Evaluation method, device and equipment for irregular corrosion defect pipeline and medium
By acquiring three-dimensional scanning data and pressure fluctuation data of pipelines with irregular corrosion defects, a limit state function and corrosion growth model are constructed to calculate the pipeline failure probability. This solves the problem of accuracy in evaluating pipelines with irregular corrosion defects and improves the safety and reliability of pipeline operation.
Patent Information
- Application Number
- CN202511438610.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-09
- Publication Date
- 2026-01-02
AI Technical Summary
Existing technologies are insufficient to accurately assess the failure risk of pipelines with irregular corrosion defects, resulting in significant discrepancies between the assessment results and the actual bursting capacity, which affects the safety of pipeline operation.
By acquiring three-dimensional scanning data of pipelines with irregular corrosion defects and pressure fluctuation data of pipe sections, parameter statistical analysis is performed to construct limit state functions and corrosion growth models, establish limit state equations, calculate the failure probability of pipelines under conditions of no hydrogen and different hydrogen doping ratios, and conduct parameter sensitivity analysis to improve the accuracy of evaluation.
Accurately acquiring the geometric characteristics of pipelines with corrosion defects reduces the deviation between assessment results and actual bursting capacity, thereby improving the safety and reliability of pipeline operation.
Smart Images
Figure CN121256264A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of pipeline safety technology, and in particular to a method, apparatus, equipment and medium for evaluating pipelines with irregular corrosion defects. Background Technology
[0002] Pipeline transportation is a crucial method for transporting oil and natural gas. Over long-term use, corrosion damage on the pipeline surface can reduce its load-bearing capacity. Furthermore, due to the strong diffusivity and permeability of hydrogen, especially in pipelines that have been in service for many years, hydrogen atoms are more likely to accumulate at corrosion defects, leading to hydrogen embrittlement and increasing the likelihood of pipeline failure, thus affecting operational safety. Most corrosion defects on pipeline surfaces are irregular, making it difficult to comprehensively reflect the pipeline's failure risk using only a single deterministic analysis method. Moreover, simplified assessments of irregular defects often deviate significantly from the actual burst capacity of the pipeline.
[0003] As can be seen from the above, how to improve the accuracy of the evaluation of pipelines with irregular corrosion defects, reduce the deviation between the evaluation results and the actual bursting capacity of the pipeline, and improve the safety of pipeline operation are problems to be solved in this field. Summary of the Invention
[0004] In view of this, the purpose of this invention is to provide a method, apparatus, equipment, and medium for evaluating pipelines with irregular corrosion defects, which can improve the accuracy of evaluating pipelines with irregular corrosion defects, reduce the deviation between the evaluation results and the actual bursting capacity of the pipeline, and enhance the safety of pipeline operation. The specific solution is as follows:
[0005] Firstly, this application discloses a method for evaluating pipelines with irregular corrosion defects, including:
[0006] Three-dimensional scanning data of pipelines with irregular corrosion defects and pressure fluctuation data of pipe sections are acquired. Parametric statistical analysis is performed on the corrosion defect characteristics in the three-dimensional scanning data and the pressure fluctuation data of pipe sections to obtain the parametric statistical analysis results.
[0007] A limit state function and a corrosion growth model are constructed. Based on the limit state function and the corrosion growth model, a limit state equation for an irregularly corroded pipeline is established. The limit state equation is then modified to obtain the limit state equation.
[0008] Using the limit state equation and the results of the parameter statistical analysis, the failure probability of the pipeline with irregular corrosion defects under hydrogen-free and different hydrogen doping ratios is calculated.
[0009] A parameter sensitivity analysis is performed on the failure probability of the pipeline so that the pipeline with irregular corrosion defects can be evaluated based on the results of the parameter sensitivity analysis.
[0010] Optionally, acquiring the three-dimensional scanning data of the irregularly corroded pipeline and the pressure fluctuation data of the pipeline segment includes:
[0011] Obtain three-dimensional scanning data of a pipeline with irregular corrosion defects; the three-dimensional scanning data is obtained by scanning the outline of the corrosion defects of the pipeline with irregular corrosion defects using a handheld three-dimensional laser scanner; the three-dimensional scanning data includes the depth and length of the corrosion defects;
[0012] Acquire real-time pressure fluctuation data of the pipe section collected on site; the pressure fluctuation data of the pipe section includes pipe diameter, wall thickness, yield strength, tensile strength, operating pressure, corrosion rate of depth and length.
[0013] Optionally, the step of performing parametric statistical analysis on the corrosion defect characteristics in the three-dimensional scan data and the pipe segment pressure fluctuation data includes:
[0014] Histograms were plotted based on the corrosion defect depth and length in the three-dimensional scanning data, and the histograms were subjected to parametric statistical analysis using the distribution fit goodness test method.
[0015] Statistical analysis of the corrosion rates of pipe diameter, wall thickness, yield strength, tensile strength, operating pressure, depth, and length in the pressure fluctuation data of the pipe section was performed.
[0016] Optionally, the limiting state function is:
[0017] ;
[0018] in, The failure pressure of a pipeline with irregular corrosion defects. The working pressure for pipelines with irregular corrosion defects;
[0019] The corrosion growth model is as follows:
[0020] ;
[0021] ;
[0022] ;
[0023] ;
[0024] in, The initial depth of the corrosion defect. The initial length of the corrosion defect. For the growth rate of corrosion defect depth, This represents the growth rate of the corrosion defect length.
[0025] Optionally, the limit state equation for the irregularly corroded pipeline is:
[0026] ;
[0027] ;
[0028] in, Let D be the initial depth of the corrosion defect, and D be the outer diameter of the pipe with irregular corrosion defects. For the growth rate of corrosion defect depth, The growth rate of corrosion defect length, The failure pressure of a pipeline with irregular corrosion defects. For pipelines with irregular corrosion defects, the working pressure The axial length of the defect. t represents the ultimate tensile strength of the pipe, and t represents the pipe wall thickness.
[0029] The limiting state equation is:
[0030] .
[0031] Optionally, the step of calculating the pipeline failure probability of irregularly corroded pipelines under hydrogen-free and different hydrogen doping ratios using the limiting state equation and the results of the parameter statistical analysis includes:
[0032] The program was written using Monte Carlo simulation methods and MATLAB software.
[0033] Using a preset time series prediction model, corrosion pipeline evaluation model, and Reynolds stress transfer model, and based on the program, limit state function, limit state equation, and parameter statistical analysis results, the pipeline failure probability of irregular corrosion defect pipelines under hydrogen-free and different hydrogen doping ratios is calculated.
[0034] Optionally, the parameter sensitivity analysis of the pipeline failure probability includes:
[0035] A parameter sensitivity analysis was performed on the failure probability of the pipeline using a corrosion pipeline assessment model to obtain the parameter sensitivity analysis results of the influence of each random variable on the failure probability of the pipeline. The random variables include pipeline size and material parameters, pipeline operating pressure, initial length and depth of pipeline corrosion defects, and pipeline corrosion defect growth rate. The pipeline size and material parameters include pipeline wall thickness, pipeline diameter, and pipeline tensile strength. The corrosion defect growth rate includes the corrosion defect length growth rate and the corrosion defect depth growth rate.
[0036] Secondly, this application discloses an evaluation device for pipelines with irregular corrosion defects, comprising:
[0037] The parameter sensitivity analysis of the pipeline failure probability includes:
[0038] A parameter sensitivity analysis was performed on the failure probability of the pipeline using a corrosion pipeline assessment model to obtain the parameter sensitivity analysis results of the influence of each random variable on the failure probability of the pipeline. The random variables include pipeline size and material parameters, pipeline operating pressure, initial length and depth of pipeline corrosion defects, and pipeline corrosion defect growth rate. The pipeline size and material parameters include pipeline wall thickness, pipeline diameter, and pipeline tensile strength. The corrosion defect growth rate includes the corrosion defect length growth rate and the corrosion defect depth growth rate.
[0039] Thirdly, this application discloses an electronic device, including:
[0040] Memory, used to store computer programs;
[0041] A processor is used to execute the computer program to implement the aforementioned evaluation method for pipelines with irregular corrosion defects.
[0042] Fourthly, this application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed method for evaluating irregularly corroded pipelines.
[0043] As can be seen, this application provides an evaluation method for irregularly corroded pipelines, including acquiring three-dimensional scanning data and pressure fluctuation data of the pipeline with irregular corrosion defects; performing parametric statistical analysis on the corrosion defect characteristics in the three-dimensional scanning data and the pressure fluctuation data to obtain the parametric statistical analysis results; constructing a limit state function and a corrosion growth model; establishing a limit state equation for the pipeline with irregular corrosion defects based on the limit state function and the corrosion growth model; modifying the limit state equation to obtain the limit state equation; calculating the pipeline failure probability of the pipeline with irregular corrosion defects under hydrogen-free and different hydrogen doping ratios using the limit state equation and the parametric statistical analysis results; and performing parametric sensitivity analysis on the pipeline failure probability to evaluate the pipeline with irregular corrosion defects based on the parametric sensitivity analysis results. This application acquires three-dimensional scanning data and pressure fluctuation data of irregularly corroded pipelines, enabling precise acquisition of the defect geometric features. Statistical analysis of corrosion defect features in the three-dimensional scanning data and pressure fluctuation data is performed. Addressing the issue of traditional methods neglecting the statistical nature of individual irregular corrosion defect size parameters, a limit state function and corrosion growth model are constructed. Based on these, a limit state equation for the irregularly corroded pipeline is established. This equation is then modified to obtain a final limit state formula, improving the accuracy of pipeline evaluation. Using the limit state equation and the results of the statistical analysis, the pipeline failure probability under hydrogen-free and different hydrogen doping ratios is calculated. Parameter sensitivity analysis is then performed on the pipeline failure probability to evaluate the pipeline based on the results, reducing the deviation between the assessment results and the actual burst capability of the pipeline, and improving the safety and reliability of pipeline operation. Attached Figure Description
[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0045] Figure 1 This application discloses a flowchart of an evaluation method for pipelines with irregular corrosion defects.
[0046] Figure 2 This is a fitting diagram of the corrosion length distribution of defect 1 disclosed in this application;
[0047] Figure 3 This is a fitted diagram of the corrosion depth distribution of defect 1 disclosed in this application;
[0048] Figure 4 This is a fitting diagram of the corrosion length distribution of defect 2 disclosed in this application;
[0049] Figure 5 This is a fitted diagram of the corrosion depth distribution of defect 2 disclosed in this application;
[0050] Figure 6 This is a schematic diagram of stress-intensity interference disclosed in this application;
[0051] Figure 7 This application discloses a failure probability diagram of the YM pipeline (including defect 1) calculated by various models over 25 years.
[0052] Figure 8 This application discloses a failure probability diagram of a YM pipeline (including defect 1) calculated using various models over a 50-year period.
[0053] Figure 9 This application discloses a failure probability diagram of a GY pipeline (including defect 2) calculated by various models over 25 years.
[0054] Figure 10 This application discloses a failure probability diagram of a GY pipeline (including defect 2) calculated by various models over 50 years.
[0055] Figure 11 This is a failure probability diagram of a YM pipeline disclosed in this application under different hydrogen doping ratios;
[0056] Figure 12 This application discloses a failure probability diagram of a GY pipeline under different hydrogen doping ratios.
[0057] Figure 13 This application discloses a failure probability diagram for a YM pipeline based on ASME B31G-2009, DNV RP-F101, and RSTRENG models.
[0058] Figure 14 This application discloses a failure probability diagram for a GY pipeline based on ASME B31G-2009, DNV RP-F101, and RSTRENG models.
[0059] Figure 15 This is a graph showing the effect of the change in the coefficient of variation of pipe wall thickness on the probability of pipe failure, as disclosed in this application.
[0060] Figure 16 This is a graph showing the effect of the change in the coefficient of variation of the pipe diameter on the probability of pipe failure, as disclosed in this application.
[0061] Figure 17This is a graph showing the effect of the change in the coefficient of variation of the tensile strength of a pipe material on the probability of pipe failure, as disclosed in this application.
[0062] Figure 18 This is a graph showing the failure probability variation of a corrosion-defect hydrogen-doped pipeline disclosed in this application under different operating pressures.
[0063] Figure 19 This is a graph showing the failure probability of a hydrogen-doped pipeline with corrosion defects under different coefficients of variation for the initial length of corrosion defects, as disclosed in this application.
[0064] Figure 20 This application discloses the coefficient of variation (cov) of a hydrogen-doped pipeline with corrosion defects at different initial depths of corrosion defects. Failure probability variation curve under ( );
[0065] Figure 21 This is a graph showing the failure probability variation of a hydrogen-doped pipeline with corrosion defects under different initial lengths c of corrosion defects, as disclosed in this application.
[0066] Figure 22 This is a curve showing the failure probability of a hydrogen-doped pipeline with corrosion defects under different initial depths of corrosion defects, as disclosed in this application.
[0067] Figure 23 This is a schematic diagram of the structure of an evaluation device for irregular corrosion defects in a pipeline disclosed in this application;
[0068] Figure 24 This application provides a structural diagram of an electronic device. Detailed Implementation
[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0070] Pipeline transportation is a crucial method for transporting oil and natural gas. Over long-term use, corrosion damage on the pipeline surface reduces its load-bearing capacity. Furthermore, due to the strong diffusivity and permeability of hydrogen, especially in pipelines that have been in service for many years, hydrogen atoms easily accumulate at corrosion defects, leading to hydrogen embrittlement and increasing the likelihood of pipeline failure, thus affecting operational safety. Most corrosion defects on pipeline surfaces are irregular, making it difficult to comprehensively reflect pipeline failure risks using only a single deterministic analysis method. Moreover, simplified assessments of irregular defects often deviate significantly from the actual burst capacity of the pipeline. Therefore, improving the accuracy of pipeline evaluation for irregular corrosion defects, reducing the discrepancy between assessment results and the actual burst capacity, and enhancing pipeline operational safety are pressing issues that need to be addressed in this field.
[0071] See Figure 1 As shown in the figure, this invention discloses an evaluation method for pipelines with irregular corrosion defects, which may specifically include:
[0072] Step S11: Obtain three-dimensional scanning data of the pipeline with irregular corrosion defects and pressure fluctuation data of the pipeline section. Perform parameter statistical analysis on the corrosion defect characteristics in the three-dimensional scanning data and the pressure fluctuation data of the pipeline section to obtain the parameter statistical analysis results.
[0073] In this embodiment, three-dimensional scanning data of an irregularly corroded pipeline is acquired. This three-dimensional scanning data is obtained by scanning the corrosion defect contour of the pipeline using a handheld three-dimensional laser scanner. The three-dimensional scanning data includes the corrosion defect depth and length. Real-time pressure fluctuation data of the pipeline section is acquired. A histogram is plotted based on the corrosion defect depth and length in the three-dimensional scanning data, and the histogram is subjected to parametric statistical analysis using a distribution fit goodness test method. Parametric statistical analysis is performed on the corrosion rates of pipe diameter, wall thickness, yield strength, tensile strength, operating pressure, depth, and length in the pipeline section pressure fluctuation data to obtain the parametric statistical analysis results. The pipeline section pressure fluctuation data includes the corrosion rates of pipe diameter, wall thickness, yield strength, tensile strength, operating pressure, depth, and length.
[0074] This application uses a handheld 3D (Three Dimensions) laser scanner to scan the contour of corrosion defects, obtain the depth and length of corrosion defects in the pipeline, draw a histogram based on the depth and length of corrosion defects, and statistically analyze its distribution type according to the KS (Kolmogorov-Smirnov) test method; the depth and length of corrosion defects include the mean, standard deviation, variance, skewness and coefficient of variation.
[0075] Two sections of pipeline, referred to as YM pipeline and GY pipeline, are used as research objects. Corrosion defects on their pipe walls are denoted as defect 1 and defect 2, respectively. Both pipelines are made of X52 steel, with a diameter of 457 mm and a wall thickness of 9.5 mm. A handheld 3D laser scanner was used to scan the contours of the corrosion defects, obtaining three-dimensional scanning data of the pipeline corrosion defects. Histograms were plotted on the distribution of corrosion defect depth and length data, and the types of corrosion defect length and depth distributions were statistically analyzed. The corrosion length distribution of defect 1 is fitted as follows: Figure 2 As shown, this can well reflect the right-skewed characteristic of the corrosion defect length in this pipe section. The corrosion depth distribution of defect 1 is fitted as follows: Figure 3 As shown, the corrosion length distribution of defect 2 is fitted as follows: Figure 4 As shown, the corrosion depth distribution of defect 2 is fitted as follows: Figure 5 As shown.
[0076] Based on the Kolmogorov-Smirnov distribution goodness-of-fit test, the corrosion length of defect 1 is determined to follow a Gen-logistic (Generalized Logistic Distribution) model. The statistical data characteristics of the corrosion defect length and depth include mean, standard deviation, variance, skewness, and coefficient of variation. The statistical data characteristics of the corrosion length of corrosion defect 1 are shown in Table 1.
[0077] Table 1. Statistical characteristics of corrosion length for corrosion defect 1
[0078]
[0079] The statistical data characteristics of the corrosion depth of corrosion defect 1 are shown in Table 2:
[0080] Table 2 Statistical characteristics of corrosion depth for corrosion defect 1
[0081]
[0082] The statistical data characteristics of the corrosion length of corrosion defect 2 are shown in Table 3:
[0083] Table 3 Statistical characteristics of corrosion length of corrosion defect 2
[0084]
[0085] The statistical data characteristics of the corrosion depth of corrosion defect 2 are shown in Table 4:
[0086] Table 4 Statistical characteristics of corrosion depth for corrosion defect 2
[0087]
[0088] Table 2 shows that the depth of corrosion defect 1 follows a normal distribution with a mean of 1.8951 mm and a variance of 1.1799 mm. Table 4 and... Figure 5 It can be seen that the depth of defect 2 is mostly shallow, with most of the data concentrated on the left side and a small number of depth maxima. It exhibits the characteristic of having a long tail on the right side, and is more suitable for a log-normal distribution.
[0089] In this embodiment, other relevant random variables are also statistically analyzed. These random variables include pipe diameter, wall thickness, yield strength, tensile strength, operating pressure, and corrosion rate, as shown in Table 5.
[0090] Table 5 Statistical characteristics of relevant random variables
[0091]
[0092] Step S12: Construct the limit state function and corrosion growth model. Based on the limit state function and corrosion growth model, establish the limit state equation for the pipeline with irregular corrosion defects. Modify the limit state equation to obtain the limit state equation.
[0093] In this embodiment, the limit state function is:
[0094] ;
[0095] in, The failure pressure of a pipeline with irregular corrosion defects. The working pressure for pipelines with irregular corrosion defects;
[0096] The corrosion growth model is as follows:
[0097] ;
[0098] ;
[0099] ;
[0100] ;
[0101] in, The initial depth of the corrosion defect. The initial length of the corrosion defect. For the growth rate of corrosion defect depth, This represents the growth rate of corrosion defect length.
[0102] Taking the DNV RP-F101 standard formula for calculating the failure pressure of corrosion-defective pipelines as an example, the limit state equation for the irregularly corroded pipeline is:
[0103] ;
[0104] ;
[0105] in, Let D be the initial depth of the corrosion defect, and D be the outer diameter of the pipe with irregular corrosion defects. For the growth rate of corrosion defect depth, The growth rate of corrosion defect length, The failure pressure of a pipeline with irregular corrosion defects. For pipelines with irregular corrosion defects, the working pressure The axial length of the defect. t represents the ultimate tensile strength of the pipe, and t represents the pipe wall thickness.
[0106] The limiting state equation is:
[0107] .
[0108] In this embodiment, the failure probability is the probability that the structure fails to achieve its target function under the same time and conditions. Stress refers to the internal forces generated when a structure deforms due to external influences. It is generally represented by 's' and can be expressed by the following formula:
[0109] ;
[0110] The stress that resists structural failure is called strength, usually denoted by S, and can be expressed by the following formula:
[0111] ;
[0112] In this embodiment, due to the variability of pipe material properties and design parameters, both the stress function and the strength function are essentially random variables. A schematic diagram of stress-strength interference is shown below. Figure 6 As shown, when the stress distribution and intensity distribution probabilistically overlap, such as Figure 6 The overlapping areas shown in the diagram can potentially lead to structural failure. For example... Figure 6 As shown, when the probability density function curves f(s) and g(S) of stresses s(x) and S(r) interfere, the average value of the working stress is usually less than the average value of the structural strength. In some cases, the stress of the structure will exceed its material strength, and the failure probability of the structure will be greater than 0.
[0113] Furthermore, based on the stress-strength interference theory, the limit state expression can be defined as follows, which can be used to determine whether the structure is in a safe and reliable state:
[0114] ;
[0115] Based on this, a limit state function is established when... When, the pipeline fails; when At that time, the pipeline was operating normally.
[0116] Assuming that the growth rate of corrosion defects under steady-state conditions follows a linear law, a corrosion growth model is established. Taking the failure pressure calculation formula of DNV RP-F101 standard corrosion defect pipeline as an example, the limit state equation of irregular corrosion defect pipeline is constructed. Based on the tensile strength of 492MPa for corrosion defect pipeline with a hydrogen doping ratio of 50%, the limit state equation of pipeline with a hydrogen doping ratio of 50% is modified to obtain the limit state equation.
[0117] Step S13: Using the limit state equation and the results of the parameter statistical analysis, calculate the failure probability of the pipeline with irregular corrosion defects under conditions of no hydrogen and different hydrogen doping ratios.
[0118] In this embodiment, a program is written using the Monte Carlo simulation method and MATLAB software; using a preset time series prediction model, corrosion pipeline evaluation model, Reynolds stress transfer model, and based on the program, limit state function, limit state equation, and parameter statistical analysis results, the pipeline failure probability of irregular corrosion defect pipelines under hydrogen-free and different hydrogen doping ratios is calculated.
[0119] In this embodiment, the pipeline failure probability is calculated using three models: ASME B31G-2009 (time series prediction model), DNV RP-F101 (corrosion pipeline assessment model), and RSTRENGY (Reynolds stress transfer model).
[0120] In this embodiment, Monte Carlo simulation is a numerical calculation method based on random trials. It generally solves approximate solutions to engineering problems by simulating the statistical distribution characteristics of random variables. When the sample size of the simulation is large enough, the probability of an event can be approximated by the frequency of the event occurring in multiple trials. Its core idea can be expressed mathematically as follows:
[0121] ;
[0122] in, As an indicator function, its mathematical expression is as follows:
[0123] ;
[0124] Let the total number of simulations be N, and the number of times g(x) < 0 be N. According to the law of large numbers, the unbiased estimate of the final failure probability is:
[0125] ;
[0126] Understandably, the core advantage of Monte Carlo simulation lies in its high adaptability to nonlinear and complex systems, as it does not require mathematical simplification of the limit state function. It can effectively solve the complex limit state equation problem involved in the reliability assessment of corroded pipelines.
[0127] For the limit state equations corresponding to the different corrosion defect pipeline failure prediction formulas constructed above, as well as the limit state equations for hydrogen-doped pipelines, combined with the parametric statistical analysis results based on three-dimensional scanning data and the statistical distribution characteristics of other relevant variables, a MATLAB program was used to calculate the failure probability of corrosion defect pipelines under hydrogen-free conditions and under different hydrogen doping ratios using the Monte Carlo simulation method. The Monte Carlo simulation iteration was set to... .
[0128] The failure probabilities of the YM pipeline (including defect 1) calculated by various models over 25 years are as follows: Figure 7 As shown, the failure probabilities of the YM pipeline (including defect 1) calculated by various models over 50 years are as follows: Figure 8 As shown, the failure probabilities of the GY pipeline (including defect 2) calculated by various models over 25 years are as follows: Figure 9 As shown, the failure probabilities of the GY pipeline (including defect 2) calculated by various models over 50 years are as follows: Figure 10 As shown, the horizontal axis of the curve corresponds to time, and the vertical axis corresponds to the failure probability of the evaluation model. It can be seen that the failure probability of the corrosion-damaged pipeline calculated by these three different evaluation models increases with time, but the calculation results also differ. Specifically, the failure probability value calculated by the ASME B31G-2009 evaluation model is generally higher, while the failure probability calculated by the RSTRENG evaluation model is the lowest. As the pipeline's service life increases, the failure probability calculated by the ASME B31G-2009 model becomes increasingly closer to the pipeline failure probability predicted by the DNV RP-F101 model, and the failure probabilities calculated by each model show a relatively gradual increasing trend after 25 years.
[0129] Based on the limit state equations described above, it is known that the higher the failure pressure calculated by the evaluation model, the fewer times the pipeline with corrosion defects will fail, resulting in a lower pipeline failure probability. Therefore, the pipeline failure pressure calculated by the RSTRENG model is the highest, while the failure pressure evaluated by the DNV RP-F101 model falls between the failure pressure calculated by ASME B31G-2009 and RSTRENG.
[0130] Depend on Figures 7-10It can be seen that the failure probability of GY pipelines is generally lower than that of YM pipelines. Taking the failure probability calculated by DNV RP-F101 as an example, at a time interval of 50 years, the failure probability of YM pipelines reaches 0.614, while the failure probability of GY pipelines is only 0.541. However, at 42 years since the last inspection, the failure probability of YM pipelines has reached 0.55. Therefore, the remaining lifespan of GY pipelines will be nearly 8 years longer than that of YM pipelines.
[0131] Based on the DNV-RP-F101 model, the failure probability of the YM pipeline under different hydrogen doping ratios (0, 2.5%, 12.5%, 25%, and 50%) is calculated as follows: Figure 11 As shown, the failure probability of the GY pipeline under different hydrogen doping ratios (0, 2.5%, 12.5%, 25%, and 50%) is as follows: Figure 12 As shown, the horizontal axis represents time, and the vertical axis represents the failure probability. It can be seen that when the hydrogen doping ratio reaches 50%, the failure probability increases significantly; when the hydrogen doping ratio is less than 50%, the failure probability does not change significantly compared to the hydrogen-free environment.
[0132] Under a 50% hydrogen doping ratio, the failure probability of the YM pipeline based on ASME B31G-2009, DNV RP-F101, and RSTRENG models is as follows: Figure 13 As shown, the failure probabilities of the GY pipeline based on ASME B31G-2009, DNV RP-F101, and RSTRENG models are as follows: Figure 14 As shown, the failure probability curve of the YM pipeline in the absence of hydrogen is compared with... Figure 13 and Figure 14 A comparison of the curves reveals that under hydrogen doping conditions, the failure probabilities calculated by all models increase. For example, over a 25-year time interval, the failure probability calculated by the ASME B31G-2009 model increases by approximately 0.03; the failure probability calculated by the DNV RP-F101 model increases by 0.02; and the change in failure probability calculated by the RSTRENG model is only... This indicates that the RSTRENG model has poor applicability under hydrogen-doped conditions.
[0133] Based on the failure probability calculation results of the DNV RP-F101 model, the failure probabilities of YM pipeline and GY pipeline in hydrogen-free and hydrogen-doped states are shown in Table 6 below, 50 years after the most recent inspection:
[0134] Table 6 Failure Probability in Hydrogen-Free and Hydrogen-Doped States
[0135]
[0136] As shown in Table 6, taking the YM pipeline as an example, under hydrogen-doped conditions, the failure probability reaches 0.6123 after a 48-year interval, which is close to the failure probability level under hydrogen-free conditions after a 50-year interval. Therefore, it can be concluded that when the hydrogen doping ratio is 50%, the pipeline's service life will be shortened by 2 years compared to the hydrogen-free state.
[0137] Step S14: Perform parameter sensitivity analysis on the failure probability of the pipeline so as to evaluate the pipeline with irregular corrosion defects based on the parameter sensitivity analysis results.
[0138] In this embodiment, the parameter sensitivity analysis process is as follows: A corrosion pipeline assessment model is used to perform parameter sensitivity analysis on the pipeline failure probability to obtain the parameter sensitivity analysis results of the influence of each random variable on the pipeline failure probability; the random variables include pipeline size and material parameters, pipeline operating pressure, initial length and depth of pipeline corrosion defects, and pipeline corrosion defect growth rate; the pipeline size and material parameters include pipeline wall thickness, pipeline diameter, and pipeline tensile strength; the corrosion defect growth rate includes the corrosion defect length growth rate and the corrosion defect depth growth rate.
[0139] In this embodiment, the coefficient of variation can be used to represent parameter sensitivity, which characterizes the dispersion and uncertainty of the random variable. The definition of the coefficient of variation is shown in the following equation:
[0140] ;
[0141] in, The average value of the variable. Let be the standard deviation. While keeping the mean of each random variable constant, the failure probability of a hydrogen-doped pipeline with corrosion defects under different coefficients of variation is obtained by changing the standard deviation of the variables.
[0142] Based on the analysis of specific implementation examples and the failure probability calculation results of the aforementioned models, it is found that the ASMEB31G-2009 model has a high degree of conservatism in its prediction results. The RSTRENG evaluation model is based on rheological stress for calculation. Therefore, the DNV RP-F101 evaluation model is selected to analyze the influence of each random variable on the pipeline failure probability and obtain the parameter sensitivity analysis results of the influence of each random variable on the pipeline failure probability.
[0143] The curve showing the effect of the change in the pipe wall thickness coefficient of variation (cov(t)) on the pipe failure probability is as follows: Figure 15 As shown in the figure, the curve of the effect of the change in the pipe diameter coefficient of variation cov(D) on the pipe failure probability is as follows. Figure 16As shown in the figure, the curve of the effect of the change in the coefficient of variation of the tensile strength of the pipe, cov(U), on the failure probability of the pipe is as follows: Figure 17 As shown, a sensitivity analysis of the pipeline operating pressure on the pipeline failure probability was performed. The failure probability curves of the hydrogen-doped pipeline with corrosion defects under different operating pressures are shown in the figure. Figure 18 As shown.
[0144] The influence of the initial length and depth of corrosion defects on the pipeline failure probability was analyzed, and the coefficient of variation (cov) of the corrosion defect in hydrogen-doped pipelines with different initial lengths of corrosion defects was analyzed. The failure probability change curve under ( ) is as follows Figure 19 As shown, the coefficient of variation (cov) of the corrosion defect in the hydrogen-doped pipeline varies with different initial depths of corrosion defects. The failure probability change curve under ( ) is as follows Figure 20 As shown.
[0145] Finally, a parametric sensitivity analysis was conducted on the effect of corrosion defect growth rate on pipeline failure probability, and the corrosion defect growth rate of hydrogen-doped pipelines was analyzed under different initial corrosion defect lengths (cov). The failure probability change curve under ( ) is as follows Figure 21 As shown, the coefficient of variation (cov) of the hydrogen-doped pipeline with corrosion defects varies with different initial depths of corrosion defects. The failure probability change curve under ( ) is as follows Figure 22 As shown.
[0146] Based on the above curves, parameter sensitivity analysis reveals that cov(t), cov(D), cov(P), and cov( The impact of changes in these variables on pipeline failure probability is not a simple positive correlation. Instead, after a certain time interval, the pipeline failure probability decreases as the coefficients of variation of these variables increase. Regarding pipeline size and material, pipeline failure probability is highly sensitive to cov(t) and cov(U), especially pipeline wall thickness, which has the greatest impact on the failure probability. Furthermore, cov(P) and cov(U)... Changes in the pressure of the pipeline can significantly alter the probability of pipeline failure. Therefore, in the absence of changes to the pipe material, in hydrogen-doped environments, it is of great significance to reasonably control the pipeline operating pressure and strengthen the monitoring of deep corrosion rates to improve pipeline reliability.
[0147] Additionally, cov( ), cov( ) and cov( The changes in corrosion defect depth have a generally weak impact on the failure probability. Specifically, the initial length of the corrosion defect and the corrosion rate along the defect length have negligible effects on the pipeline's failure probability. In contrast, the initial depth of the corrosion defect has a slightly greater impact on the failure probability than the initial length, indicating that the uncertainty of the corrosion defect depth parameter during pipeline operation can have a certain degree of impact on its reliability.
[0148] This application utilizes a handheld 3D laser scanner to scan the contours of corrosion defects on-site, obtaining characteristic data corresponding to the corrosion-defective pipeline. Then, using the 3D scan data and on-site collected pipeline pressure fluctuation data, parametric statistical analysis is performed on the corrosion defect characteristics. A limit state function and corrosion growth model for the pipeline containing corrosion defects are then constructed. Taking the DNV RP-F101 standard formula for calculating the failure pressure of corrosion-defective pipelines as an example, a limit state equation for the corrosion-defective pipeline is established, and the limit state equation for hydrogen-doped pipelines is modified to obtain limit state equations for the pipeline under hydrogen-free conditions and different hydrogen doping ratios. Using the limit state equations and the results of parametric statistical analysis, the pipeline failure probability under hydrogen-free and different hydrogen doping ratios is calculated. Simultaneously, through parameter sensitivity analysis, key sensitive factors affecting the failure probability of hydrogen-doped pipelines are studied. In this way, by using 3D scanning technology and other new technologies, the defect geometric characteristics of the pipeline under test are accurately obtained, enabling a more accurate assessment of the reliability of pipelines with irregular corrosion defects. This is of great significance for the safety assessment of hydrogen energy transmission pipelines that consider the actual morphology of corrosion defects.
[0149] In this embodiment, three-dimensional scanning data and pressure fluctuation data of an irregularly corroded pipeline are acquired. Statistical analysis of the corrosion defect characteristics in the three-dimensional scanning data and the pressure fluctuation data is performed to obtain the statistical analysis results. A limit state function and a corrosion growth model are constructed. Based on the limit state function and the corrosion growth model, a limit state equation for the irregularly corroded pipeline is established. The limit state equation is then modified to obtain the limit state equation. Using the limit state equation and the statistical analysis results, the pipeline failure probability under hydrogen-free and different hydrogen doping ratios is calculated. A parameter sensitivity analysis is performed on the pipeline failure probability to evaluate the irregularly corroded pipeline based on the parameter sensitivity analysis results. This application acquires three-dimensional scanning data and pressure fluctuation data of irregularly corroded pipelines, enabling precise acquisition of the defect geometric features. Statistical analysis of corrosion defect features in the three-dimensional scanning data and pressure fluctuation data is performed. Addressing the issue of traditional methods neglecting the statistical nature of individual irregular corrosion defect size parameters, a limit state function and corrosion growth model are constructed. Based on these, a limit state equation for the irregularly corroded pipeline is established. This equation is then modified to obtain a final limit state formula, improving the accuracy of pipeline evaluation. Using the limit state equation and the results of the statistical analysis, the pipeline failure probability under hydrogen-free and different hydrogen doping ratios is calculated. Parameter sensitivity analysis is then performed on the pipeline failure probability to evaluate the pipeline based on the results, reducing the deviation between the assessment results and the actual burst capability of the pipeline, and improving the safety and reliability of pipeline operation.
[0150] See Figure 23 As shown in the figure, an embodiment of the present invention discloses an evaluation device for pipelines with irregular corrosion defects, which may specifically include:
[0151] The data acquisition and parameter statistical analysis module 11 is used to acquire three-dimensional scanning data of irregular corrosion defect pipelines and pipe section pressure fluctuation data, perform parameter statistical analysis on the corrosion defect characteristics in the three-dimensional scanning data and the pipe section pressure fluctuation data, and obtain parameter statistical analysis results.
[0152] The model building and equation correction module 12 is used to build the limit state function and the corrosion growth model. Based on the limit state function and the corrosion growth model, the limit state equation of the irregular corrosion defect pipeline is established, and the limit state equation is corrected to obtain the limit state equation.
[0153] The pipeline failure probability calculation module 13 is used to calculate the pipeline failure probability of irregular corrosion defect pipelines under hydrogen-free and different hydrogen doping ratios by using the limit state equation and the parameter statistical analysis results.
[0154] The parameter sensitivity analysis module 14 is used to perform parameter sensitivity analysis on the failure probability of the pipeline, so as to evaluate the pipeline with irregular corrosion defects based on the parameter sensitivity analysis results.
[0155] In this embodiment, three-dimensional scanning data and pressure fluctuation data of an irregularly corroded pipeline are acquired. Statistical analysis of the corrosion defect characteristics in the three-dimensional scanning data and the pressure fluctuation data is performed to obtain the statistical analysis results. A limit state function and a corrosion growth model are constructed. Based on the limit state function and the corrosion growth model, a limit state equation for the irregularly corroded pipeline is established. The limit state equation is then modified to obtain the limit state equation. Using the limit state equation and the statistical analysis results, the pipeline failure probability under hydrogen-free and different hydrogen doping ratios is calculated. A parameter sensitivity analysis is performed on the pipeline failure probability to evaluate the irregularly corroded pipeline based on the parameter sensitivity analysis results. This application acquires three-dimensional scanning data and pressure fluctuation data of irregularly corroded pipelines, enabling precise acquisition of the defect geometric features. Statistical analysis of corrosion defect features in the three-dimensional scanning data and pressure fluctuation data is performed. Addressing the issue of traditional methods neglecting the statistical nature of individual irregular corrosion defect size parameters, a limit state function and corrosion growth model are constructed. Based on these, a limit state equation for the irregularly corroded pipeline is established. This equation is then modified to obtain a final limit state formula, improving the accuracy of pipeline evaluation. Using the limit state equation and the results of the statistical analysis, the pipeline failure probability under hydrogen-free and different hydrogen doping ratios is calculated. Parameter sensitivity analysis is then performed on the pipeline failure probability to evaluate the pipeline based on the results, reducing the deviation between the assessment results and the actual burst capability of the pipeline, and improving the safety and reliability of pipeline operation.
[0156] In some specific embodiments, the data acquisition and parameter statistical analysis module 11 may specifically include:
[0157] A three-dimensional scanning data acquisition module is used to acquire three-dimensional scanning data of irregularly corroded pipelines; the three-dimensional scanning data is obtained by scanning the outline of the corrosion defects of the irregularly corroded pipelines using a handheld three-dimensional laser scanner; the three-dimensional scanning data includes the corrosion defect depth and corrosion defect length.
[0158] The pipe segment pressure fluctuation data acquisition module is used to acquire real-time pipe segment pressure fluctuation data collected on site; the pipe segment pressure fluctuation data includes pipe diameter, wall thickness, yield strength, tensile strength, operating pressure, and corrosion rate of depth and length.
[0159] In some specific embodiments, the data acquisition and parameter statistical analysis module 11 may specifically include:
[0160] The first parameter statistical analysis module is used to draw a histogram based on the corrosion defect depth and corrosion defect length in the three-dimensional scanning data, and to perform parameter statistical analysis on the histogram using the distribution fit goodness test method.
[0161] The second parameter statistical analysis module is used to perform parameter statistical analysis on the corrosion rate of pipe diameter, wall thickness, yield strength, tensile strength, operating pressure, depth, and length in the pressure fluctuation data of the pipe section.
[0162] In some specific embodiments, the limiting state function is:
[0163] ;
[0164] in, The failure pressure of a pipeline with irregular corrosion defects. The working pressure for pipelines with irregular corrosion defects;
[0165] The corrosion growth model is as follows:
[0166] ;
[0167] ;
[0168] ;
[0169] ;
[0170] in, The initial depth of the corrosion defect. The initial length of the corrosion defect. For the growth rate of corrosion defect depth, This represents the growth rate of the corrosion defect length.
[0171] In some specific embodiments, the limit state equation for the irregularly corroded pipeline is:
[0172] ;
[0173] ;
[0174] in, Let D be the initial depth of the corrosion defect, and D be the outer diameter of the pipe with irregular corrosion defects. For the growth rate of corrosion defect depth, The growth rate of corrosion defect length, The failure pressure of a pipeline with irregular corrosion defects. For pipelines with irregular corrosion defects, the working pressure The axial length of the defect. t represents the ultimate tensile strength of the pipe, and t represents the pipe wall thickness.
[0175] The limiting state equation is:
[0176] .
[0177] In some specific embodiments, the pipeline failure probability calculation module 13 may specifically include:
[0178] The programming module is used to write programs using Monte Carlo simulation methods and MATLAB software.
[0179] The pipeline failure probability calculation module is used to calculate the pipeline failure probability of irregularly corroded pipelines under hydrogen-free and different hydrogen doping ratios by using a preset time series prediction model, corrosion pipeline evaluation model, Reynolds stress transfer model, and based on the program, limit state function, limit state equation and parameter statistical analysis results.
[0180] In some specific embodiments, the parameter sensitivity analysis module 14 may specifically include:
[0181] The parameter sensitivity analysis result determination module is used to perform parameter sensitivity analysis on the failure probability of the pipeline using a corrosion pipeline assessment model, so as to obtain the parameter sensitivity analysis results of the influence of each random variable on the failure probability of the pipeline; the random variables include pipeline size and material parameters, pipeline operating pressure, initial length and depth of pipeline corrosion defects, and pipeline corrosion defect growth rate; the pipeline size and material parameters include pipeline wall thickness, pipeline diameter, and pipeline tensile strength; the corrosion defect growth rate includes corrosion defect length growth rate and corrosion defect depth growth rate.
[0182] Figure 24This is a schematic diagram of an electronic device provided in an embodiment of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the evaluation method for irregularly corroded pipelines disclosed in any of the foregoing embodiments.
[0183] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.
[0184] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored on it include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.
[0185] The operating system 221 manages and controls the various hardware devices on the electronic device 20 and the computer program 222 to enable the processor 21 to perform calculations and processing on the data 223 in the memory 22. The operating system can be Windows, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the evaluation method for irregularly corroded pipelines executed by the electronic device 20 as disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the evaluation device for irregularly corroded pipelines from external devices, as well as data collected by its own input / output interface 25.
[0186] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.
[0187] Furthermore, embodiments of this application also disclose a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, it implements the evaluation method steps for irregular corrosion defect pipelines disclosed in any of the foregoing embodiments.
[0188] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0189] The above provides a detailed description of the evaluation method, apparatus, equipment, and storage medium for irregular corrosion defects in pipelines provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for evaluating irregular corrosion defects in pipelines, characterized in that, include: Three-dimensional scanning data of pipelines with irregular corrosion defects and pressure fluctuation data of pipe sections are acquired. Parametric statistical analysis is performed on the corrosion defect characteristics in the three-dimensional scanning data and the pressure fluctuation data of pipe sections to obtain the parametric statistical analysis results. A limit state function and a corrosion growth model are constructed. Based on the limit state function and the corrosion growth model, a limit state equation for an irregularly corroded pipeline is established. The limit state equation is then modified to obtain the limit state equation. Using the limit state equation and the results of the parameter statistical analysis, the failure probability of the pipeline with irregular corrosion defects under hydrogen-free and different hydrogen doping ratios is calculated. A parameter sensitivity analysis is performed on the failure probability of the pipeline so that the pipeline with irregular corrosion defects can be evaluated based on the results of the parameter sensitivity analysis.
2. The evaluation method for irregular corrosion defects in pipelines according to claim 1, characterized in that, The acquisition of three-dimensional scanning data of the irregularly corroded pipeline and pressure fluctuation data of the pipeline section includes: Obtain three-dimensional scanning data of a pipeline with irregular corrosion defects; the three-dimensional scanning data is obtained by scanning the outline of the corrosion defects of the pipeline with irregular corrosion defects using a handheld three-dimensional laser scanner; the three-dimensional scanning data includes the depth and length of the corrosion defects; Acquire real-time pressure fluctuation data of the pipe section collected on site; the pressure fluctuation data of the pipe section includes pipe diameter, wall thickness, yield strength, tensile strength, operating pressure, corrosion rate of depth and length.
3. The evaluation method for irregular corrosion defects in pipelines according to claim 1, characterized in that, The parameter statistical analysis of corrosion defect characteristics in the three-dimensional scan data and the pipe section pressure fluctuation data includes: Histograms were plotted based on the corrosion defect depth and length in the three-dimensional scanning data, and the histograms were subjected to parametric statistical analysis using the distribution fit goodness test method. Statistical analysis of the corrosion rates of pipe diameter, wall thickness, yield strength, tensile strength, operating pressure, depth, and length in the pressure fluctuation data of the pipe section was performed.
4. The evaluation method for irregular corrosion defects in pipelines according to claim 1, characterized in that, The limiting state function is: ； in, The failure pressure of a pipeline with irregular corrosion defects. The working pressure for pipelines with irregular corrosion defects; The corrosion growth model is as follows: ； ； ； ； in, The initial depth of the corrosion defect. The initial length of the corrosion defect. For the growth rate of corrosion defect depth, This represents the growth rate of the corrosion defect length.
5. The evaluation method for irregular corrosion defects in pipelines according to claim 1, characterized in that, The limit state equation for the irregularly corroded pipeline is: ； ； in, Let D be the initial depth of the corrosion defect, and D be the outer diameter of the pipe with irregular corrosion defects. For the growth rate of corrosion defect depth, The growth rate of corrosion defect length, The failure pressure of a pipeline with irregular corrosion defects. For pipelines with irregular corrosion defects, the working pressure is... The axial length of the defect. t represents the ultimate tensile strength of the pipe, and t represents the pipe wall thickness. The limiting state equation is: 。 6. The evaluation method for irregular corrosion defects in pipelines according to claim 1, characterized in that, The calculation of pipeline failure probability under hydrogen-free and different hydrogen doping ratios using the limiting state equation and the parameter statistical analysis results includes: The program was written using Monte Carlo simulation methods and MATLAB software. Using a preset time series prediction model, corrosion pipeline evaluation model, and Reynolds stress transfer model, and based on the program, limit state function, limit state equation, and parameter statistical analysis results, the pipeline failure probability of irregular corrosion defect pipelines under hydrogen-free and different hydrogen doping ratios is calculated.
7. The evaluation method for irregular corrosion defects in pipelines according to any one of claims 1 to 6, characterized in that, The parameter sensitivity analysis of the pipeline failure probability includes: A parameter sensitivity analysis was performed on the failure probability of the pipeline using a corrosion pipeline assessment model to obtain the parameter sensitivity analysis results of the influence of each random variable on the failure probability of the pipeline. The random variables include pipeline size and material parameters, pipeline operating pressure, initial length and depth of pipeline corrosion defects, and pipeline corrosion defect growth rate. The pipeline size and material parameters include pipeline wall thickness, pipeline diameter, and pipeline tensile strength. The corrosion defect growth rate includes the corrosion defect length growth rate and the corrosion defect depth growth rate.
8. An evaluation device for pipelines with irregular corrosion defects, characterized in that, include: The data acquisition and parameter statistical analysis module is used to acquire three-dimensional scanning data of pipelines with irregular corrosion defects and pressure fluctuation data of pipe sections, and to perform parameter statistical analysis on the corrosion defect characteristics in the three-dimensional scanning data and the pressure fluctuation data of pipe sections to obtain parameter statistical analysis results. The model building and equation correction module is used to build the limit state function and the corrosion growth model. Based on the limit state function and the corrosion growth model, the limit state equation of the pipeline with irregular corrosion defects is established, and the limit state equation is corrected to obtain the limit state equation. The pipeline failure probability calculation module is used to calculate the pipeline failure probability of irregular corrosion defect pipelines under hydrogen-free and different hydrogen doping ratios by using the limit state equation and the parameter statistical analysis results. The parameter sensitivity analysis module is used to perform parameter sensitivity analysis on the failure probability of the pipeline, so as to evaluate the pipeline with irregular corrosion defects based on the parameter sensitivity analysis results.
9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the evaluation method for irregular corrosion defects in pipelines as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the evaluation method for irregular corrosion defect pipelines as described in any one of claims 1 to 7.