrating the steps of a production quality inspection method for reinforced carbon steel seamless pipes provided in this application. Detailed Implementation

[0031] 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 production quality inspection method for reinforced carbon steel seamless pipes 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.

[0032] Unless otherwise defined, 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.

[0033] The following description, in conjunction with the accompanying drawings, details the specific scheme of the production quality inspection method for reinforced carbon steel seamless pipes provided in this application.

[0034] This application provides an embodiment of a production quality inspection method for reinforced carbon steel seamless pipes. For details, please refer to [link to specific documentation]. Figure 1 This includes the following steps:

[0035] Step 1: Obtain reflected wave data at various locations on the reinforced carbon steel seamless pipe.

[0036] The production process of carbon steel seamless steel pipes requires rolling and straightening steps to change the shape, size and mechanical properties of the steel pipe. If the process control is not accurate, cracks or defects may appear inside or on the surface of the steel pipe. Ultrasonic testing can quickly and comprehensively identify possible defects inside and outside the steel pipe.

[0037] Therefore, the ultrasonic data waveforms of each position of the steel pipe are collected by the intelligent sensor. Specifically, in the embodiment, the ultrasonic flaw detector is used to detect the production quality of the steel pipe, and the waveform data of the ultrasonic wave collected by the intelligent sensor is combined. The ultrasonic flaw detector uses a transverse wave probe. When the transverse wave propagates in the steel pipe and encounters a defect or an interface, part of the transverse wave will be reflected back. By receiving these reflected waves and analyzing their waveform characteristics, it is helpful to identify the defects existing in the production of the steel pipe. During the detection process of the embodiment, the transverse wave probe moves on the surface of the steel pipe for scanning. The detection frequency is set to 10 HZ. Thus, the reflected wave data at multiple positions of the steel pipe can be obtained.

[0038] Step two: Extract the detection period of each position of the steel pipe. According to the peak amplitude difference and the distribution of the peak position of the reflected wave data at each position in the detection period, and combining the discrete degree of the time interval between adjacent peaks, the first abnormal value of the peak distribution in the reflected wave data at each position is obtained.

[0039] The deformation degree of different positions in the production process of the steel pipe may be different, resulting in different grain sizes. For example, the grains on the surface of the steel pipe may be refined due to greater deformation, while the grains in the center are relatively large. Such uneven grain size not only causes differences in mechanical properties such as strength and hardness at different parts of the steel pipe, affecting the overall performance of the steel pipe, but also affects the propagation of ultrasonic waves in the steel pipe to some extent, causing scattering and absorption, and further exacerbating the distortion of the ultrasonic waveforms collected by the intelligent sensor. The common defect types in the production process of seamless steel pipes include but are not limited to cracks, delamination, and shrinkage. When these defects exist, the intelligent sensor will obtain corresponding defect echoes in the collected reflected wave data. Generally, the more serious the defect, the more obvious the defect echo characteristics in the reflected wave data. Therefore, the application analyzes the waveform variation characteristics of the reflected wave to realize the production quality detection of the seamless steel pipe.

[0040] If the production quality of the steel pipe is good, i.e. no defects, there are no other echoes with obvious amplitude changes in the waveform of the collected reflected wave except for the pipe end echo with large amplitude. The pipe end echo is formed by the reflection of the sound wave emitted by the ultrasonic probe at the other end of the pipe. In addition, there are only some small noise signals at other positions. These noises may come from the unevenness of the material or other data collection interference. They have small amplitudes and different positions. If the produced steel pipe has defects, the reflected wave waveform will usually present various defect echoes. For example, the defect echoes generated by cracks and delamination present needle-shaped peak patterns, and there are periodic multiple reflection characteristics due to delamination. Compared with the waveform without defects, the amplitude at the position of the defect echo is significantly increased, and the noise signal is also increased accordingly, and there is a certain degree of baseline upward drift phenomenon.

[0041] Therefore, in the embodiment, the maximum value corresponding to the time point in the reflection wave data at the current position is obtained. For the convenience of understanding and description, the time point is recorded as the cutoff time of the current position in the embodiment. The period between the time of sound wave emission and the cutoff time is taken as the detection period of the current position.

[0042] To obtain the defect echo that may exist in the detection period, in the embodiment, an automatic multi-scale peak searching algorithm is used to obtain all peak points in the reflection wave data at the current position in the detection period. The obtained peak points may be located at the defect echo position or the noise signal position. Generally, the larger the area or depth of the crack or delamination in the steel pipe, the larger the amplitude of the corresponding peak value. Therefore, first, the average value of the reflection wave data corresponding to all non-peak positions of the current position in the detection period is calculated. The absolute value of the difference between the reflection wave data corresponding to each peak point and the average value is calculated, respectively. The cumulative sum of all absolute values is calculated. The cumulative sum reflects the significance of the size of the peak value in the reflection wave data.

[0043] Further, in the embodiment, the kurtosis of the position corresponding to each peak point is obtained. The kurtosis reflects the sharpness of the peak pattern. The mean value of all kurtosis is calculated. The larger the mean value, the sharper the peak in the reflection wave data.

[0044] Therefore, the product between the cumulative sum of all absolute values corresponding to the current position and the mean value of the kurtosis of all peak point positions is taken as the significant coefficient of the amplitude difference and the sharpness of the peak at the current position. The significant coefficient is recorded as C. The larger the obtained C, the more obvious the amplitude difference between the peak value and the non-peak value data corresponding to the reflection wave data at the current position. The sharper the peak feature is also more obvious.

[0045] In addition, when the ultrasonic wave encounters a delamination defect, the defect echo has the characteristics of periodic multiple reflections. The stronger the regularity of the position distribution between the obtained peak values is. Therefore, the difference between the time points corresponding to all adjacent peak points at the current position is calculated. The standard deviation of all differences is taken as the regularity coefficient of the peak distribution at the current position. The regularity coefficient is recorded as D. The obtained D reflects the significant characteristics of the periodic multiple reflections of the peak in the reflection wave data.

[0046] Further, in the embodiment, the first abnormal value of the peak distribution of the reflection wave data at each position is calculated according to the significant coefficient of the amplitude difference and the sharpness of the peak at each position, and the regularity coefficient of the peak distribution. The specific calculation formula is: , wherein E is a first abnormal value of the peak distribution of the current position reflected wave data, C is a significant coefficient of the amplitude difference and the peak sharpness of the current position, and D is a regular coefficient of the peak distribution of the current position, wherein the greater the obtained E, the more obvious the abnormal characteristics of the peak corresponding amplitude and the peak distribution of the current position reflected wave data.

[0047] Step three: obtain the second abnormal value of the noise and baseline drift in the reflected wave data of each position by the signal-to-noise ratio of the reflected wave data of each position and the distribution change of the reflected wave data in the peak width of each peak, and obtain the significant value of the reflected wave data of each position having the defect echo characteristics in combination with the first abnormal value of the peak distribution of the reflected wave data of each position, analyze the change trend and mutation degree of the significant value corresponding to each position of the steel pipe, and obtain the defect abnormal evaluation value of each position.

[0048] Further, when the ultrasonic wave propagates in the steel pipe and encounters a defect, the reflected wave generated thereby is more likely to be accompanied by more noise, and the baseline of the waveform is prone to upward drift. Therefore, in the present embodiment, the signal-to-noise ratio of all reflected wave data of each position in the detection period is first obtained, and the greater the obtained signal-to-noise ratio, the more noise contained in the reflected wave data collected at the position. In order to obtain the baseline drift degree feature, in the present embodiment, the reflected wave data in the peak width range corresponding to each peak in the detection period is first obtained as non-baseline part data, which usually corresponds to the reflection part of the ultrasonic wave, and the remaining reflected wave data is taken as baseline part data, and then a quadratic polynomial fitting technique is used to fit the obtained baseline part data to obtain the corresponding fitting curve. When the steel pipe is defect-free, the baseline part of the reflected wave data is small as a whole, usually near 0 value, and has obvious linear characteristics, and when baseline drift occurs, the greater the upward convexity of the obtained fitting curve.

[0049] In view of this, in the present embodiment, the curvature of each point on the fitting curve is calculated, and the sum of the mean value of all curvatures and the mean value of the amplitudes of all points on the fitting curve is taken as the baseline drift significant value of each position caused by defect reflection, which reflects the baseline drift feature of the reflected wave data at the position. Further, the product of the signal-to-noise ratio of the reflected wave data of each position and the baseline drift significant value is taken as the second abnormal value of the noise and baseline drift in the reflected wave data of each position, i.e. G. The greater the value, the more obvious the abnormal characteristics of the noise and baseline drift in the baseline part of the reflected wave data due to defect reflection.

[0050] In summary, the first and second abnormal values obtained respectively reflect the possibility of defects existing at each position of the steel pipe from different angles, wherein the first abnormal value is mainly analyzed from the defect echo characteristics shown by the data in the wave crest part, and the second abnormal value is analyzed from the related characteristics of the data in the baseline part affected by the defect reflection. Therefore, the greater the first and second abnormal values obtained, the more likely it is that the corresponding position of the steel pipe has a production defect.

[0051] Furthermore, in the embodiment, the significant value of the reflection wave data of each position having defect echo characteristics in the wave crest part and the baseline part is obtained, and the significant value of the reflection wave data of each position having defect echo characteristics is the product of the first abnormal value and the second abnormal value corresponding to each position. For the convenience of understanding and description, the specific calculation formula in the embodiment is: In the formula, H is the significant value of the reflection wave data of the current position having defect echo characteristics, E is the first abnormal value of the wave crest distribution of the reflection wave data of the current position, and G is the second abnormal value of the noise and baseline drift in the reflection wave data of the current position. The obtained H reflects the significant degree of the defect echo in the wave crest part and the baseline part of the reflection wave data of the steel pipe at the current position.

[0052] In addition, during the movement scanning process of the probe, the distribution of defects inside the steel pipe is relatively random, the probe will detect defect echo characteristics with a certain randomness during the scanning process, and the existing background noise will interfere with the accuracy of defect feature detection at each position to a certain extent. However, when the probe is closer to the defect part, the reflection degree of the ultrasonic wave in the defect is greater, so that the significant degree of the defect echo shows a corresponding progressive change or mutation as the probe moves. That is, the closer to the defect position, the greater the significant value of the defect echo characteristics obtained, and vice versa.

[0053] Therefore, in the embodiment, the significant values of the defect echo characteristics at all positions of the steel pipe are arranged in ascending order of time to obtain a defect significant sequence. A sliding window with a size of 1x11 is set, the sliding step is 1, and then the trend change characteristics of the corresponding data in each window are obtained by using the Mann-Kendall detection algorithm. The output of the Mann-Kendall detection algorithm is the test statistic of the data in each window, the absolute value of the test statistic corresponding to the current window is recorded as L, the greater the L obtained, the more likely it is that there is a progressive change in the current window. Then, the first-order difference sequence of the corresponding data in each window is obtained, the maximum value of the absolute values of all data in the first-order difference sequence is counted, and the maximum value of the first-order difference sequence in the current window is recorded as M. The greater the maximum value of the first-order difference sequence in the current window, the more likely it is that there is a mutation in the data in the current window.

[0054] Furthermore, in the embodiment, the defect abnormality evaluation value corresponding to the center position of each window is obtained, and the formula is:

[0055] wherein is the mean value of all significant values in the current window, P is the defect anomaly evaluation value of the position corresponding to the center of the current window, L is the absolute value of the test statistic corresponding to the current window, and M is the maximum value in the first-order difference sequence of the data in the current window. The obtained P reflects the significant degree of the defect echo characteristics and the gradual change or abrupt change characteristics corresponding to the defect characteristics of the position at the center of the window.

[0056] Step three: detecting the production quality of the steel pipe according to the defect anomaly evaluation value.

[0057] In the embodiment, the defect anomaly evaluation value of each position can be obtained through the above steps and processes. Further, the production quality of the steel pipe is evaluated and detected according to the defect anomaly evaluation value. Specifically, in the embodiment, the defect anomaly evaluation values calculated at all positions are normalized by using a sigmoid function. The implementer can also select other existing normalization methods, which are not specially limited in the embodiment. The mean value of all normalized results is calculated, and the quality evaluation threshold is set, which is 0.6 in the embodiment. If the mean value of all normalized results is less than the evaluation threshold, the production quality of the steel pipe is qualified, otherwise, the production quality of the steel pipe is unqualified and needs to be processed again.

[0058] Thus, the production quality of the reinforced carbon steel seamless steel pipe can be detected according to the above method of the embodiment, and the defect of low production quality detection precision can be made up.

[0059] It can be understood that the reference to “one embodiment” or “some embodiments” and the like described in the present application specification means that the specific features, structures or characteristics described in connection with the embodiment are included in one or more embodiments of the present application. Therefore, if “in one embodiment”, “in some embodiments”, “in other some embodiments”, “in further some embodiments” and the like appear in the present specification, it does not necessarily refer to the same embodiment, but means “one or more but not all embodiments”, unless otherwise specifically emphasized. The terms “include”, “contain”, “have” and their variants mean “include but not limited to”, unless otherwise specifically emphasized.

[0060] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous. At the same time, the size of the serial number of each step in the embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments in the specification.

[0061] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for detecting the production quality of a reinforced carbon steel seamless steel pipe, characterized by, The method comprises the following steps: Obtaining reflected wave data of each position of the reinforced carbon steel seamless steel pipe; Extracting a detection period of each position of the steel pipe, obtaining a first abnormal value of peak distribution of the reflected wave data of each position according to a difference of peak amplitude and a distribution of peak position of the reflected wave data of each position in the detection period, and combining a discrete degree of time interval between adjacent peaks; Obtaining a second abnormal value of noise and baseline drift in the reflected wave data of each position through a signal-to-noise ratio of the reflected wave data of each position and a distribution change of the reflected wave data in a peak width corresponding to each peak, taking a product of the first abnormal value and the second abnormal value as a significant value of the reflected wave data of each position having a defect echo characteristic, analyzing a change trend and a mutation degree of the significant value corresponding to each position of the steel pipe, and obtaining a defect abnormal evaluation value of each position; Detecting a production quality of the steel pipe according to the defect abnormal evaluation value; The method for obtaining the first abnormal value of the peak distribution of the reflected wave data of each position comprises the following steps: , wherein E is the first abnormal value of the peak distribution of the current position reflected wave data, C is the significant coefficient of the amplitude difference and the peak sharpness of the current position, and D is the regular coefficient of the peak value distribution of the current position. The method for obtaining the second abnormal value of noise and baseline drift in the reflected wave data of each position comprises the following steps: Taking the reflected wave data in a peak width range corresponding to each peak of each position in the detection period as non-baseline part data of each position, taking remaining part of the reflected wave data as baseline part data, fitting the baseline part data, and obtaining a baseline drift significant value of each position by using a data distribution change degree of the fitted curve; Taking a product of the signal-to-noise ratio of the reflected wave data of each position and the baseline drift significant value as the second abnormal value of noise and baseline drift in the reflected wave data of each position; The method for obtaining the baseline drift significant value further comprises the following steps: counting curvatures of each point on the fitted curve corresponding to each position, and taking a sum of a mean value of all curvatures and a mean value of amplitude values of all points on the fitted curve as the baseline drift significant value of each position; The method for obtaining the defect abnormal evaluation value of each position comprises the following steps: wherein P is the defect anomaly evaluation value of the current window center corresponding position, is the mean value of all significant values in the current window, L is the absolute value of the test statistic corresponding to the current window, and M is the maximum value in the first-order difference sequence of the data in the current window, wherein the significant values corresponding to all positions of the steel pipe are arranged in time sequence to form a defect significant sequence, and each window is obtained by presetting a sliding window in the defect significant sequence.

2. A method of inspecting the production quality of a reinforced carbon steel seamless pipe according to claim 1, characterized by, The method for extracting the detection period of each position of the steel pipe further comprises the following step: taking a period between a time when the sound wave is emitted and a time corresponding to a maximum value in the reflected wave data of each position as the detection period of each position.

3. A method of inspecting the production quality of a reinforced carbon steel seamless pipe according to claim 1, characterized by, The method for obtaining the significant coefficient of the amplitude difference and the peak sharpness degree further comprises the following steps: Counting an average value of reflected wave data corresponding to all non-peak positions in the reflected wave data of the current position in the detection period, and calculating a cumulative sum of absolute values of differences between the reflected wave data of each peak point and the average value; Taking a product between the cumulative sum of all absolute values corresponding to the current position and a mean value of kurtosis of all peak point positions as the significant coefficient of the amplitude difference and the peak sharpness degree of the current position.

4. A method of inspecting the production quality of a reinforced carbon steel seamless pipe according to claim 1, characterized by, The regular coefficient of the peak distribution of the current position is a standard deviation of differences between time instants corresponding to all adjacent peak points of the current position.

5. A method of inspecting the production quality of a reinforced carbon steel seamless pipe according to claim 1, wherein The significant value of the reflected wave data of each position having the defect echo characteristic is a product of the first abnormal value and the second abnormal value.

6. A method of inspecting the production quality of a reinforced carbon steel seamless pipe according to claim 1, characterized by, The defect abnormal evaluation values of all positions of the steel pipe are normalized, and if a mean value of all normalized results is less than a preset quality evaluation threshold, the production quality of the steel pipe is qualified.

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