Production quality detection method of reinforced carbon steel seamless steel pipe

Abstract

The invention relates to the technical field of intelligent sensor steel pipe detection, in particular to a production quality detection method for a reinforced carbon steel seamless steel pipe, which comprises the following steps: acquiring reflected wave data of the reinforced carbon steel seamless steel pipe through an intelligent sensor; obtaining a first abnormal value of wave crest distribution of the reflected wave data at each position according to the wave crest amplitude difference of the reflected wave data and the distribution condition of wave crest positions in combination with the dispersion degree of the time interval between adjacent wave crests; and through the signal-to-noise ratio of the reflected wave data and the distribution change of the reflected wave data in the peak width corresponding to each peak, obtaining a second abnormal value of noise and baseline drift in the reflected wave data at each position, and further obtaining a saliency value with defect echo characteristics of the reflected wave data at each position so as to obtain a defect abnormality evaluation value at each position. And detecting the production quality of the steel pipe. The steel pipe production quality detection precision can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent sensor steel pipe detection, and specifically to a production quality detection method for reinforced carbon steel seamless steel pipes. Background Art

[0002] During the manufacturing process, seamless steel pipes may develop various defects due to factors such as material quality, production processes, and environmental conditions. These defects can reduce the structural integrity of seamless steel pipes and lead to serious accidents such as leakage and breakage. Therefore, detecting and analyzing surface defects is crucial to ensuring the quality and reliability of seamless steel pipes.

[0003] Among numerous inspection methods, ultrasonic testing offers relatively low cost and high speed, greatly satisfying the production requirements of some companies for seamless steel pipes. However, in actual inspections, the presence of large grains within the steel pipe can lead to an uneven microstructure, which in turn causes scattering and absorption of ultrasonic waves during propagation, resulting in 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 structural unevenness on ultrasonic propagation, resulting in low production quality inspection accuracy.

[0004] Publication No. CN120121709A discloses a quality inspection method and system for finished seamless steel pipes. This system utilizes a heat treatment analysis network and a surface treatment analysis network, combined with a phased array ultrasonic module, to monitor and assess the quality of seamless steel pipes. However, the ultrasonic inspection process fails to fully account for the uneven internal structure of the steel pipes, resulting in insufficient accuracy in the test results. Summary of the Invention

[0005] In order to solve the above technical problems, the present application provides a production quality inspection method for reinforced carbon steel seamless steel pipes to solve the existing problems.

[0006] The present invention discloses a production quality inspection method for reinforced carbon steel seamless pipes using the following technical solutions: One embodiment of the present application provides a method for detecting the production quality of a reinforced carbon steel seamless pipe, comprising the following steps: Obtaining reflected wave data at various locations of the reinforced carbon steel seamless pipe; Extract the detection period of each position of the steel pipe, and obtain the first outlier of the peak distribution of the reflected wave data at each position based on the difference in peak amplitude and peak position of the reflected wave data at each position in the detection period, combined with the degree of dispersion of the time interval between adjacent peaks; The signal-to-noise ratio of the reflected wave data at each position and the distribution change of the reflected wave data within the peak width corresponding to each peak are used to obtain the second abnormal value of the noise and baseline drift in the reflected wave data at each position. Combined with the first abnormal value of the peak distribution in the reflected wave data at each position, the significant value of the reflected wave data at each position having the defect echo characteristic is obtained. The change trend and mutation degree of the significant value corresponding to each position of the steel pipe are analyzed to obtain the defect abnormality assessment value at each position. The production quality of the steel pipe is tested according to the defect abnormality evaluation value.

[0007] Preferably, the extracting of the detection period of each position of the steel pipe further includes: taking the period between the time of sound wave emission at each position and the time corresponding to the maximum value in the reflected wave data at each position as the detection period of each position.

[0008] Preferably, the method for obtaining the first abnormal value of the peak distribution of the reflected wave data at each position is: , where E is the first abnormal value of the peak distribution of the reflected wave data at the current position, C is the significant coefficient of the amplitude difference and peak sharpness at the current position, and D is the regular coefficient of the peak distribution at the current position.

[0009] Preferably, obtaining the significant coefficients of the amplitude difference and peak sharpness further includes: Count the average value of the reflected wave data corresponding to all non-peak positions in the reflected wave data of the current position within the detection period, and calculate the cumulative sum of the absolute values ​​of the differences between the reflected wave data corresponding to each peak point and the average value; The product of the cumulative sum of all absolute values ​​corresponding to the current position and the mean kurtosis of all peak points is used as the significant coefficient of the amplitude difference and peak sharpness of the current position.

[0010] Preferably, the regularity coefficient of the peak distribution at the current position is the standard deviation of the difference between the corresponding moments of all adjacent peak points corresponding to the current position.

[0011] Preferably, the method for obtaining the second abnormal value of noise and baseline drift in the reflected wave data at each position is: The reflected wave data within the peak width corresponding to each peak in the detection period at each position is used as the non-baseline data at each position, and the remaining reflected wave data is used as the baseline data. The baseline data is fitted, and the degree of change in the data distribution of the fitting curve is used to obtain the baseline shift significance value at each position; The product of the signal-to-noise ratio of the reflected wave data at each position and the baseline drift significance value is taken as the second abnormal value of the noise and baseline drift in the reflected wave data at each position.

[0012] Preferably, obtaining the baseline drift significance value further includes: counting the curvature of each point on the fitting curve corresponding to each position, and taking the sum of the mean of all curvatures and the mean of the amplitudes corresponding to all points on the fitting curve as the baseline drift significance value of each position.

[0013] Preferably, the significant value of the defect echo characteristic in the reflected wave data at each position is the product of the first abnormal value and the second abnormal value.

[0014] Preferably, the method for obtaining the defect anomaly evaluation value of each position is: , where P is the defect anomaly assessment value corresponding to the center of the current window, is the mean 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. The significant values ​​corresponding to all positions of the steel pipe are arranged in time series to form a defect significance sequence, and each window is obtained by presetting a sliding window in the defect significance sequence.

[0015] Preferably, the defect anomaly assessment values ​​at all positions of the steel pipe are normalized. If the mean of all normalized results is less than a preset quality assessment threshold, the production quality of the steel pipe is qualified.

[0016] This application has at least the following beneficial effects: This application deeply analyzes the abnormal characteristics of the amplitude and distribution of the peaks in the reflected wave data at various positions of the steel pipe, and obtains the abnormal characteristics of the noise and baseline drift in the baseline part due to defect reflection. Its advantage is that it can reduce the interference of ultrasonic reflection or absorption caused by uneven tissue structure on the identification of defect echo characteristics; further combined with the corresponding mutation of the significance of the defect echo as the probe moves, the defect abnormality assessment value is calculated, and the production quality of the seam steel pipe is tested based on this, which further improves the detection accuracy of the steel pipe production quality and helps to make up for the defect of low detection accuracy of the steel pipe production quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 This is a flowchart of the steps of a production quality inspection method for reinforced carbon steel seamless pipes provided in this application. DETAILED DESCRIPTION

[0019] To further illustrate the technical means and effectiveness of this application's implementation of the intended invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a production quality inspection method for reinforced carbon steel seamless pipes proposed in this application. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0020] Unless otherwise defined, terms such as "comprises," "comprising," or any other variants thereof are intended to encompass non-exclusive inclusion, such that a circuit structure, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the article or device comprising the element. In addition, the term "and\or" as used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains.

[0021] The specific scheme of the production quality inspection method of reinforced carbon steel seamless pipe provided by this application is described in detail below with reference to the accompanying drawings.

[0022] An embodiment of the present application provides a production quality inspection method for reinforced carbon steel seamless steel pipes. For details, please refer to Figure 1 , including the following steps: Step 1: Obtain reflection wave data at various locations of the reinforced carbon steel seamless pipe.

[0023] The production process of carbon steel seamless pipes requires rolling and straightening steps to change the shape, size and mechanical properties of the steel pipes. If the process control is not accurate, cracks may easily appear inside or on the surface of the steel pipes. Ultrasonic testing can quickly and comprehensively identify possible defects inside and outside the steel pipes.

[0024] Therefore, intelligent sensors are used to collect ultrasonic waveform data at various locations on the steel pipe. Specifically, in this embodiment, an ultrasonic flaw detector is used to inspect the quality of steel pipe production, and the intelligent sensor is used to collect ultrasonic waveform data. The ultrasonic flaw detector uses a shear wave probe. When a shear wave propagates within the steel pipe and encounters a defect or interface, part of the shear wave is reflected back. By receiving these reflected waves and analyzing their waveform characteristics, defects in the steel pipe can be identified. During the inspection process in this embodiment, the shear wave probe moves and scans the surface of the steel pipe, with the inspection frequency set to 10 Hz. This allows for the acquisition of reflected wave data at multiple locations on the steel pipe.

[0025] Step 2: Extract the detection time period of each position of the steel pipe, and obtain the first outlier of the peak distribution in the reflected wave data at each position based on the difference in peak amplitude and distribution of peak positions in the detection period, combined with the degree of discreteness of the time intervals between adjacent peaks.

[0026] The degree of deformation at different positions during the steel pipe production process may be different, resulting in different grain sizes. For example, due to the large deformation on the surface of the steel pipe, the grains may be refined, while the grains in the center are relatively large. This uneven grain size will not only cause differences in mechanical properties such as strength and hardness in different parts of the steel pipe, affecting the overall performance of the steel pipe, but will also affect the propagation of ultrasonic waves inside the steel pipe to a certain extent, causing scattering and absorption, thereby exacerbating the distortion of the ultrasonic waveform collected by the intelligent sensor. Common defect types in the production process of seamless steel pipes include but are not limited to cracks, delamination, and shrinkage cavities. When these defects exist, corresponding defect echoes will appear in the reflected wave data collected by the intelligent sensor. Generally, the more severe the defect, the more obvious the defect echo characteristics in the reflected wave data. Therefore, this application analyzes the waveform change characteristics of the reflected wave to achieve production quality detection of seamless steel pipes.

[0027] If the steel pipe is produced to good quality, i.e., defect-free, the waveform of the collected reflected wave will show no other echoes with noticeable amplitude variations, except for the larger-amplitude echo from the pipe end face. This echo is formed when the sound waves emitted by the ultrasonic probe are reflected back from the other end of the pipe. In addition, only minor noise signals are present at other locations. These noise signals may originate from material inhomogeneities or other data acquisition interferences, and their amplitudes are smaller and their positions vary. If the steel pipe is defective, the waveform of the reflected wave will typically show a variety of defect echoes. For example, defect echoes from cracks and delamination will exhibit a needle-like peak pattern, characterized by periodic multiple reflections due to the delamination. Compared to the waveform of a defect-free sample, the amplitude of the defect echo is significantly increased, and the noise signal also increases accordingly, resulting in a certain degree of upward baseline drift.

[0028] Therefore, in this embodiment, taking the reflected wave data collected at the current position as an example, since the defect echo is between the time of sound wave emission and the time corresponding to the pipe end surface echo, where the amplitude corresponding to the pipe end surface echo is the largest, therefore, in this embodiment, the time corresponding to the maximum value in the reflected wave data at the current position is obtained. For the convenience of understanding and expression, in this embodiment, it is recorded as the cutoff time of the current position, and the period between the sound wave emission time and the cutoff time is used as the detection period of the current position.

[0029] To obtain possible defect echoes during the inspection period, this embodiment utilizes an automatic multi-scale peak search algorithm to obtain all peak points in the reflected wave data at the current position within the inspection period. The resulting peak point may be located at the defect echo position or the noise signal position. Generally, the larger the crack or delamination area or depth in the steel pipe, the larger the corresponding peak amplitude. Therefore, the average value of the reflected wave data corresponding to all non-peak positions at the current position during the inspection period is first calculated. The absolute value of the difference between the reflected wave data corresponding to each peak point and this average value is calculated, and the cumulative sum of all absolute values ​​is calculated. This cumulative sum reflects the significance of the peak size in the reflected wave data.

[0030] Furthermore, in this embodiment, the kurtosis of each peak point is obtained, and the kurtosis reflects the sharpness of the peak pattern. The mean of all kurtosis is calculated. The larger the mean, the sharper the peak in the reflected wave data.

[0031] Therefore, the product of the cumulative sum of all absolute values ​​corresponding to the current position and the mean kurtosis of all peak point positions is taken as the significance coefficient of the amplitude difference and peak sharpness of the current position, which is denoted as C. The larger the obtained C, the more obvious the amplitude difference between the peak and non-peak data of the corresponding reflected wave data at the current position, and the more obvious the peak sharpness feature.

[0032] In addition, when ultrasonic wave propagation encounters a delamination defect, the defect echo has the characteristic of periodic multiple reflections. The stronger the regularity of the position distribution between the obtained peaks, the more regular the peaks are. Therefore, the difference between the corresponding moments of all adjacent peak points corresponding to the current position is calculated, and the standard deviation of all differences is used as the regularity coefficient of the peak distribution at the current position, denoted as D. The obtained D reflects the significant characteristic of periodic multiple reflections of the peaks in the reflected wave data.

[0033] Furthermore, in this embodiment, the first abnormal value of the peak distribution of the reflected wave data at each position is calculated based on the amplitude difference at each position, the significance coefficient of the peak sharpness, and the regularity coefficient of the peak distribution. The specific calculation formula is: Where, E is the first abnormal value of the peak distribution of the reflected wave data at the current position, C is the significant coefficient of the amplitude difference and peak sharpness at the current position, and D is the regular coefficient of the peak distribution at the current position. The larger the obtained E is, the more obvious the abnormal characteristics of the peak corresponding amplitude and peak distribution of the reflected wave data at the current position are.

[0034] Step 3: Obtain the second abnormal value of noise and baseline drift in the reflected wave data at each position through the signal-to-noise ratio of the reflected wave data at each position and the distribution change of the reflected wave data within the peak width corresponding to each peak. Combined with the first abnormal value of the peak distribution in the reflected wave data at each position, obtain the significant value of the reflected wave data at each position with the defect echo feature. Analyze the change trend and mutation degree of the significant value corresponding to each position of the steel pipe to obtain the defect abnormality assessment value at each position.

[0035] Furthermore, when ultrasonic waves propagate inside a steel pipe and encounter defects, the resulting reflected waves are more likely to be accompanied by more noise, and the baseline of the waveform is more likely to drift upward. Therefore, in this embodiment, the signal-to-noise ratio of all reflected wave data at each location within the detection period is first obtained. The larger the obtained signal-to-noise ratio, the more noise the reflected wave data collected at that location contains. To obtain the characteristics of the degree of baseline drift, in this embodiment, the reflected wave data within the peak width corresponding to each peak in the detection period is first obtained as the non-baseline data. This data generally corresponds to the reflected portion of the ultrasonic wave. The remaining reflected wave data is used as the baseline data. The obtained baseline data is then fitted using a quadratic polynomial fitting technique to obtain the corresponding fitting curve. When the steel pipe is defect-free, the baseline portion of the reflected wave data is generally small, usually located near 0, and has obvious linear characteristics. When baseline drift occurs, the degree of upward bulge of the obtained fitting curve is greater.

[0036] Given this, in this embodiment, the curvature of each point on the fitting curve is calculated. The sum of the mean of all curvatures and the mean of the corresponding amplitudes at all points on the fitting curve is used as the baseline drift significance value at each location due to defect reflections, reflecting the baseline drift characteristics of the reflected wave data at that location. Furthermore, the product of the signal-to-noise ratio of the reflected wave data at each location and the baseline drift significance value is used as the second outlier value for noise and baseline drift in the reflected wave data at that location, i.e., G. A larger value indicates a more significant abnormality in the noise and baseline drift in the baseline portion of the reflected wave data due to defect reflections.

[0037] In summary, the first and second outlier values ​​reflect the likelihood of defects at various locations on the steel pipe from different perspectives. The first outlier primarily analyzes the defect echo characteristics of the peak data, while the second outlier analyzes the characteristics of the baseline data (non-peak data) affected by defect reflections. Therefore, the larger the first and second outlier values, the more likely the corresponding location on the steel pipe is to have a production defect.

[0038] Furthermore, in this embodiment, the significant value of the defect echo characteristics in the peak part and the baseline part of the reflected wave data at each position is obtained. The significant value of the defect echo characteristics in the reflected wave data at each position is the product of the first abnormal value and the second abnormal value corresponding to each position. For ease of understanding and expression, the specific calculation formula in this embodiment is: , where H is the significance value of defect echo characteristics in the reflected wave data at the current location, E is the first outlier in the peak distribution of the reflected wave data at the current location, and G is the second outlier in the noise and baseline drift in the reflected wave data at the current location. The obtained H reflects the significance of defect echoes in the peak and baseline portions of the reflected wave data at the current location.

[0039] Furthermore, as the probe moves and scans, the defect echo signatures detected by the probe also exhibit a degree of randomness due to the relatively random distribution of defects within the steel pipe. Furthermore, the presence of background noise can interfere with the accuracy of defect signature detection at various locations. However, as the probe approaches the defect, the ultrasonic wave is more reflected from the defect, causing the defect echo significance to exhibit gradual or sudden changes as the probe moves. Specifically, the closer the probe is to the defect, the greater the significance of the defect echo signature, and vice versa.

[0040] In view of this, in this embodiment, the significant values ​​of the defect echo characteristics at all locations on the steel pipe are arranged in ascending time order to obtain a defect significance sequence. A sliding window of size 1×11 is set with a sliding step of 1. The Mann-Kendall detection algorithm is then used to obtain the trend change characteristics of the data corresponding to each window. The output of the Mann-Kendall detection algorithm is the test statistic corresponding to the data in each window. The absolute value of the test statistic corresponding to the current window is recorded as L. The larger the obtained L, the more likely there is a gradual change in the current window. The first-order difference sequence of the data corresponding to each window is then obtained. The maximum absolute value of all data in the first-order difference sequence is calculated, and the maximum value in the first-order difference sequence of the data in the current window is recorded as M. The larger the maximum value in the first-order difference sequence of the data in the current window, the more likely there is a sudden change in the data in the current window.

[0041] Furthermore, in this embodiment, the defect anomaly assessment value corresponding to the center position of each window is obtained, and the formula is: ,in is the mean of all significant values ​​within the current window, P is the defect anomaly assessment value at 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 within the current window. The obtained P reflects the significance of the defect echo characteristics at the center of the window and the degree of significance of the gradual change or sudden change characteristics corresponding to the defect characteristics.

[0042] Step 3: Inspect the production quality of the steel pipe according to the defect abnormality assessment value.

[0043] In this embodiment, the above steps and processes can be used to obtain defect anomaly assessment values ​​at each location. Furthermore, the quality of steel pipe production is assessed and tested based on the defect anomaly assessment values. Specifically, in this embodiment, the defect anomaly assessment values ​​calculated at all locations are normalized using a sigmoid function. The implementer may also select other existing normalization methods, which are not specifically limited in this embodiment. The mean of all normalized results is calculated, and a quality assessment threshold is set. In this embodiment, the value is 0.6. If the mean of all normalized results is less than the assessment threshold, the steel pipe production quality is qualified. Otherwise, the steel pipe production quality is unqualified and requires further processing.

[0044] Thus, according to the above method of this embodiment, the production quality of reinforced carbon steel seamless steel pipes can be inspected, and the defect of low production quality inspection accuracy can be compensated.

[0045] It is understood that references to "one embodiment" or "some embodiments" in the present specification mean that one or more embodiments of the present application include a particular feature, structure, or characteristic described in conjunction with that embodiment. Thus, if "in one embodiment," "in some embodiments," "in other embodiments," or "in other embodiments" appear in different places in this specification, they do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0046] It should be noted that the above-mentioned sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above description is of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous. At the same time, the size of the sequence number of each step in the embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments in this specification.

[0047] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A production quality inspection method for reinforced carbon steel seamless pipes, characterized in that: The following steps are involved: Obtaining reflected wave data at various locations of the reinforced carbon steel seamless pipe; Extract the detection period of each position of the steel pipe, and obtain the first outlier of the peak distribution of the reflected wave data at each position based on the difference in peak amplitude and peak position of the reflected wave data at each position in the detection period, combined with the degree of dispersion of the time interval between adjacent peaks; The signal-to-noise ratio of the reflected wave data at each position and the distribution change of the reflected wave data within the peak width corresponding to each peak are used to obtain the second abnormal value of the noise and baseline drift in the reflected wave data at each position. Combined with the first abnormal value of the peak distribution in the reflected wave data at each position, the significant value of the reflected wave data at each position having the defect echo characteristic is obtained. The change trend and mutation degree of the significant value corresponding to each position of the steel pipe are analyzed to obtain the defect abnormality assessment value at each position. The production quality of the steel pipe is tested according to the defect abnormality evaluation value.

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

3. The production quality inspection method of a reinforced carbon steel seamless pipe according to claim 1, characterized in that: The method for obtaining the first abnormal value of the peak distribution of the reflected wave data at each position is: , where E is the first abnormal value of the peak distribution of the reflected wave data at the current position, C is the significant coefficient of the amplitude difference and peak sharpness at the current position, and D is the regular coefficient of the peak distribution at the current position.

4. A production quality inspection method for reinforced carbon steel seamless pipes according to claim 3, characterized in that: The acquisition of the significant coefficients of the amplitude difference and the peak sharpness further includes: Count the average value of the reflected wave data corresponding to all non-peak positions in the reflected wave data of the current position within the detection period, and calculate the cumulative sum of the absolute values ​​of the differences between the reflected wave data corresponding to each peak point and the average value; The product of the cumulative sum of all absolute values ​​corresponding to the current position and the mean kurtosis of all peak points is used as the significant coefficient of the amplitude difference and peak sharpness of the current position.

5. A production quality inspection method for reinforced carbon steel seamless pipes as claimed in claim 3, characterized in that: The regularity coefficient of the peak distribution of the current position is the standard deviation of the difference between the corresponding moments of all adjacent peak points corresponding to the current position.

6. A production quality inspection method for reinforced carbon steel seamless pipes according to claim 1, characterized in that: The method for obtaining the second abnormal value of noise and baseline drift in the reflected wave data at each position is: The reflected wave data within the peak width corresponding to each peak in the detection period at each position is used as the non-baseline data at each position, and the remaining reflected wave data is used as the baseline data. The baseline data is fitted, and the degree of change in the data distribution of the fitting curve is used to obtain the baseline shift significance value at each position; The product of the signal-to-noise ratio of the reflected wave data at each position and the baseline drift significance value is taken as the second abnormal value of the noise and baseline drift in the reflected wave data at each position.

7. A production quality inspection method for reinforced carbon steel seamless pipes according to claim 6, characterized in that: The acquisition of the baseline drift significance value further includes: counting the curvature of each point on the fitting curve corresponding to each position, and taking the sum of the mean of all curvatures and the mean of the amplitudes corresponding to all points on the fitting curve as the baseline drift significance value of each position.

8. The production quality inspection method of a reinforced carbon steel seamless pipe according to claim 1, characterized in that: The significant value of the defect echo feature in the reflected wave data at each position is the product of the first abnormal value and the second abnormal value.

9. A production quality inspection method for reinforced carbon steel seamless pipes according to claim 1, characterized in that: The method for obtaining the defect anomaly evaluation value of each position is: , where P is the defect anomaly assessment value corresponding to the center of the current window, is the mean 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. The significant values ​​corresponding to all positions of the steel pipe are arranged in time series to form a defect significance sequence, and each window is obtained by presetting a sliding window in the defect significance sequence.

10. The production quality inspection method of a reinforced carbon steel seamless pipe according to claim 1, characterized in that: The defect anomaly assessment values ​​at all positions of the steel pipe are normalized. If the mean of all normalized results is less than the preset quality assessment threshold, the steel pipe production quality is qualified.

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