Pipeline failure analysis method, equipment and medium

Abstract

The invention provides a pipeline failure analysis method, equipment and a medium, and relates to the field of oil and gas transmission. The method comprises the following steps: acquiring pipeline service condition information and a pipeline image of a to-be-analyzed pipeline, wherein the pipeline image comprises a damaged part of the to-be-analyzed pipeline; performing primary identification according to the pipeline service condition information and the pipeline image to obtain a corresponding primary identification result; whether secondary identification needs to be carried out on the to-be-analyzed pipeline or not is judged according to the primary identification result, the secondary identification comprises performance testing and analysis of the pipeline, and the analysis precision of the secondary identification is higher than that of the primary identification; if yes, secondary identification is conducted according to the to-be-analyzed pipeline and a contrast pipeline, a failure analysis result of the to-be-analyzed pipeline is obtained, and the contrast pipeline is the same batch of the to-be-analyzed pipeline and is not used or not failed. According to the method, the comprehensiveness and accuracy of failure reason analysis of the glass fiber reinforced plastic pipeline are improved.

Description

Technical Field

[0001] This application relates to the field of oil and gas transportation, and in particular to an analysis method, equipment and medium for pipeline failure. Background Technology

[0002] To mitigate the corrosion of metal pipelines by the medium and reduce operating costs and environmental impact, oilfields have increased the investment and use of fiberglass reinforced plastic (FRP) pipelines. However, with the increase in service life and changes in operating conditions, FRP pipelines are prone to failure phenomena such as cracking and delamination.

[0003] Current pipeline failure analysis methods cannot comprehensively and accurately analyze the causes of FRP (fiberglass reinforced plastic) pipeline failures, thus hindering the use of these causes for failure mitigation. Chinese Patent CN115575450A discloses a method for evaluating the failure of surface protective materials due to particulate impact erosion. This method can quantitatively and accurately characterize the degree and rate of damage caused by particulate erosion, establishing failure criteria for the protective material during service. It reflects both the average phenomenon of the protective material and quantitatively describes the local microscopic changes in the coating. This method is primarily applicable to analyzing surface damage and cumulative failures, mainly focusing on physical loss analysis. Chinese Patent CN108693243B discloses a method for evaluating joint failure. This method can classify the morphology and distribution characteristics of metal loss on the outer wall of the pipeline at the joint, and establish a correspondence between metal loss types and joint failure response plans. The overall evaluation is highly operable, and scientific and reasonable response plans are formulated based on different metal losses. This method is mainly applicable to the failure analysis of metal pipelines, also focusing on the physical loss analysis of pipelines.

[0004] Therefore, there is a need for a pipeline failure analysis scheme that can improve the comprehensiveness and accuracy of the failure cause analysis of FRP pipelines. Summary of the Invention

[0005] This application provides a method, equipment, and medium for analyzing pipeline failures, which can improve the comprehensiveness and accuracy of the analysis of the causes of FRP pipeline failures.

[0006] In a first aspect, embodiments of this application provide a method for analyzing pipeline failure, including:

[0007] Obtain pipeline service condition information and pipeline images of the pipeline to be analyzed, wherein the pipeline images include the damaged areas of the pipeline to be analyzed;

[0008] Based on the pipeline service condition information and pipeline images, a first-level identification is performed to obtain the corresponding first-level identification result;

[0009] Based on the results of the primary identification, it is determined whether secondary identification is required for the pipeline to be analyzed. The secondary identification includes performance testing and analysis of the pipeline, and the analysis accuracy of the secondary identification is higher than that of the primary identification.

[0010] If necessary, a secondary identification is performed based on the pipeline to be analyzed and the control pipeline to obtain the failure analysis result of the pipeline to be analyzed. The control pipeline is a pipeline from the same batch as the pipeline to be analyzed that has not been used, or a pipeline that has not failed.

[0011] In one possible implementation, the step of performing primary identification based on the pipeline service condition information and pipeline image to obtain the corresponding primary identification result includes:

[0012] Based on the pipeline service condition information, generate image tags for the pipeline image;

[0013] The pipeline image with the image label is input into the failure analysis model to perform a first-level failure analysis and obtain the corresponding first-level identification result. The first-level identification result includes whether the pipeline to be analyzed has failed and the failure category when the pipeline fails.

[0014] In one possible implementation, the failure analysis model is obtained in the following way:

[0015] A training sample set is generated based on images of failed and unfailed pipelines. Both images of failed and unfailed pipelines are labeled with image tags and failure tags. The image tags are generated using pipeline service condition information corresponding to the images. The failure tags include whether the pipeline has failed and the failure category when the pipeline fails.

[0016] The initial neural network model is trained based on the training sample set to obtain the failure analysis model;

[0017] The pipeline service condition information includes one or more of the following: pipeline specifications and composition, process parameters, composition of the transported medium, external environment and geographical information of the pipeline, pipeline failure information, and third-party construction information; the failure categories include one or more of the following: improper operation, third-party damage, manufacturing and construction defects, natural disasters, and material aging.

[0018] In one possible implementation, determining whether secondary identification of the pipeline to be analyzed is required based on the primary identification result includes:

[0019] When the first-level identification result includes that the pipeline is not failed, it is determined that no second-level identification is needed for the pipeline to be analyzed, and the failure analysis result of the pipeline to be analyzed is determined to be that the pipeline is not failed.

[0020] When the primary identification result includes pipeline failure, determine whether the failure category of the pipeline failure includes manufacturing and construction defects and / or material aging.

[0021] If not, then it is determined that secondary identification of the pipeline to be analyzed is not required, and the failure analysis result of the pipeline to be analyzed is determined based on the primary identification result;

[0022] If so, then it is determined that secondary identification of the pipeline to be analyzed is required.

[0023] In one possible implementation, the step of performing secondary identification based on the pipeline to be analyzed and a control pipeline to obtain the failure analysis result of the pipeline to be analyzed includes:

[0024] Obtain the standard analysis results of the control pipeline. The standard analysis results include whether the control pipeline has manufacturing defects and the type of manufacturing failure caused by the manufacturing defects. The standard analysis results are obtained by comparing the first performance analysis results of the control pipeline with the standard information of the standard pipeline. The first performance analysis results are obtained by performing physical performance tests and microstructure analysis on the control pipeline.

[0025] Obtain the second performance analysis result of the control pipe. The second performance analysis result is obtained by performing performance analysis and simulation on the control pipe. The performance analysis and simulation include physicochemical performance analysis, thermal performance analysis, mechanical performance analysis and stress simulation.

[0026] A pipe sample of the pipe to be analyzed is collected, and the performance of the pipe sample is analyzed and simulated to obtain a third performance analysis result. The pipe sample includes a failure area, a non-failure area, and an intermediate area between the failure area and the non-failure area.

[0027] When the standard analysis results indicate that the control pipeline has no manufacturing defects, the failure analysis results of the pipeline to be analyzed are determined based on the second performance analysis results and the third performance analysis results.

[0028] When the standard analysis results indicate that the control pipeline has manufacturing defects, the failure analysis results of the pipeline to be analyzed are determined based on the standard analysis results, the second performance analysis results, and the third performance analysis results.

[0029] In one possible implementation, determining the failure analysis result of the pipeline to be analyzed based on the second performance analysis result and the third performance analysis result includes:

[0030] The third performance analysis result is compared with the second performance analysis result to obtain the aging stress analysis result of the pipeline to be analyzed. The aging stress analysis result includes whether the pipeline to be analyzed has aging stress failure, and the aging stress failure category when aging stress failure occurs. The aging stress failure category includes one or more of chemical aging, physical aging, and stress failure.

[0031] Based on the aging stress analysis results and the first-level identification results, the failure analysis results of the pipeline to be analyzed are determined.

[0032] In one possible implementation, determining the failure analysis result of the pipeline to be analyzed based on the standard analysis result, the second performance analysis result, and the third performance analysis result includes:

[0033] Based on the manufacturing failure category caused by manufacturing defects in the standard analysis results, the manufacturing analysis results of the pipeline to be analyzed are determined. The manufacturing analysis results include the presence of manufacturing defects in the pipeline to be analyzed and the corresponding manufacturing failure category. The manufacturing failure category includes one or more of the following: parameter specification defects, resin dripping, resin peeling, fiber breakage, fiber debonding, interlayer inclusions, and resin and fiber delamination.

[0034] The third performance analysis result is compared with the second performance analysis result to obtain the aging stress analysis result of the pipeline to be analyzed. The aging stress analysis result includes whether the pipeline to be analyzed has aging stress failure, and the aging stress failure category when aging stress failure occurs. The aging stress failure category includes one or more of chemical aging, physical aging, and stress failure.

[0035] Based on the manufacturing analysis results, the aging stress analysis results, and the primary identification results, the failure analysis results of the pipeline to be analyzed are determined.

[0036] Secondly, embodiments of this application provide a computing device, including:

[0037] The acquisition module is used to acquire pipeline service condition information and pipeline images of the pipeline to be analyzed, wherein the pipeline images include the damaged parts of the pipeline to be analyzed.

[0038] The processing module is used to perform primary identification based on the pipeline service condition information and pipeline images to obtain the corresponding primary identification result; determine whether secondary identification is needed for the pipeline to be analyzed based on the primary identification result, the secondary identification including pipeline performance testing and analysis, the analysis accuracy of the secondary identification being higher than that of the primary identification; if needed, perform secondary identification based on the pipeline to be analyzed and a control pipeline to obtain the failure analysis result of the pipeline to be analyzed, the control pipeline being a pipeline from the same batch as the pipeline to be analyzed that has not been used, or a pipeline that has not failed.

[0039] In one possible implementation, the processing module is further configured to:

[0040] Based on the pipeline service condition information, generate image tags for the pipeline image;

[0041] The pipeline image with the image label is input into the failure analysis model to perform a first-level failure analysis and obtain the corresponding first-level identification result. The first-level identification result includes whether the pipeline to be analyzed has failed and the failure category when the pipeline fails.

[0042] In one possible implementation, the processing module is further configured to:

[0043] A training sample set is generated based on images of failed and unfailed pipelines. Both images of failed and unfailed pipelines are labeled with image tags and failure tags. The image tags are generated using pipeline service condition information corresponding to the images. The failure tags include whether the pipeline has failed and the failure category when the pipeline fails.

[0044] The initial neural network model is trained based on the training sample set to obtain the failure analysis model;

[0045] The pipeline service condition information includes one or more of the following: pipeline specifications and composition, process parameters, composition of the transported medium, external environment and geographical information of the pipeline, pipeline failure information, and third-party construction information; the failure categories include one or more of the following: improper operation, third-party damage, manufacturing and construction defects, natural disasters, and material aging.

[0046] In one possible implementation, the processing module is further configured to:

[0047] When the first-level identification result includes that the pipeline is not failed, it is determined that no second-level identification is needed for the pipeline to be analyzed, and the failure analysis result of the pipeline to be analyzed is determined to be that the pipeline is not failed.

[0048] When the primary identification result includes pipeline failure, determine whether the failure category of the pipeline failure includes manufacturing and construction defects and / or material aging.

[0049] If not, then it is determined that secondary identification of the pipeline to be analyzed is not required, and the failure analysis result of the pipeline to be analyzed is determined based on the primary identification result;

[0050] If so, then it is determined that secondary identification of the pipeline to be analyzed is required.

[0051] In one possible implementation, the processing module is further configured to:

[0052] Obtain the standard analysis results of the control pipeline. The standard analysis results include the presence of manufacturing defects in the control pipeline and the manufacturing failure categories caused by the manufacturing defects. The standard analysis results are obtained by comparing the first performance analysis results of the control pipeline with the standard information of the standard pipeline. The first performance analysis results are obtained by performing physical performance tests and microstructure analysis on the control pipeline.

[0053] Obtain the second performance analysis result of the control pipe. The second performance analysis result is obtained by performing performance analysis and simulation on the control pipe. The performance analysis and simulation include physicochemical performance analysis, thermal performance analysis, mechanical performance analysis and stress simulation.

[0054] A pipe sample of the pipe to be analyzed is collected, and the performance of the pipe sample is analyzed and simulated to obtain a third performance analysis result. The pipe sample includes a failure area, a non-failure area, and an intermediate area between the failure area and the non-failure area.

[0055] When the standard analysis results indicate that the control pipeline has no manufacturing defects, the failure analysis results of the pipeline to be analyzed are determined based on the second performance analysis results and the third performance analysis results.

[0056] When the standard analysis results indicate that the control pipeline has manufacturing defects, the failure analysis results of the pipeline to be analyzed are determined based on the standard analysis results, the second performance analysis results, and the third performance analysis results.

[0057] In one possible implementation, the processing module is further configured to:

[0058] The third performance analysis result is compared with the second performance analysis result to obtain the aging stress analysis result of the pipeline to be analyzed. The aging stress analysis result includes whether the pipeline to be analyzed has aging stress failure, and the aging stress failure category when aging stress failure occurs. The aging stress failure category includes one or more of chemical aging, physical aging, and stress failure.

[0059] Based on the aging stress analysis results and the first-level identification results, the failure analysis results of the pipeline to be analyzed are determined.

[0060] In one possible implementation, the processing module is further configured to:

[0061] Based on the manufacturing failure category caused by manufacturing defects in the standard analysis results, the manufacturing analysis results of the pipeline to be analyzed are determined. The manufacturing analysis results include the presence of manufacturing defects in the pipeline to be analyzed and the corresponding manufacturing failure category. The manufacturing failure category includes one or more of the following: parameter specification defects, resin dripping, resin peeling, fiber breakage, fiber debonding, interlayer inclusions, and resin and fiber delamination.

[0062] The third performance analysis result is compared with the second performance analysis result to obtain the aging stress analysis result of the pipeline to be analyzed. The aging stress analysis result includes whether the pipeline to be analyzed has aging stress failure, and the aging stress failure category when aging stress failure occurs. The aging stress failure category includes one or more of chemical aging, physical aging, and stress failure.

[0063] Based on the manufacturing analysis results, the aging stress analysis results, and the primary identification results, the failure analysis results of the pipeline to be analyzed are determined.

[0064] Thirdly, embodiments of this application provide another computing device, including:

[0065] The processor, and the memory that is in communication with the processor;

[0066] Memory is used to store instructions that the computer executes;

[0067] The processor is used to execute computer execution instructions stored in memory to implement the pipeline failure analysis method of the first aspect.

[0068] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the pipeline failure analysis method of the first aspect.

[0069] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, is used to implement the pipeline failure analysis method of the first aspect.

[0070] This application provides a method, device, and medium for analyzing pipeline failure. It first acquires pipeline service condition information and pipeline images, providing a comprehensive data foundation for subsequent failure analysis and ensuring data completeness. After obtaining the corresponding first-level identification results based on the pipeline service condition information and pipeline images, it can determine whether a more precise second-level identification is needed, thereby improving the accuracy of the pipeline failure cause analysis. Furthermore, if second-level identification is required, it can be performed on the pipeline under analysis and control pipelines from the same batch that are unused or not failed. This blank control analysis method improves the comprehensiveness and accuracy of the pipeline failure cause analysis.

[0071] Furthermore, during the secondary identification phase, various physicochemical, thermodynamic, and other performance tests and analyses can be performed on both the pipeline to be analyzed and the control pipeline, going beyond just physical loss analysis. This further improves the comprehensiveness and accuracy of the failure cause analysis of the pipeline to be analyzed. This setup provides a method suitable for analyzing the failure causes of FRP (fiberglass reinforced plastic) pipelines. Utilizing two-level identification, in-depth analysis of real pipeline data and laboratory test data can be conducted, thereby comprehensively and accurately determining the root cause of the failure of the pipeline to be analyzed. This can guide the implementation of FRP pipeline failure mitigation strategies, effectively reduce the degree of FRP pipeline failure, and provide technical support for the protection of FRP pipelines in oil and gas fields, reducing pipeline failure rates, and ensuring the normal transportation of oil and gas. Attached Figure Description

[0072] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0073] Figure 1 This is a flowchart of a pipeline failure analysis method according to an embodiment of this application;

[0074] Figure 2 This is a schematic diagram illustrating the analysis process of pipeline failure according to an embodiment of this application;

[0075] Figure 3 This is a schematic diagram of the structure of a computing device according to an embodiment of this application;

[0076] Figure 4 This is a schematic diagram of the structure of a computing device according to another embodiment of this application.

[0077] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0078] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0079] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0080] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0081] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0082] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0083] The pipeline failure analysis method, equipment, and medium of this application can be used in the field of oil and gas transportation, as well as in any other field besides oil and gas transportation, such as data processing. The application field of the pipeline failure analysis method, equipment, and medium of this application is not limited.

[0084] The pipeline failure analysis method, equipment, and medium of this application can be applied to the failure cause analysis of FRP pipelines for oil and gas transportation, as well as to the failure cause analysis of pipelines made of other materials for oil and gas transportation. The pipeline failure analysis method, equipment, and medium of this application can be applied to any relevant scenario involving pipeline failure cause analysis.

[0085] First, let me explain the terms used in this application:

[0086] Damage refers to acceptable damage to a pipeline that allows it to continue operating normally. A damaged pipeline will not experience significant performance changes and can still transport oil and gas normally.

[0087] Failure refers to unacceptable damage to a pipeline that renders it unusable. It is also called defect or damage. Failed pipelines will experience a certain degree of performance degradation and will not meet the current service conditions, thus making it impossible to transport oil and gas normally. Failed pipelines need to be replaced in a timely manner.

[0088] For a long time, the traditional oil and gas transportation industry has mainly relied on the use of metal pipelines (including cast iron pipes, carbon steel pipes, and stainless steel pipes). However, with the continuous deepening of exploitation, in order to improve the recovery rate, multiple displacement methods such as water flooding, carbon dioxide flooding, deoxygenated air flooding, deoxygenated air foam flooding, and polymer flooding have been applied to oil and gas fields to achieve the goal of stabilizing or increasing production. However, this also makes the produced media in oil and gas fields more complex, such as increased concentrations of dissolved oxygen, carbon dioxide, hydrogen sulfide, bacteria, chloride ions, and scaling ions, which poses a great challenge to the safe transportation of traditional metal pipelines.

[0089] To mitigate the corrosion of metal pipelines by various media and reduce operating costs and environmental impact, oilfields have increased their investment in and use of fiberglass reinforced plastic (FRP) pipelines. FRP pipelines possess characteristics such as corrosion and wear resistance, low friction and water resistance, delayed scaling, ease of installation and maintenance, and long service life, making them an important development direction for corrosion protection of oilfield surface gathering and transportation pipelines. However, with the increase in service life and changes in operating conditions, FRP pipelines are prone to failure phenomena such as cracking and delamination. Failed FRP pipelines seriously affect the production efficiency of oil and gas fields and the normal supply of energy.

[0090] Therefore, it is necessary to conduct timely failure analysis on FRP (fiberglass reinforced plastic) pipelines to determine whether they have failed and the root cause of the failure. This allows for timely replacement of failed FRP pipelines, ensuring the normal transportation of oil and gas, and enabling the use of the failure causes to mitigate pipeline failures and reduce the failure rate. However, current pipeline failure analysis methods cannot comprehensively and accurately analyze the causes of FRP pipeline failures.

[0091] For example, Chinese patent CN115575450A discloses a method for evaluating the failure of surface protective materials due to particulate impact erosion. This method determines the damage location based on the damage morphology, measures the depth of surface erosion damage, and uses Origin software to fit a damage depth variation equation. The depth curve is then integrated to obtain the test area cross-section. The cross-sectional area of ​​the test area is multiplied by the test width for each measurement to obtain the volume loss for each measurement. Based on the differential principle, the volume loss measured for each measurement in the damaged area is summed to obtain the total volume loss of the damaged area. A three-dimensional morphology of the damage is plotted, and the degree of damage and erosion rate of the protective material are quantitatively evaluated. Failure criteria are proposed based on the degree of damage, and the material's erosion resistance is evaluated based on the erosion rate. This method can quantitatively and accurately characterize the degree and rate of damage to surface protective materials caused by particulate erosion, establish failure criteria for protective materials during service, reflect both the average phenomena of the protective material and quantitatively describe the local microscopic changes in the coating. This method is mainly applicable to analyzing failures caused by surface damage that accumulates over time, and primarily involves physical losses. It is not applicable to damage within the object or between microstructures, or performance degradation or failure caused by a certain degree of chemical reaction.

[0092] Chinese patent CN108693243B discloses a method for evaluating the failure of pipe joint repairs. This method can classify the morphology and distribution characteristics of metal loss on the outer wall of the pipe at the repair site, and establish a correspondence between metal loss types and repair failure response plans. The overall evaluation is highly operable, and scientific and reasonable response plans can be formulated based on different types of metal loss. This failure evaluation method involves conducting magnetic flux leakage (MF) internal detection on the pipe to obtain a grayscale image of the MF internal detection on the outer wall of the pipe covered by the repair strip. It determines whether there is sheet-like metal loss at the bottom of the pipe or at the intersection of the pipe weld and the circumferential weld, metal loss caused by mechanical scratches, the presence of wrinkled corrosion lines, strip-like metal loss distributed circumferentially, or point-like metal loss, etc., and formulates scientific and reasonable response plans based on different types of metal loss. This method can classify the morphology and distribution characteristics of metal loss on the outer wall of the pipe at the repair site, and establish a correspondence between metal loss types and repair failure response plans. The overall evaluation is highly operable, and scientific and reasonable response plans can be formulated based on different types of metal loss. The overall approach is highly adaptable to joint failures and distinguishes different response priorities, facilitating the arrangement and planning of subsequent repair work and providing scientific guidance for pipeline maintenance. This method is primarily applicable to failure analysis of metal pipelines and also to the analysis of physical losses in pipelines.

[0093] Therefore, there is a need for a pipeline failure analysis method that can improve the comprehensiveness and accuracy of the failure cause analysis of FRP pipes.

[0094] Based on the above-mentioned technical problems, this application provides a method, equipment and medium for analyzing pipeline failure, which can be applied to the analysis of the causes of failure of FRP pipelines. By utilizing surface analysis in the first-level identification and performance testing and analysis in the second-level identification with higher precision, in-depth analysis can be carried out on real pipeline data and laboratory test analysis data, so as to comprehensively and accurately determine the root cause of the failure of the pipeline to be analyzed.

[0095] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0096] Figure 1 This is a flowchart illustrating a pipeline failure analysis method according to an embodiment of this application. This embodiment describes the pipeline failure analysis method using a computing device as the executing entity. Figure 1 As shown, the analysis method for this pipeline failure may include the following steps:

[0097] S101: Obtain pipeline service condition information and pipeline images of the pipeline to be analyzed. The pipeline images include the damaged areas of the pipeline to be analyzed.

[0098] In this embodiment, the computing device can be a terminal device such as a computer, a server-side device such as a server, or a terminal-server integrated device; no restrictions are imposed here.

[0099] In this embodiment, the pipeline to be analyzed can be a pipeline in service in an oil and gas field. The pipeline failure analysis method of this embodiment is used to analyze whether the pipeline has failed and to determine the cause of failure when it fails. The pipeline to be analyzed can also be a pipeline that has failed. The pipeline failure analysis method of this embodiment is used to determine the root cause of failure.

[0100] In this embodiment, the pipeline service condition information can be obtained by personnel through on-site inspection, testing, and collection of the pipeline. It may include one or more of the following: pipeline specifications and composition, process parameters, composition of the transported medium, external environmental and geographical information, pipeline failure information, and third-party construction information. Of course, the pipeline service condition information is not limited to the above-mentioned information; those skilled in the art can flexibly set it according to actual needs, and no restrictions are imposed here. For example, the pipeline service condition information may also include interference information caused by special environments such as high-voltage transmission lines, DC grounding electrodes, and mining areas.

[0101] The pipeline specifications and composition can include information such as the pipeline matrix composition, reinforcing material composition, curing agent composition, curing process, molding process, manufacturer, pipeline inner diameter, and pipeline length. Process parameters can include various parameters such as design pressure and temperature, design pressure, and actual operating pressure. The composition of the transported medium can include information such as the composition of the transported medium, medium temperature, medium internal pressure, and medium flow rate. The external environment and geographical information of the pipeline can include pipeline location information, burial depth, geological environment, and hydrological records. Pipeline failure information can include information on the composition of scale buildup on the pipe wall, corrosion products on the inner and outer walls, failure location information, damage information at the failure point, and morphological information at the failure point. Failure locations include the inner wall of the pipeline, the outer wall of the pipeline, pipeline tees, pipeline bends, and joints / diameter changes. Third-party construction information can include information such as the construction unit, construction methods, construction equipment, and construction conditions and parameters.

[0102] In addition, in order to improve the comprehensiveness of pipeline service condition information and provide a comprehensive data analysis basis for subsequent failure analysis of the pipeline to be analyzed, those skilled in the art can also test or collect information such as carbon dioxide partial pressure, sulfate ion content, liquid phase flow rate, sulfate-reducing bacteria content, pH value and dissolved oxygen content in the liquid phase inside the pipeline on site, and test information such as soil resistivity, DC potential, AC voltage, AC current density and soil DC potential gradient at the pipeline failure location on site, and use the above information as part of the pipeline service condition information.

[0103] In this embodiment, the pipeline image may include the damaged parts of the pipeline to be analyzed, so as to facilitate subsequent determination of whether the pipeline to be analyzed has failed based on the pipeline image, and to identify the cause of failure that can be identified on the surface, such as third-party damage.

[0104] S102: Perform primary identification based on pipeline service condition information and pipeline images to obtain the corresponding primary identification results.

[0105] In this embodiment, the first-level identification can be a preliminary failure analysis based on the surface information of the pipeline to be analyzed, to determine whether a failure has occurred that can be determined based on the surface information. However, this method has low accuracy, and can identify failures such as improper operation, third-party damage, or natural disasters.

[0106] In this embodiment, image recognition and machine learning technologies can be used to perform primary identification based on pipeline service condition information and pipeline images. Alternatively, since primary identification is a preliminary failure analysis of pipeline surface information, and to save on the cost of primary identification, it can also be performed manually based on pipeline service condition information and pipeline images.

[0107] It should be noted that the pipe surface information here refers to information obtained solely through observation (machine identification or manual identification) of damaged parts of the pipe without conducting laboratory-related tests, analyses, or simulations.

[0108] S103: Determine whether secondary identification is needed for the pipeline to be analyzed based on the results of primary identification. Secondary identification includes pipeline performance testing and analysis. The analysis accuracy of secondary identification is higher than that of primary identification.

[0109] In this embodiment, secondary identification can involve conducting specific performance tests and analyses, such as physicochemical and thermodynamic tests, on the pipeline to be analyzed. This allows for a more precise and in-depth analysis, further determining the cause of failure based on the results of primary identification. Secondary identification is a progressive analysis with even higher precision built upon primary identification.

[0110] S104: If necessary, perform secondary identification based on the pipeline to be analyzed and the control pipeline to obtain the failure analysis results of the pipeline to be analyzed. The control pipeline is a pipeline from the same batch as the pipeline to be analyzed that has not been used, or a pipeline that has not failed.

[0111] In this embodiment, when there are unused pipes from the same batch that are to be analyzed, the unused pipes from the same batch can be used as control pipes; when there are no unused pipes from the same batch that are to be analyzed, the non-failed part of the pipes to be analyzed (the non-failed pipes) can be used as control pipes, that is, the non-failed pipes can be the non-failed part of the pipes to be analyzed.

[0112] S104: If necessary, determine the failure analysis results of the pipeline to be analyzed based on the first-level identification results.

[0113] In this embodiment, the service condition information and images of the pipeline to be analyzed can be obtained first, providing a comprehensive data analysis foundation for subsequent failure analysis and ensuring data comprehensiveness. After obtaining the corresponding first-level identification results based on the pipeline service condition information and pipeline images, it can be determined whether a more precise second-level identification is needed, thereby improving the accuracy of the failure cause analysis. Furthermore, if a second-level identification is required, it can be performed on the pipeline to be analyzed and a control pipeline from the same batch that is unused or not failed, using a blank control analysis method to improve the comprehensiveness and accuracy of the failure cause analysis. Furthermore, during the second-level identification, various physicochemical, thermodynamic, and other performance tests and analyses can be performed on the pipeline to be analyzed and the control pipeline, not just limited to physical loss analysis, further improving the comprehensiveness and accuracy of the failure cause analysis. This approach provides a method for analyzing the causes of FRP (fiberglass reinforced plastic) pipeline failures. By utilizing two-level identification, it enables in-depth analysis of real pipeline data and laboratory test data, thereby comprehensively and accurately determining the root cause of the pipeline failure. This guides the implementation of FRP pipeline failure mitigation strategies, effectively reducing the degree of failure and providing technical support for the protection of FRP pipelines in oil and gas fields, reducing pipeline failure rates, and ensuring the normal transportation of oil and gas.

[0114] In one possible implementation, step S102 above, which performs primary identification based on pipeline service condition information and pipeline images to obtain the corresponding primary identification result, may include:

[0115] S11: Generate image labels for pipeline images based on pipeline service condition information.

[0116] S12: Input the pipeline image with image label into the failure analysis model to perform first-level failure analysis and obtain the corresponding first-level identification results. The first-level identification results include whether the pipeline to be analyzed has failed and the failure category when the pipeline fails.

[0117] The pipeline service condition information includes one or more of the following: pipeline specifications and composition, process parameters, composition of the transported medium, pipeline external environment and geographical information, pipeline failure information, and third-party construction information; failure categories include one or more of the following: improper operation, third-party damage, manufacturing and construction defects, natural disasters, and material aging.

[0118] In this embodiment, the pipeline service condition information is not limited to the information described above. Those skilled in the art can flexibly set it according to actual needs, and no restrictions are imposed here. Similarly, the failure categories are not limited to the information described above. Those skilled in the art can flexibly set it according to actual needs, and no restrictions are imposed here.

[0119] In this embodiment, improper operation can include operational problems during pipeline operation, which can be identified using pipeline operating parameters (found in pipeline service condition information). For example, improper operation can be determined based on operating parameters such as the actual valve opening. Third-party damage can be human-caused damage to the pipeline, such as damage caused by a sharp object. Manufacturing and construction defects can be defects present during pipeline manufacturing and construction, such as pipelines not meeting manufacturing standards. Natural disasters can be damage to the pipeline caused by extreme weather or other disasters, such as mudslides. Material aging can be the aging of the pipeline during service due to increased service time, corrosion from the transported medium, or the surrounding environment.

[0120] In this embodiment, when the failure analysis model identifies that the damaged part of the pipeline to be analyzed does not belong to the above-mentioned failure categories, the pipeline to be analyzed can be considered not to have failed, and the damage is acceptable. When the failure analysis model identifies that the damaged part of the pipeline to be analyzed belongs to the above-mentioned failure categories, the pipeline to be analyzed can be considered to have failed, and the identified failure category is the failure category of the pipeline to be analyzed.

[0121] In this embodiment, in contrast to the failure category, the failure analysis model can perform damage or defect analysis, third-party damage analysis, natural disaster analysis, and operation analysis on pipeline images; when performing damage or defect analysis, it can identify whether the pipeline has manufacturing and construction defects and material aging.

[0122] Alternatively, since Level 1 identification involves preliminary failure analysis of the pipeline surface, to save costs, it can also be performed using manual visual inspection based on pipeline service condition information and pipeline images. Manual visual inspection can involve acquiring pipeline images or directly observing the pipeline. During manual visual inspection, damage or defect analysis, third-party damage analysis, natural disaster analysis, and operational analysis can also be performed on the pipeline images (or actual pipelines).

[0123] In this embodiment, after generating image tags for pipeline images based on pipeline service condition information, the pipeline images with image tags are input into the failure analysis model. By using image recognition technology and machine learning technology, the pipeline to be analyzed can be accurately subjected to first-level failure analysis, ensuring the accuracy of the first-level identification results.

[0124] In one possible implementation, the failure analysis model in step S12 above can be obtained in the following way:

[0125] S21: Generate a training sample set based on the images of failed and unfailed pipelines. Both images of failed and unfailed pipelines are labeled with image labels and failure labels. The image labels are generated using the pipeline service condition information corresponding to the images. The failure labels include whether the pipeline has failed and the failure category when the pipeline fails.

[0126] S22: Train the initial neural network model based on the training sample set to obtain the failure analysis model.

[0127] In this embodiment, the images of unfailed pipelines may include images corresponding to each failure category, thereby improving the performance of the trained failure analysis model.

[0128] In this embodiment, after generating a training sample set based on labeled images of failed and unfailed pipelines, the initial neural network model is trained using this training sample set. This enables the final failure analysis model to learn to identify whether a pipeline has failed and to identify the specific failure category when a pipeline fails, thereby improving the performance of the failure analysis model.

[0129] In one possible implementation, step S103 above, which determines whether a secondary identification of the pipeline to be analyzed is required based on the primary identification result, may include:

[0130] S31: When the first-level identification result includes that the pipeline is not failed, it is determined that there is no need to perform a second-level identification on the pipeline to be analyzed, and the failure analysis result of the pipeline to be analyzed is determined to be that the pipeline is not failed.

[0131] S32: When the first-level identification result includes pipeline failure, determine whether the failure category of the pipeline failure includes manufacturing and construction defects and / or material aging.

[0132] S33: If not, then it is determined that secondary identification of the pipeline to be analyzed is not required, and the failure analysis result of the pipeline to be analyzed is determined based on the primary identification result;

[0133] S34: If so, then it is determined that secondary identification of the pipeline to be analyzed is required.

[0134] In this embodiment, if the failure category of the pipeline does not include either manufacturing and construction defects or material aging, then secondary identification of the pipeline to be analyzed is not required; if the failure category of the pipeline includes either manufacturing and construction defects or material aging, then secondary identification of the pipeline to be analyzed is required to further determine the cause of pipeline failure.

[0135] In this embodiment, after obtaining the primary identification result, it is necessary to determine whether secondary identification of the pipeline is required based on whether the pipeline has failed and the corresponding failure category. This aims to minimize the cost and improve the efficiency of failure analysis while ensuring the comprehensiveness and accuracy of the pipeline failure cause analysis. Secondary identification is not required if the pipeline is not failed or the failure category does not include manufacturing and construction defects and / or material aging. Secondary identification is only necessary when the primary identification result indicates pipeline failure, and the failure category includes manufacturing and construction defects and / or material aging. The higher precision of secondary identification allows for a more detailed and accurate analysis of the pipeline failure cause.

[0136] In one possible implementation, step S104 above, which performs secondary identification based on the pipeline to be analyzed and the control pipeline to obtain the failure analysis results of the pipeline to be analyzed, may include:

[0137] S41: Obtain the standard analysis results of the control pipeline. The standard analysis results may include whether the control pipeline has manufacturing defects and the type of manufacturing failure caused by the manufacturing defects.

[0138] In this embodiment, the standard analysis result can be obtained by comparing the first performance analysis result of the control pipeline with the standard information of the standard pipeline. The first performance analysis result can be obtained by conducting physical performance tests and microstructure analysis on the control pipeline.

[0139] S42: Obtain the second performance analysis results of the control pipeline. The second performance analysis results are obtained by performing performance analysis and simulation on the control pipeline.

[0140] In this embodiment, performance analysis and simulation may include physicochemical performance analysis, thermal performance analysis, mechanical performance analysis, and stress simulation.

[0141] S43: Collect pipe samples of the pipe to be analyzed, and perform performance analysis and simulation on the pipe samples to obtain the third performance analysis results.

[0142] In this embodiment, the pipeline sample may include a failed area, a non-failed area, and an intermediate area between the failed area and the non-failed area.

[0143] S44: When the standard analysis results include the absence of manufacturing defects in the control pipeline, the failure analysis results of the pipeline to be analyzed are determined based on the second and third performance analysis results.

[0144] S45: When the standard analysis results include manufacturing defects in the control pipeline, the failure analysis results of the pipeline to be analyzed shall be determined based on the standard analysis results, the second performance analysis results, and the third performance analysis results.

[0145] In this embodiment, after each batch of pipes leaves the factory, one pipe from each batch is randomly selected as a control pipe. The control pipe undergoes physical performance testing and microstructure analysis to obtain a first performance analysis result. This first performance analysis result is then compared with the standard information of the standard pipe to obtain a standard analysis result. The standard analysis results of each batch of control pipes are then stored. When secondary identification of the pipe to be analyzed is required, the standard analysis result of the control pipe from the same batch as the pipe to be analyzed can be directly obtained from the stored standard analysis results.

[0146] It should be noted that, in order to ensure the normal operation of the secondary recognition, non-destructive testing can be performed first and destructive testing can be performed later in each test and analysis process of the secondary recognition.

[0147] In this embodiment, since the specifications and composition of any part of the control pipe should be consistent in principle, any area of ​​the control pipe can be randomly selected as a sample for testing and analysis when conducting physical performance tests, microstructure analysis, performance analysis and simulation on the control pipe.

[0148] In this embodiment, physical performance testing and microstructure analysis can be used to determine whether the pipeline has manufacturing and construction defects, while performance analysis and simulation can be used to determine whether the pipeline has experienced material aging and stress failure. Specifically, physicochemical performance analysis can be used to determine whether the pipeline has experienced chemical aging, thermal and mechanical performance analysis can be used to determine whether the pipeline has experienced physical aging, and stress simulation can be used to determine whether the pipeline has experienced stress failure.

[0149] In this embodiment, physical performance testing may include appearance inspection and defect assessment, resin content testing, and curing degree testing. Appearance inspection and defect assessment mainly tests whether the pipe's inner diameter and length meet the corresponding standards in the Non-metallic Pipe Quality Acceptance Standard SY / T 6770.1-2023. Resin content testing mainly tests whether the resin content in the pipe meets the corresponding standards in the Non-metallic Pipe Quality Acceptance Standard SY / T 6770.1-2023. Curing degree testing mainly tests whether the curing degree in the pipe meets the corresponding standards in the Non-metallic Pipe Quality Acceptance Standard SY / T 6770.1-2023.

[0150] In this embodiment, the microstructure analysis may include surface morphology analysis and interlayer fiber structure analysis. Surface morphology analysis mainly analyzes whether there are phenomena such as resin fragmentation, fiber layer tearing, structural layer delamination, transverse cracks, and bubbles in the GFRP (fiberglass reinforced plastic) layer morphology of the pipeline. Interlayer fiber structure analysis mainly analyzes whether there are phenomena such as interlayer cracks, bubbles, fiber breakage, resin shedding, and fiber detachment in the interlayer fiber structure of the pipeline.

[0151] In this embodiment, the physicochemical performance analysis may include chemical composition analysis and functional group analysis. Chemical composition analysis mainly analyzes whether the chemical composition of the pipe, such as -OH groups, has changed significantly. Functional group analysis mainly analyzes whether the functional groups of the pipe, such as peracid and perester functional groups, have changed significantly.

[0152] In this embodiment, thermal performance analysis may include glass transition temperature analysis, thermal decomposition temperature analysis, and dynamic thermomechanical performance analysis. Glass transition temperature analysis mainly analyzes whether the glass transition temperature of the pipeline meets the standard specified range. Thermal decomposition temperature analysis mainly analyzes whether the thermal decomposition temperature of the pipeline meets the standard specified range. Dynamic thermomechanical performance (DMA) analysis mainly analyzes the relationship between dynamic modulus and mechanical loss under vibration load and temperature to determine the stability of the pipeline under high temperature dynamic load.

[0153] In this embodiment, the mechanical property analysis may include nanoindentation analysis, tensile property analysis, Barcol hardness analysis, and microhardness analysis. Nanoindentation analysis mainly analyzes whether the pipe meets the nanohardness index, tensile property analysis mainly analyzes whether the pipe meets the circumferential tensile strength index, Barcol hardness analysis mainly analyzes whether the pipe meets the Barcol hardness index, and microhardness analysis mainly analyzes whether the pipe meets the microhardness index.

[0154] In this embodiment, stress simulation can be used to calculate and evaluate the magnitude of stress concentration at the pipe joint location, analyze the impact of stress concentration effect on the overall performance of the pipe, and use numerical simulation with fracture mechanics methods to analyze the stress distribution at the pipe joint location and determine whether the pipe has experienced stress failure.

[0155] In this embodiment, when collecting pipe samples of the pipe to be analyzed, the gradient change of the performance of the failed pipe section can be considered from the axial direction and the direction perpendicular to the pipe wall. That is, the sampling can be divided into three regions along the axial direction: the failed region, the unfailed region, and the intermediate region between the failed region and the unfailed region. Furthermore, the three layers of the outer surface, the intermediate layer, and the inner wall can be distinguished along the depth direction of the cross section.

[0156] In this embodiment, since the control pipe is a pipe from the same batch as the pipe to be analyzed that has not been used or has not failed, the standard analysis result of the control pipe should theoretically also be the standard analysis result of the pipe to be analyzed. When the control pipe does not meet the standard and has manufacturing defects, it indicates that the pipe to be analyzed also does not meet the standard and has manufacturing defects.

[0157] In this embodiment, if the standard analysis results include the absence of manufacturing defects in the control pipe, it indicates that the control pipe (the pipe to be analyzed) also lacks manufacturing defects. Only the second and third performance analysis results are needed to determine whether the pipe to be analyzed has experienced aging stress failure, and the specific type of such failure. If the standard analysis results include the presence of manufacturing defects in the control pipe, it indicates that the control pipe (the pipe to be analyzed) also has manufacturing defects. In this case, it is necessary not only to determine the failure type caused by the manufacturing defects based on the standard analysis results, but also to determine whether the pipe to be analyzed has experienced aging stress failure, and the specific type of such failure, based on the second and third performance analysis results.

[0158] In this embodiment, a secondary identification process can be performed using the pipeline to be analyzed and a control pipeline from the same batch that is unused or not failed. This blank control analysis method improves the comprehensiveness and accuracy of the failure cause analysis of the pipeline to be analyzed. Specifically, firstly, based on the standard analysis results of the physical performance tests and microstructure of the control pipeline, it can be determined whether the control pipeline has manufacturing defects. Then, by comparing the performance analysis results of the pipeline to be analyzed and the control pipeline with the simulated performance analysis results, it can be determined whether the pipeline to be analyzed has experienced aging stress failure. Through this setup, various physicochemical, thermodynamic, and other performance tests and analyses can be performed on the pipeline to be analyzed and the control pipeline, going beyond just physical loss analysis, further improving the comprehensiveness and accuracy of the failure cause analysis of the pipeline to be analyzed.

[0159] In one possible implementation, determining the failure analysis result of the pipeline to be analyzed based on the second performance analysis result and the third performance analysis result in step S44 above may include:

[0160] S51: Compare the results of the third performance analysis with the results of the second performance analysis to obtain the aging stress analysis results of the pipeline to be analyzed.

[0161] In this embodiment, the aging stress analysis results may include whether the pipeline under analysis has aging stress failure, and the aging stress failure category when aging stress failure occurs. The aging stress failure category may include one or more of chemical aging, physical aging, and stress failure.

[0162] S52: Based on the aging stress analysis results and the first-level identification results, determine the failure analysis results of the pipeline to be analyzed.

[0163] In this embodiment, the aging stress failure category when aging stress fails can be a subcategory under the material aging failure category in the primary identification results.

[0164] In this embodiment, since the control pipe is known to have no manufacturing defects and all specifications and parameters conform to standards based on the standard analysis results, comparing the third performance analysis results of the pipe to be analyzed with the second performance analysis results of the control pipe allows for a simple and accurate determination of whether the pipe to be analyzed has aging stress failure, and the type of aging stress failure at that time. Because the first-level identification results may include failure types other than manufacturing and construction defects and material aging, the failure analysis results of the pipe to be analyzed can be comprehensively and accurately determined based on the aging stress analysis results and the first-level identification results.

[0165] In one possible implementation, determining the failure analysis result of the pipeline to be analyzed based on the standard analysis result, the second performance analysis result, and the third performance analysis result in step S45 above may include:

[0166] S61: Determine the manufacturing analysis results of the pipeline to be analyzed based on the manufacturing failure category caused by manufacturing defects in the standard analysis results.

[0167] In this embodiment, the manufacturing analysis results may include manufacturing defects in the pipe to be analyzed, as well as the corresponding manufacturing failure categories. The manufacturing failure categories may include one or more of the following: parameter specification defects, resin dripping, resin peeling, fiber breakage, fiber debonding, interlayer inclusions, and resin-fiber delamination.

[0168] S62: Compare the results of the third performance analysis with the results of the second performance analysis to obtain the aging stress analysis results of the pipeline to be analyzed.

[0169] In this embodiment, the aging stress analysis results may include whether the pipeline under analysis has aging stress failure, and the aging stress failure category when aging stress failure occurs. The aging stress failure category may include one or more of chemical aging, physical aging, and stress failure.

[0170] S63: Based on the manufacturing analysis results, aging stress analysis results, and primary identification results, determine the failure analysis results of the pipeline to be analyzed.

[0171] In this embodiment, since the control pipe is a pipe from the same batch as the pipe to be analyzed that has not been used or has not failed, the standard analysis results of the control pipe should theoretically also be the standard analysis results of the pipe to be analyzed. When the control pipe does not meet the standard and has manufacturing defects, it indicates that the pipe to be analyzed also does not meet the standard and has manufacturing defects. The manufacturing failure category caused by the manufacturing defects of the control pipe in the standard analysis results should also be the manufacturing failure category caused by the manufacturing defects of the pipe to be analyzed.

[0172] In this embodiment, the manufacturing failure category caused by manufacturing defects can be a subcategory under the failure category of manufacturing and construction defects in the primary identification results; the aging stress failure category when aging stress fails can be a subcategory under the failure category of material aging in the primary identification results.

[0173] In this embodiment, since the control pipe is known to have manufacturing defects based on the standard analysis results, the manufacturing analysis results (manufacturing failure category caused by manufacturing defects) of the pipe to be analyzed can be determined based on the standard analysis results. Furthermore, by comparing the third performance analysis results of the pipe to be analyzed with the second performance analysis results of the control pipe, it is possible to easily and accurately determine whether the pipe to be analyzed has aging stress failure, and the aging stress failure category when it occurs. Since the primary identification results may include failure categories other than manufacturing and construction defects and material aging, the failure analysis results of the pipe to be analyzed can be comprehensively and accurately determined based on the manufacturing analysis results, aging stress analysis results, and primary identification results.

[0174] The following is a specific embodiment illustrating the pipeline failure analysis method of this application.

[0175] In one specific embodiment, a staff member conducted a failure analysis on medium-pressure water injection branch pipeline A, which was put into operation in the work area in September 2006. Figure 2 This is a schematic diagram of the pipeline failure analysis process according to an embodiment of this application. The specific process is as follows:

[0176] The first step involves using computing equipment to acquire pipeline service condition information and images for pipeline A, including pipeline specifications and composition, process parameters, composition of the transported medium, external environmental and geographical information, and pipeline failure information. Specific information is as follows:

[0177] 1) Pipeline specifications and composition: The pipeline specification is DN (inner diameter) 98mm, the total length of the branch pipeline is 1.846km, and the burial depth of the failure section is 1.8m.

[0178] 2) Process parameters: The pipeline design pressure and temperature is 60℃, and the design pressure is 16Mpa; the actual operating pressure is 15~15.8Mpa.

[0179] 3) Components of the transport medium: The transport medium is wastewater purification water, a highly acidic and mineralized environment with a pH value of 1.383 and a total mineralization of 24482.8 mg / L. The water sample type is calcium chloride type.

[0180] 4) External environment and geographical information of the pipeline: The construction depth of the failed section is 1.8m. The terrain of the area is aeolian desert landform, and the surface is covered by semi-fixed sand dunes. According to the service environment gas phase report, the highest ambient temperature is 42℃ and the lowest temperature is -32℃.

[0181] 5) Pipeline failure information: ① The failure location is 632m from the pipeline starting point, and the clock direction of the failure is 9 o'clock. ② The total length of the fiberglass pipe with metal fittings is 46 cm, the outer diameter of the pipe is 94.5 mm, and the wall thickness is 9.4 mm.

[0182] The second step involves the computing equipment performing primary identification based on pipeline service condition information and pipeline images, yielding the corresponding primary identification results. Primary identification includes damage or defect analysis, third-party damage analysis, natural disaster analysis, and operational analysis. Specifically:

[0183] 1) Damage or Failure Analysis: ① The GFRP consists of an outer fiberglass structural layer and a white inner liner. The exterior of the fiberglass pipe is reddish-brown, with irregular spots and stripes of resin peeling and gaps on the surface, the distribution of which follows the same direction as the fiberglass winding angle. ② The largest defect on the outer surface of the pipe is 6 cm below the end face, measuring 14×9 mm. The crack at the failure location is 10 cm long, with a maximum width of 3.5 cm, and the crack direction is at a 45° angle to the axial direction. The crack originates 2.5 cm above the metal joint. ③ At the fracture location, the fibers show obvious delamination along the winding angle, exhibiting distinct lamellar tearing characteristics; at the pipe break, the outer fiberglass structural layer is partially missing. ④ At the break location, the white inner liner bulges outward from the center, with thinning and deformation at the edges.

[0184] 2) Third-party damage analysis: No obvious signs of third-party damage were found.

[0185] Based on the first-level identification above, the result indicates that the fiberglass pipe has failed, and the failure category is manufacturing and construction defects and material aging. The failure phenomenon is caused by sustained overpressure bursting. The preliminary inference is that the pipe failure process is roughly as follows: the strength of a local area of ​​the external fiberglass structural layer decreased, causing local blistering of the white liner, which in turn led to local bursting failure. Therefore, this failure analysis focuses on analyzing the reasons for the local reduction in the structural strength of the external fiberglass structural layer of the pipe.

[0186] The third step involves the computing equipment determining that the primary identification results include pipeline failure, and that the failure categories include manufacturing and construction defects and material aging. Then, based on the pipeline to be analyzed and a control pipeline, secondary identification is performed to obtain the failure analysis results for the pipeline to be analyzed. Secondary identification includes physical performance testing, microstructure analysis, physicochemical performance analysis, thermal performance analysis, mechanical performance analysis, and stress simulation. Specifically:

[0187] A. Physical performance testing, including appearance inspection and defect assessment, resin content testing and curing degree testing.

[0188] (1) Appearance inspection and defect assessment: In accordance with the SY / T 6770.1-2023 high-pressure fiberglass pipe quality acceptance standard, multiple measurements of the pipe's inner and outer diameters, wall thickness, and structural layer thickness were performed using vernier calipers. First, the conformity of the fiberglass pipe sample's inner diameter with SY / T 6770.1-2023 was determined. Second, it was determined whether the pipe dimensions had changed significantly after long-term use in the service environment.

[0189] The test results are as follows:

[0190] ① The inner diameters of the three types of fiberglass pipe samples all meet the requirements of the current standard SY / T 6770.1-2023.

[0191] ② No significant changes in pipe size were observed during long-term use in the service environment.

[0192] (2) Resin content test: The resin content of the GFRP structural layer was determined by the loss on ignition method according to GB / T 2577-2005 Test Method for Resin Content of Glass Fiber. The resin content of the GFRP structural layer was found to be 18.827%. According to the standard SY / T 6770.1-2023, the specified range for resin content of FRP pipe is 30±5%. Therefore, it is concluded that the resin content of this FRP pipe does not meet the standard requirements.

[0193] (3) Curing degree test: i.e., the insoluble content of GFRP structural layer resin. According to GB / T 2576-2005 Test method for insoluble content of fiber reinforced plastic resin, powder samples were taken from different locations and depths of the pipe section for testing. ① Insoluble resin content (curing degree) of the inner wall: 83.69% in the non-failed location and 78.22% in the failed area; Insoluble resin content of the middle location: 79.2% in the non-failed location and 74.59% in the failed area, which are basically equal. ② Insoluble resin content (curing degree) of the outer surface: 78.97% in the non-failed location and 53.57% in the failed location, a decrease of 32%. Therefore, the insoluble resin content decreases overall from the non-failed area to the failed area. Along the wall thickness direction, the insoluble resin content in different areas decreases from the inner wall to the outer surface, and is significantly lower at the outer surface of the failed fracture site than in other locations. The curing process involves heating and curing from the inside out, resulting in a lower degree of curing on the outer surface of the pipe. Insufficient resin curing leads to relatively poor mechanical properties at this location, making it a weak point prone to failure and damage.

[0194] B. Microscopic morphology and structure analysis, including surface morphology analysis and interlayer fiber structure analysis.

[0195] (1) Surface morphology analysis: Based on the morphology of the GFRP layer at the fracture failure location observed by optical microscopy. ① Resin fragmentation and peeling were observed on the outer surface of the fracture location, and the internal fiber bundles were torn, unevenly distributed, and located in the relatively loose surface resin-rich area; the tearing direction was consistent with the glass fiber winding direction. ② Large-area structural layer delamination was found along the depth direction of the vertical section, and the delamination location was in the intermediate resin transition layer in the fiber winding direction. ③ Transverse cracks were found in local locations. The transverse cracks penetrated from the surface to the interlayer cracks, providing diffusion channels for medium leakage and intrusion. The cracks propagated non-linearly and contained a small number of air bubbles.

[0196] (2) Interlayer fiber structure: The cross-section and surface microstructure of the fracture area were observed by scanning electron microscopy. ① The fiberglass pipe has 11 layers of fiberglass winding structure. The thickness of a single layer of fiberglass is 496 μm, the thickness of the resin bonding layer is 24.7 μm, and the diameter of a single fiberglass is 10.9 μm. ② The fibers are symmetrically wound in two directions, with a fiber winding angle of ±45°. The fibers are evenly dispersed and completely impregnated. The bonding between the fiberglass and the resin is good, and no defects such as interlayer cracks and bubbles are observed. ③ There are pores formed by fiber detachment in local locations in the cross-sectional direction, but no obvious resin-rich layer is observed on the inner and outer surfaces of the resin, indicating a lack of resin layer protection.

[0197] Samples were taken from the glass tubing near the failure site and microscopically observed along the cross-sectional direction. ① Delamination and brittle fracture were observed on the surface of the fiber sheets at the fracture site; ② Interlaminar cracks were obvious, with a width of 350 μm, and the fiber and resin were completely detached in the same sheet on the fracture surface; ③ Transverse cracks were present along the depth direction of the tube wall, and the crack propagation direction changed to form a stepped pattern; ④ A pore with a diameter of up to 521 μm was found on the surface of the failure area due to resin detachment, and smooth-walled pores were found in the cross-section. Cavities formed by insufficient resin filling and cracks at the resin-fiber interface were found in the interlaminar sections.

[0198] C. Physicochemical property analysis, including chemical composition analysis and functional group analysis.

[0199] (1) Chemical composition analysis: The resin composition was determined by measuring the absorption spectra of different chemical bonds in the epoxy resin polymer at various failure sites using Fourier transform infrared spectroscopy. In this example, a Nicolet 6700 Fourier transform infrared spectrometer was used. By comparing the absorption peaks with those of standard epoxy resin and the vibrational frequencies of standard characteristic groups, it was confirmed that the matrix material of the fiberglass pipe was bisphenol A type epoxy resin, which was synthesized from bisphenol A propane and epichlorohydrin under alkaline catalytic conditions.

[0200] Because epoxy resin undergoes hydrolysis and aging, the hydrolysis products contain a large number of hydroxyl groups. The absorption intensity distribution of -OH bonds in pipe A verifies that the concentration of -OH groups is highest on the inner wall of the GFRP pipe, indicating that the oxidation and hydrolysis on the outer surface at the failure location of the pipe are relatively severe.

[0201] ② Functional Group Analysis: X-ray photoelectron spectroscopy (XPS) was used to determine functional groups and analyze the changes in the content of various elements and the concentration of major chemical bonds during the aging process of the pipe, verifying the oxidative degradation law of epoxy resin at the failed and unfailed locations of the pipe. All carbon (C) came from the resin matrix, and all silicon (Si) came from the glass fiber. Both epoxy resin and glass fiber provided sources of oxygen. The results showed that the oxygen content increased and the oxidation degree intensified at the outer layer of the GFRP pipe, with the highest oxygen concentration (27.12%) at the failed location (outer layer, A3). The decrease in C content in the outer layer also indicates a reduction in the resin content. Peak fitting of C1s at each location yielded the concentration changes of corresponding chemical bonds. The thermal degradation process of the cured epoxy resin produces superacid and superacid ester functional groups, and the formation of OC=O bonds also leads to an increase in C=O bond concentration. The results indicate that the oxidative degradation process is more significant at the outer layer of the failed area.

[0202] D. Thermal performance analysis, including glass transition temperature analysis, thermal decomposition temperature analysis, and dynamic thermomechanical performance analysis.

[0203] (1) Glass transition temperature analysis: The glass transition temperature of the failed FRP pipe was tested according to GB / T19466.2-2004 Differential scanning calorimetry for plastics, Part 2: Determination of glass transition temperature. The heating rate was 20℃ / min. The temperature was first raised to 100℃ and held for 5min, then rapidly cooled to 60℃ and held for 5min, and then raised to 180℃ at a heating rate of 20℃ / min and then cooled to room temperature. According to the standard SY / T 6770.1-2023, the glass transition temperature of aromatic amine-cured fiberglass pipes is specified within the range of (150±5)℃. Based on the experimental data from two tests, the Tg values ​​for each group can be obtained as follows: ① The Tg value at the failure location A2 is 156.6℃, and the Tg value at A3 is 163.9℃; ② The Tg value at the non-failure location B2 is 157.37℃, and the Tg value at B3 is 148.2℃; ③ The transition temperature at some locations, such as the failure point A3 on the outer layer of the pipe, deviates significantly from the specified range.

[0204] (2) Thermal decomposition temperature analysis: The decomposition rate and temperature of the resin were determined using a TGA550 thermogravimetric analyzer (i.e., the change in mass of the fiberglass resin polymer powder with temperature or time), and the contents of volatiles and fillers were also measured. The heating rate was 10℃ / min, and the heating was terminated at 600℃. According to the experimental results, the thermal decomposition temperature of the inner and middle layers in the failed and unfailed zones was around 364℃, while the decomposition temperature of the outer layer was 344℃, and the degradation rate of the outer layer was significantly greater than that of the inner layer.

[0205] (3) Dynamic Thermomechanical Properties (DMA) Analysis: The dynamic thermomechanical properties analysis method was used to analyze the relationship between the dynamic modulus and mechanical loss of the GFRP pipe under vibration load and temperature, and to determine the stability of the GFRP pipe under high temperature dynamic load. The comparison results show that the inner layer B1 position of the unfailed section in the initial stage of loading has a relatively large storage modulus value E0=1.83×10¹⁰Pa, and the modulus decreases by 17.5% after reaching the outer layer B2 position; at the failure position, the modulus value from the inner layer A1 to the outer surface A3 position decreases from 1.69×10¹⁰Pa to 1.38×10¹⁰Pa, and the modulus retention rate at the failure position is 81%, and the modulus decay from the unfailed position to the failure position is about 10%. The results show that the mechanical properties of the pipe decrease to a certain extent at the failure position, and the performance decay along the pipe wall thickness direction is greater than the axial performance decay from the unfailed position to the failure position.

[0206] E. Mechanical property analysis, including nanoindentation analysis, tensile property analysis, Barcol hardness analysis, and microhardness analysis.

[0207] (1) Nanoindentation analysis: A Bruker Hysitron TI980 nanoindenter was used to measure nanohardness and other indicators. Samples were taken from different locations on the pipe section according to the testing plan. In high-load mode, a maximum load of 0.3 N was selected, and five points were evenly spaced from the outer surface of the sample inwards. The maximum load (Pmax), maximum indentation depth (hmax), Young's modulus (Er), and nanohardness value (H) were used as evaluation indicators. The test results showed that: ① The average Young's modulus at the non-failure location was 24.18 GPa, and the nanohardness value was 3.04 GPa. ② The Young's modulus value near the failure fracture decreased by 19.3%, down to 19.51 GPa; the nanohardness at the corresponding location decreased by 47.1%, down to 1.61 GPa. ③ Under the same experimental conditions, the experimental results of the lining layer were verified; the modulus and hardness at the failure and non-failure locations of the lining layer showed almost no change.

[0208] (2) Tensile property analysis: To evaluate the changes in circumferential tensile strength and macroscopic mechanical properties of GFRP, symmetrical notched annular samples were taken from intact locations of the pipe section according to the ASTM 2290-2019 standard method for the apparent circumferential tensile strength test using the disc method for plastic or reinforced plastic pipes. Circumferential tensile strength was measured at a tensile rate of 10 mm / min according to the standard. The results showed that the average Young's modulus of the three samples was 26.2 GPa, the tensile strength was 905.6 MPa, and the elongation at break was 9.86%, which is basically consistent with the results of the nanoindentation test.

[0209] (3) Barcol Hardness Analysis: The hardness values ​​of the failed pipeline were determined according to GB / T 3854-2017 Barcol Hardness Test Method for Reinforced Plastics, combined with microhardness characterization method. ① The hardness values ​​of the inner wall of the pipe at different locations were all greater than those of the outer surface. At the failure location, the hardness value of the inner wall decreased slightly, while the hardness value of the outer surface increased. The hardness value did not change significantly along the axial direction of the pipeline. ② At the failure location of the pipe section, the Barcol hardness value of the inner surface was 62.5 HBa, and that of the outer surface was 55.4 HBa; at the tail of the pipe section, the hardness value of the inner wall was 60.6 HBa, and that of the outer surface was 56.7 HBa. ③ The microhardness value decreased significantly from the inner wall to the outer surface of the pipe section, and the pattern was consistent with the Barcol hardness variation pattern.

[0210] 4) Microhardness analysis: Samples were taken from the fiberglass structural layer inside the metal joint and the microhardness was tested. It was found that the microhardness value of the outermost layer was 62.77 HV, which was much lower than that of other structural layers. Elemental analysis by scanning electron microscopy revealed that there were fragmented glass fibers in the outer resin. The main components were oxides such as SiO2, CaO and Al2O3, which were consistent with the composition of glass fibers, but their size was uneven and the distribution was disordered.

[0211] F. Stress simulation.

[0212] A three-dimensional solid model of the pipeline, including a Q235 steel joint, fiber-reinforced plastic (FRP) core, and an inner lining, was constructed using SolidWorks. The pipe joint is made of Q235 steel with an elastic modulus of 210 GPa, a Poisson's ratio of 0.3, and a coefficient of linear expansion of 1.3 × 10⁻⁵ / K. Experimental measurements showed that the fiber-reinforced FRP structural layer has an elastic modulus of 29 GPa, a Poisson's ratio of 0.3, and a coefficient of linear expansion of 1.3 × 10⁻³ / K. The inner lining material is polyvinylidene fluoride (PVDF), with an elastic modulus of 2.3 GPa, an experimentally measured coefficient of linear expansion of 2.28 × 10⁻³ / K, and a Poisson's ratio of 0.3.

[0213] The boundary conditions were: the steel joint was radially fixed, the fiberglass base was supported, and there was no stress during assembly at 298 K. The loading conditions were: a uniform load of 16 MPa was applied internally to the pipe; a burial depth of 1.8 m was equivalent to a load of 4.5 × 10⁴ Pa applied axially perpendicular to the pipe; gravity was perpendicular to the pipe's axis. Based on service conditions, the temperature load was set at 315 K. A fine mesh was used at the joint and fiberglass mating area, while a coarse mesh was used in the pipe body area, with a total of approximately 1.1 × 10⁵ meshes. In the post-processing stage, finite element simulation was used to calculate and output the Mies stress distribution results at various locations along the pipeline, resulting in Mies stress distribution maps at different directions.

[0214] The results show that stress concentration occurs in the circumferential direction at the metal joint of the GFRP pipe. The maximum thermal stress is located within a range of 25 mm above the steel joint and 10 mm below the GFRP pipe, where the thermal stress is significantly higher than at other locations, reaching 100 MPa at 315 K. This stress concentration accelerates damage and aging in this area, making it a weak point in the pipeline. This is because the thermal expansion coefficients of the metal joint and the GFRP structural layer are mismatched, resulting in restricted radial expansion at the joint and thus the maximum stress. Simulation results of the joint's deformation under load show that the expansion deformation of the GFRP pipe is not constrained by the metal joint after 40 mm from the top edge of the joint, with a maximum deformation exceeding 1.59 mm. The thermal stress type at this location is bending stress, and delamination failure caused by external shear stress on the GFRP pipe is the main failure mode at this location.

[0215] Based on the results of the first-level and second-level identifications above, the failure analysis results for pipeline A are as follows:

[0216] (1) The deformation of the GFRP pipe section on a macroscopic scale is not obvious and still meets the requirements of standard SY / T 6770.1-2023.

[0217] (2) Morphological analysis revealed that there were defects such as insufficient resin filling bubbles and fiber-resin interface cracking inside the GFRP pipe. The failure site first delaminated and debonded, forming whitening. In the resin-rich area, there were layered tears of glass fiber, which were in the same direction as the fiber winding, and through cracks formed by the transverse expansion of microcracks were found.

[0218] (3) The overall resin content of the GFRP pipe section is 18.8%, which is lower than the standard value range (25%~35%). This will result in insufficient bonding strength between the wound glass fibers. Under shear load, it is easy to debond and delaminate. The content of insoluble resin decreases significantly from the unfailed area to the failure area. The infrared spectroscopy results also verify that the local components of the pipe have undergone oxidative degradation, resulting in a decrease in the curing degree of the failure area.

[0219] (4) The test results of mechanical properties show that the mechanical properties of GFRP pipes decrease at the failure location. The Young's modulus value at the failure location decreases by 19.3%; the nano hardness decreases by 47.1%, and the Barcol hardness and microhardness decrease from the inside to the outside.

[0220] (5) Based on the analysis results of the GFRP liner, the mechanical properties of the failed and unfailed locations, such as the elastic modulus and hardness, did not change significantly. The composition changes and thermal stability degradation were also not obvious, and the corrosion of the liner and metal joints was not obvious.

[0221] (6) The stress simulation results of the GFRP joint area show that there is stress concentration in the circumferential direction near the joint. The thermal stress concentration location is within a range of 25mm above the steel joint and 10mm below the GFRP pipe. The maximum stress value is 100MPa and the maximum radial expansion deformation is 1.59mm. The stress concentration at this location will accelerate the damage and failure of the pipeline.

[0222] Based on the failure analysis results of pipeline A above, combined with the analysis of pipeline operation, the main reasons for the failure of this FRP pipe are as follows:

[0223] 1) The impact of material aging

[0224] Damage to the internal structure of the GFRP pipe due to poor pipe quality or aging during service can reduce its performance, specifically manifested as follows:

[0225] (1) The overall resin content of the GFRP structural layer is reduced. The resin content test shows that the overall resin content of the GFRP pipe is only about 18.8%, which is lower than the standard range. The low resin content will lead to poor bonding strength of the glass fiber, and the GFRP pipe is prone to delamination and debonding failure when subjected to shear load.

[0226] (2) The curing degree of the GFRP pipe is significantly low. During the curing process, the heating and curing process is from the inside to the outside, which leads to the low curing degree of the outer surface of the pipe. The experimental results also verified that the surface resin curing degree at the failure location was only 53.57%. Insufficient resin curing resulted in relatively poor mechanical properties at this location, making it a weak point that is prone to failure and damage.

[0227] (3) The decrease in the mechanical properties of GFRP pipe, such as the elastic modulus and hardness, was verified by the experimental results of nanoindentation and Barcol hardness. The Young's modulus decreased by 19.3% from the unfailed position to the failure position along the axial direction of the pipe section, the nano hardness decreased by 47.1%, and the Barcol hardness decreased slightly from the inner wall to the outer surface.

[0228] (4) The decline in the thermal stability of GFRP pipes is partly due to the oxidation and decomposition of the resin on the outer surface caused by long-term service in the working environment. Infrared spectroscopy confirmed that the concentration of chemical bonds in the amine curing agent decreased during the aging process. During the aging process, the hydroxyl groups of epoxy resin were oxidized to aldehyde compounds, and the carbon chains were broken. The methylene groups were oxidized to amides. The thermal analysis results showed that the thermal decomposition temperature of the outer surface of the GFRP pipe decreased from 364℃ in the inner layer to 344℃ after aging. It also confirmed the decrease in the surface modulus of the GFRP pipe. Both of these indicate that the thermal stability of the pipe's exterior is reduced, which will lead to the degradation of the pipe's external performance.

[0229] 2) Influence of stress factors

[0230] Due to the difference in thermal conductivity between metallic and non-metallic materials, stress is induced at pipe joint locations under ambient temperature changes, or stress concentration occurs during joint assembly. Stress simulation results at GFRP joint locations show stress concentration along the circumferential direction of the joint, with thermal stress reaching 100 MPa at an ambient temperature of 315 K (41.85℃). The stress concentration at this location, caused by the necking effect under the constraint of the external metallic joint, accelerates pipe damage and failure.

[0231] 3) Comprehensive Analysis

[0232] After comprehensive analysis, the causes of pipeline failure are as follows:

[0233] (1) Due to the presence of a certain number of manufacturing defects at the initial location of the pipeline failure, such as internal pores, insufficient resin filling, and resin fiber interface debonding.

[0234] (2) During service, the pipeline will age and fail over time. This is mainly due to the decrease in resin content and curing degree on the outer surface of the GFRP structural layer, which leads to a decrease in fracture toughness and makes it prone to brittle fracture. Furthermore, tests of mechanical properties such as modulus and hardness have also verified the decline in mechanical properties at this location. Finally, under the combined action of thermal stress caused by changes in ambient temperature and stress concentration caused by residual assembly stress, the pipeline will eventually burst from the inside out and fail.

[0235] The failure process is as follows:

[0236] (1) In the early stage of failure, crack sources are generated at the weak points on the outer surface of the pipeline due to external load impact or pipeline aging. Whitening phenomenon is observed. Under the stress of the medium pressure, delamination and fiber debonding extend along the fiber winding direction, resulting in aging failure, including fiber debonding and resin-fiber delamination failure.

[0237] (2) During long-term service of the pipeline, due to the alternating changes in ambient temperature and the difference in thermal conductivity of joint materials, shear stress is generated in the upper axial direction of the pipeline, which further accelerates the delamination of the pipeline and leads to stress failure. The crack source generates microcracks along the depth direction, which extend from the inner surface to the outside under the action of circumferential stress. When the microcracks reach the outer surface and form a through crack, leakage occurs.

[0238] (3) In the final stage of aging failure, the fiber adhesion decreases after the resin in the pipeline debonds, and large-area fiber brittle fracture occurs after being loaded. Accompanied by the fiber being pulled out and forming holes, the overall failure mode is fiber fracture failure from the inside out.

[0239] Figure 3 This is a schematic diagram of the structure of a computing device according to an embodiment of this application, as shown below. Figure 3 As shown, the computing device includes: an acquisition module 31, used to acquire pipeline service condition information and pipeline images of the pipeline to be analyzed, the pipeline images including damaged parts of the pipeline to be analyzed; and a processing module 32, used to perform primary identification based on the pipeline service condition information and pipeline images to obtain corresponding primary identification results; determine whether secondary identification of the pipeline to be analyzed is required based on the primary identification results, the secondary identification including pipeline performance testing and analysis, the analysis accuracy of the secondary identification being higher than that of the primary identification; if required, perform secondary identification based on the pipeline to be analyzed and a control pipeline to obtain the failure analysis results of the pipeline to be analyzed, the control pipeline being a pipeline from the same batch as the pipeline to be analyzed that has not been used, or a pipeline that has not failed. In one embodiment, the specific implementation function of the computing device can be described in steps S101-S105 of Embodiment 1, and will not be repeated here.

[0240] In one possible implementation, the processing module 32 can also be used for:

[0241] Based on pipeline service condition information, generate image labels for pipeline images;

[0242] The pipeline image with image label is input into the failure analysis model to perform first-level failure analysis and obtain the corresponding first-level identification results. The first-level identification results include whether the pipeline under analysis has failed and the failure category when the pipeline fails.

[0243] The computing device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0244] In one possible implementation, the processing module 32 can also be used for:

[0245] A training sample set is generated based on images of failed and unfailed pipelines. Both images of failed and unfailed pipelines are labeled with image tags and failure tags. The image tags are generated using the pipeline service condition information corresponding to the image. The failure tags include whether the pipeline has failed and the failure category when the pipeline fails.

[0246] The initial neural network model is trained based on the training sample set to obtain the failure analysis model.

[0247] The computing device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0248] In one possible implementation, the processing module 32 can also be used for:

[0249] When the primary identification result indicates that the pipeline is not failed, it is determined that secondary identification is not required for the pipeline to be analyzed, and the failure analysis result of the pipeline to be analyzed is determined to be that the pipeline is not failed.

[0250] When the primary identification result includes pipeline failure, determine whether the failure category of the pipeline failure includes manufacturing and construction defects and / or material aging.

[0251] If not, then it is determined that secondary identification of the pipeline to be analyzed is not required, and the failure analysis results of the pipeline to be analyzed are determined based on the results of primary identification.

[0252] If so, then secondary identification of the pipeline to be analyzed is required.

[0253] The computing device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0254] In one possible implementation, the processing module 32 can also be used for:

[0255] Obtain the standard analysis results of the control pipeline. The standard analysis results include whether the control pipeline has manufacturing defects and the type of manufacturing failure caused by the manufacturing defects. The standard analysis results are obtained by comparing the first performance analysis results of the control pipeline with the standard information of the standard pipeline. The first performance analysis results are obtained by conducting physical performance tests and microstructure analysis on the control pipeline.

[0256] The second performance analysis results of the control pipeline were obtained. The second performance analysis results were obtained by performing performance analysis and simulation on the control pipeline. The performance analysis and simulation included physicochemical performance analysis, thermal performance analysis, mechanical performance analysis and stress simulation.

[0257] Collect pipe samples of the pipe to be analyzed, and perform performance analysis and simulation on the pipe samples to obtain the third performance analysis results. The pipe samples include the failure area, the non-failure area, and the intermediate area between the failure area and the non-failure area.

[0258] When the standard analysis results show that the control pipeline has no manufacturing defects, the failure analysis results of the pipeline to be analyzed are determined based on the second and third performance analysis results.

[0259] When the standard analysis results include manufacturing defects in the control pipeline, the failure analysis results of the pipeline to be analyzed are determined based on the standard analysis results, the second performance analysis results, and the third performance analysis results.

[0260] The computing device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0261] In one possible implementation, the processing module 32 can also be used for:

[0262] The results of the third performance analysis are compared with the results of the second performance analysis to obtain the aging stress analysis results of the pipeline to be analyzed. The aging stress analysis results include whether the pipeline to be analyzed has failed due to aging stress, and the type of aging stress failure when it fails due to aging stress. The type of aging stress failure includes one or more of chemical aging, physical aging, and stress failure.

[0263] Based on the aging stress analysis results and the first-level identification results, the failure analysis results of the pipeline to be analyzed are determined.

[0264] The computing device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0265] In one possible implementation, the processing module 32 can also be used for:

[0266] Based on the manufacturing failure categories caused by manufacturing defects in the standard analysis results, the manufacturing analysis results of the pipeline to be analyzed are determined. The manufacturing analysis results include the existence of manufacturing defects in the pipeline to be analyzed and the corresponding manufacturing failure categories. The manufacturing failure categories include one or more of the following: parameter specification defects, resin dripping, resin peeling, fiber breakage, fiber debonding, interlayer inclusions, and resin and fiber delamination.

[0267] The results of the third performance analysis are compared with the results of the second performance analysis to obtain the aging stress analysis results of the pipeline to be analyzed. The aging stress analysis results include whether the pipeline to be analyzed has failed due to aging stress, and the type of aging stress failure when it fails due to aging stress. The type of aging stress failure includes one or more of chemical aging, physical aging, and stress failure.

[0268] Based on the manufacturing analysis results, aging stress analysis results, and primary identification results, the failure analysis results of the pipeline to be analyzed are determined.

[0269] The computing device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0270] Figure 4 This is a schematic diagram of the structure of a computing device according to an embodiment of this application, as shown below. Figure 4 As shown, the computing device includes: a processor 401 and a memory 402 communicatively connected to the processor 401; the memory 402 stores computer execution instructions; the processor 401 executes the computer execution instructions stored in the memory 402 to implement the steps of the pipeline failure analysis method in the above-described method embodiments.

[0271] In the aforementioned computing device, the memory 402 and the processor 401 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines, such as a bus connection. The memory 402 stores computer execution instructions that implement data access control methods, including at least one software functional module that can be stored in the memory 402 in the form of software or firmware. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402.

[0272] The memory 402 may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 402 stores programs, which are executed by the processor 401 upon receiving execution instructions. Furthermore, the software programs and modules within the memory 402 may also include an operating system, which may include various software components and / or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.) and can communicate with various hardware or software components to provide an operating environment for other software components.

[0273] Processor 401 can be an integrated circuit chip with signal processing capabilities. The aforementioned processor 401 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor.

[0274] An embodiment of this application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the steps of the various method embodiments of this application.

[0275] An embodiment of this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the various method embodiments of this application.

[0276] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0277] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0278] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0279] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0280] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0281] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the appended claims.

[0282] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method of analyzing pipe failure, characterized by, The method comprises the following steps: obtaining pipeline service condition information and a pipeline image of a pipeline to be analyzed, wherein the pipeline image comprises a damage position of the pipeline to be analyzed; performing first-level identification according to the pipeline service condition information and the pipeline image to obtain a corresponding first-level identification result; determining whether second-level identification needs to be performed on the pipeline to be analyzed according to the first-level identification result, wherein the second-level identification comprises performance testing and analysis of the pipeline, and the analysis accuracy of the second-level identification is higher than that of the first-level identification; if so, performing second-level identification on the pipeline to be analyzed and a reference pipeline to obtain a failure analysis result of the pipeline to be analyzed, wherein the reference pipeline is a pipeline of the same batch as the pipeline to be analyzed and has not been used or is a pipeline that has not failed.

2. The method of analyzing pipe failure according to claim 1, wherein, The first-level identification according to the pipeline service condition information and the pipeline image to obtain a corresponding first-level identification result comprises the following steps: generating an image label of the pipeline image according to the pipeline service condition information; inputting the pipeline image with the image label into a failure analysis model to perform first-level failure analysis and obtain a corresponding first-level identification result, wherein the first-level identification result comprises whether the pipeline to be analyzed has failed and a failure category of the pipeline when it fails.

3. The method of claim 2, wherein, The failure analysis model is obtained in the following manner: generating a training sample set according to failure pipeline images and non-failure pipeline images, wherein the failure pipeline images and the non-failure pipeline images both have an image label and a failure label, the image label is generated by using pipeline service condition information corresponding to the image, and the failure label comprises whether the pipeline has failed and a failure category of the pipeline when it fails; training an initial neural network model according to the training sample set to obtain the failure analysis model; wherein the pipeline service condition information comprises one or more of pipeline specifications and composition, process parameters, conveying medium components, pipeline external environment and geographic information, pipeline failure information, and third-party construction information; and the failure category comprises one or more of improper operation, third-party damage, manufacturing and construction defects, natural disasters, and material aging.

4. The method of claim 3, wherein The determination of whether second-level identification needs to be performed on the pipeline to be analyzed according to the first-level identification result comprises the following steps: when the first-level identification result comprises that the pipeline has not failed, it is determined that second-level identification does not need to be performed on the pipeline to be analyzed, and a failure analysis result of the pipeline to be analyzed is determined to be that the pipeline has not failed; when the first-level identification result comprises that the pipeline has failed, it is determined whether the failure category of the pipeline when it fails comprises manufacturing and construction defects and / or material aging; if not, it is determined that second-level identification does not need to be performed on the pipeline to be analyzed, and the failure analysis result of the pipeline to be analyzed is determined according to the first-level identification result; if so, it is determined that second-level identification needs to be performed on the pipeline to be analyzed.

5. The method of claim 4, wherein, The second-level identification performed on the pipeline to be analyzed and the reference pipeline to obtain the failure analysis result of the pipeline to be analyzed comprises the following steps: obtaining a standard analysis result of the control pipeline, the standard analysis result including whether the control pipeline has a manufacturing defect and a manufacturing failure category caused by the manufacturing defect, the standard analysis result being obtained by comparing a first performance analysis result of the control pipeline with standard information of a standard pipeline, the first performance analysis result being obtained by performing physical performance testing and micro-morphology analysis on the control pipeline; obtaining a second performance analysis result of the control pipeline, the second performance analysis result being obtained by performing performance analysis and simulation on the control pipeline, the performance analysis and simulation including physicochemical performance analysis, thermal performance analysis, mechanical performance analysis, and stress simulation; collecting a pipeline sample of the pipeline to be analyzed, and performing performance analysis and simulation on the pipeline sample to obtain a third performance analysis result, the pipeline sample including a failure region, a non-failure region, and an intermediate region between the failure region and the non-failure region; when the standard analysis result includes that the control pipeline does not have a manufacturing defect, determining a failure analysis result of the pipeline to be analyzed according to the second performance analysis result and the third performance analysis result; when the standard analysis result includes that the control pipeline has a manufacturing defect, determining a failure analysis result of the pipeline to be analyzed according to the standard analysis result, the second performance analysis result, and the third performance analysis result.

6. The method of claim 5, wherein, The determining of the failure analysis result of the pipeline to be analyzed according to the second performance analysis result and the third performance analysis result includes: comparing the third performance analysis result with the second performance analysis result to obtain an aging stress analysis result of the pipeline to be analyzed, the aging stress analysis result including whether the pipeline to be analyzed is aging stress failed and an aging stress failure category when the pipeline to be analyzed is aging stress failed, the aging stress failure category including one or more of chemical aging, physical aging, and stress failure; determining the failure analysis result of the pipeline to be analyzed according to the aging stress analysis result and the primary identification result.

7. The method of claim 5, wherein, The determining of the failure analysis result of the pipeline to be analyzed according to the standard analysis result, the second performance analysis result, and the third performance analysis result includes: determining a manufacturing analysis result of the pipeline to be analyzed according to a manufacturing failure category caused by the manufacturing defect in the standard analysis result, the manufacturing analysis result including that the pipeline to be analyzed has a manufacturing defect and a corresponding manufacturing failure category, the manufacturing failure category including one or more of parameter specification defect, resin dripping, resin peeling, fiber breakage, fiber debonding, interlayer inclusion, and resin and fiber delamination; comparing the third performance analysis result with the second performance analysis result to obtain an aging stress analysis result of the pipeline to be analyzed, the aging stress analysis result including whether the pipeline to be analyzed is aging stress failed and an aging stress failure category when the pipeline to be analyzed is aging stress failed, the aging stress failure category including one or more of chemical aging, physical aging, and stress failure; According to the manufacturing analysis result, the aging stress analysis result, and the primary identification result, a failure analysis result of the pipeline to be analyzed is determined.

8. A computing device, comprising: The method comprises the steps of: an acquisition module, configured to acquire pipeline service condition information of a pipeline to be analyzed and a pipeline image, the pipeline image comprising a damage site of the pipeline to be analyzed; a processing module, configured to perform primary identification according to the pipeline service condition information and the pipeline image to obtain a corresponding primary identification result; determine whether secondary identification needs to be performed on the pipeline to be analyzed according to the primary identification result, the secondary identification comprising performance testing and analysis of the pipeline, and the analysis accuracy of the secondary identification being higher than that of the primary identification; if so, perform secondary identification on the pipeline to be analyzed and a reference pipeline to obtain a failure analysis result of the pipeline to be analyzed, the reference pipeline being a pipeline of the same batch as the pipeline to be analyzed and not being used or not being failed.

9. A computing device, comprising: The method comprises the steps of: a processor, and a memory connected to the processor in communication; the memory is configured to store computer execution instructions; the processor is configured to execute the computer execution instructions stored in the memory to implement the pipeline failure analysis method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the pipeline failure analysis method in any one of claims 1-7.

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