Oil and gas pipeline cathodic protection system fault diagnosis method, device and electronic equipment

Abstract

This application relates to the field of pipeline protection technology, and in particular to a fault diagnosis method, device, and electronic equipment for an oil and gas pipeline cathodic protection system. The method includes: determining the weight ratio among the de-energization potential, protection potential, and current resistance IR drop of each test pile based on a preset hierarchical model of system health; calculating the health of each test pile based on its de-energization potential, protection potential, and IR drop, and the weight ratio among these factors; obtaining the health of the oil and gas pipeline cathodic protection system based on the health of each test pile; and obtaining the fault diagnosis result of the oil and gas pipeline cathodic protection system based on its health. This solves the problems in related technologies where pipeline cathodic protection systems lack objective, accurate, and universal health assessment methods, leading to inaccurate identification, high false alarm rates, and missed alarm rates during actual use.

Description

Technical Field

[0001] This application relates to the field of pipeline protection technology, and in particular to a fault diagnosis method, device and electronic equipment for a cathodic protection system for oil and gas pipelines. Background Technology

[0002] With industrial development, the demand for energy transmission between cities in various regions is becoming increasingly strong. Long-distance oil and gas pipelines, as the most efficient, safe, and convenient mode of transportation, have been constructed and applied throughout the country. Cathodic protection systems, as an important component of pipeline protection, directly affect the safety and effectiveness of oil and gas pipeline networks.

[0003] Although domestic pipeline companies have added cathodic protection devices and established big data platforms, many problems still exist:

[0004] (1) The equipment fault alarm system is basically ineffective. More than 90% of the alarms are due to interference rather than equipment failure, which increases the burden of manual fault analysis. (2) Data analysis is mainly based on individual equipment and does not analyze from a global or systemic perspective, which easily leads to a large number of misjudgments. (3) The health status judgment only provides a "qualitative" reference, such as normal or abnormal, healthy or faulty, without reaching the level of "quantitative". (4) The method of judging the health status of the cathodic protection system is basically based on the existing experience of experts. There is a lack of relatively objective, accurate and universal health assessment methods, which easily leads to problems such as inaccurate identification, high false alarm rate and high missed alarm rate in actual use, which urgently need to be solved. Summary of the Invention

[0005] This application provides a fault diagnosis method, device, and electronic equipment for an oil and gas pipeline cathodic protection system, in order to solve the problems of inaccurate identification, high false alarm rate, and high missed alarm rate in the actual use of pipeline cathodic protection systems due to the lack of objective, accurate, and universal health assessment methods.

[0006] The first aspect of this application provides a fault diagnosis method for a cathodic protection system of an oil and gas pipeline, comprising the following steps: acquiring the de-energization potential, protection potential, and current resistance IR drop of multiple test piles; determining the weight ratio among the de-energization potential, protection potential, and IR drop of each test pile based on a preset hierarchical structure model of system health; calculating the health of each test pile according to the de-energization potential, protection potential, and IR drop of each test pile, and the weight ratio among the de-energization potential, protection potential, and IR drop of each test pile, and obtaining the health of the cathodic protection system of the oil and gas pipeline according to the health of each test pile, so as to obtain the fault diagnosis result of the cathodic protection system of the oil and gas pipeline according to the health of the cathodic protection system of the oil and gas pipeline.

[0007] Optionally, after acquiring the power-off potential, protection potential, and IR drop of the plurality of test piles, the method further includes: for each test pile, determining whether the power-off potential and the protection potential meet their respective valid conditions; if the power-off potential and the protection potential do not meet their respective valid conditions, then issuing a data missing anomaly alert; otherwise, determining whether the IR drop meets a preset existence condition; if the IR drop does not meet the preset existence condition, then issuing an IR drop anomaly alert.

[0008] Optionally, after determining that the IR drop satisfies the preset existence condition, the method further includes: inputting the de-energization potential, protection potential, and IR drop of the target test pile whose IR drop satisfies the preset existence condition into a preset single intelligent test pile steady-state identification model to obtain the steady-state identification result of the target test pile; if it is determined from the steady-state identification result that the test pile does not contain the target steady-state data, then inputting the data corresponding to the steady-state identification result of the target test pile into a preset joint test pile similarity matching model to obtain the waveform similarity matching degree of the target test pile, and determining whether the waveform similarity matching degree is greater than a preset threshold; if the waveform similarity matching degree is not greater than the preset threshold, then inputting the de-energization potential, protection potential, and IR drop of the target test pile into a preset fault identification model, and obtaining the fault type of the target test pile when the identification result is greater than the preset threshold.

[0009] Optionally, after obtaining the steady-state identification result of the target test pile, the method further includes: if the steady-state identification result determines that the test pile contains the target steady-state data and meets the preset continuous steady-state conditions, then the target test pile is determined to be fault-free; otherwise, the power-off potential, protection potential, and IR drop of the target test pile are input into a preset interference identification model to obtain the interference type of the target test pile.

[0010] Optionally, the interference types include DC high voltage transmission interference, subway interference, and other interference besides DC high voltage transmission interference and subway interference.

[0011] Optionally, after determining whether the waveform similarity matching degree is greater than the preset threshold, the method further includes: if the waveform similarity matching degree is greater than the preset threshold, then inputting the power-off potential, protection potential, and IR drop of the target test pile into a preset interference identification model to obtain the interference type of the target test pile.

[0012] Optionally, the above-mentioned fault diagnosis method for cathodic protection system of oil and gas pipeline further includes: obtaining the user's auxiliary identification result when the identification result is less than or equal to the preset threshold; and determining the fault type of the target test pile based on the user's auxiliary identification result.

[0013] A second aspect of this application provides a fault diagnosis device for a cathodic protection system of an oil and gas pipeline, comprising: a first acquisition module for acquiring the de-energization potential, protection potential, and current resistance IR drop of multiple test piles; a second acquisition module for determining the weight ratio between the de-energization potential, protection potential, and IR drop of each test pile based on a preset hierarchical structure model of system health; and a diagnosis module for calculating the health of each test pile based on the de-energization potential, protection potential, and IR drop of each test pile, and the weight ratio between the de-energization potential, protection potential, and IR drop of each test pile, and obtaining the health of the cathodic protection system of the oil and gas pipeline based on the health of each test pile, so as to obtain the fault diagnosis result of the cathodic protection system of the oil and gas pipeline based on the health of the cathodic protection system of the oil and gas pipeline.

[0014] Optionally, after acquiring the power-off potential, protection potential, and IR drop of the plurality of test piles, the first acquisition module is further configured to: for each test pile, determine whether the power-off potential and the protection potential meet their respective valid conditions; if the power-off potential and the protection potential do not meet their respective valid conditions, then issue a data missing anomaly alert; otherwise, determine whether the IR drop meets a preset existence condition; if the IR drop does not meet the preset existence condition, then issue an IR drop anomaly alert.

[0015] Optionally, after determining that the IR drop satisfies the preset existence condition, the first acquisition module is further configured to: input the power-off potential, protection potential, and IR drop of the target test pile whose IR drop satisfies the preset existence condition into a preset single intelligent test pile steady-state identification model to obtain the steady-state identification result of the target test pile; if it is determined from the steady-state identification result that the test pile does not contain the target steady-state data, then input the data corresponding to the steady-state identification result of the target test pile into a preset joint test pile similarity matching model to obtain the waveform similarity matching degree of the target test pile, and determine whether the waveform similarity matching degree is greater than a preset threshold; if the waveform similarity matching degree is not greater than the preset threshold, then input the power-off potential, protection potential, and IR drop of the target test pile into a preset fault identification model, and when the identification result is greater than the preset threshold, obtain the fault type of the target test pile.

[0016] Optionally, after obtaining the steady-state identification result of the target test pile, the first acquisition module is further configured to: if it is determined from the steady-state identification result that the test pile contains the target steady-state data and meets the preset continuous steady-state conditions, then determine that the target test pile is fault-free; otherwise, input the power-off potential, protection potential and IR drop of the target test pile into the preset interference identification model to obtain the interference type of the target test pile.

[0017] Optionally, the interference types include DC high voltage transmission interference, subway interference, and other interference besides DC high voltage transmission interference and subway interference.

[0018] Optionally, after determining whether the waveform similarity matching degree is greater than the preset threshold, the first acquisition module is further configured to: if the waveform similarity matching degree is greater than the preset threshold, input the power-off potential, protection potential and IR drop of the target test pile into a preset interference identification model to obtain the interference type of the target test pile.

[0019] Optionally, the fault diagnosis device for the cathodic protection system of the oil and gas pipeline described above further includes: a determination module, used to obtain the user's auxiliary identification result when the identification result is less than or equal to the preset threshold; and to determine the fault type of the target test pile based on the user's auxiliary identification result.

[0020] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the fault diagnosis method for an oil and gas pipeline cathodic protection system as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the fault diagnosis method for an oil and gas pipeline cathodic protection system as described in the above embodiments.

[0022] This application, based on a pre-defined hierarchical model of system health, determines the weight ratios among the de-energization potential, protection potential, and IR drop of each test pile. It then calculates the health status of each test pile based on these weight ratios, and obtains the health status of the cathodic protection system for oil and gas pipelines, thereby yielding the fault diagnosis results for the system. This addresses the problems in related technologies where pipeline cathodic protection systems lack objective, accurate, and universal health assessment methods, leading to inaccurate identification, high false alarm rates, and missed alarm rates during practical use. It reduces the workload of manual analysis and improves the accuracy of system fault and interference identification and the timeliness of early warning decisions.

[0023] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0025] Figure 1 This is a flowchart of a fault diagnosis method for a cathodic protection system for oil and gas pipelines according to an embodiment of this application;

[0026] Figure 2 This is a flowchart of a fault diagnosis method for a cathodic protection system for oil and gas pipelines according to an embodiment of this application;

[0027] Figure 3 This is a schematic flowchart illustrating the fault diagnosis process of a cathodic protection system for oil and gas pipelines according to an embodiment of this application.

[0028] Figure 4 The flowchart is shown below for an embodiment of the MFCC (Mel-scale Frequency Cepstral Coefficients) feature extraction algorithm according to this application.

[0029] Figure 5 This is a schematic diagram illustrating the principle of the DTW (Dynamic Time Warping) algorithm according to an embodiment of this application;

[0030] Figure 6 This is a schematic diagram illustrating the establishment of a system health model according to an embodiment of this application;

[0031] Figure 7 This is a schematic diagram of a system health model according to an embodiment of this application;

[0032] Figure 8 This is a flowchart illustrating the threshold indicators for the actual operation of a device according to one embodiment of this application;

[0033] Figure 9 This is a schematic diagram of a hierarchical model of system health according to an embodiment of this application;

[0034] Figure 10 This is a schematic diagram illustrating the weighting ratio according to one embodiment of this application;

[0035] Figure 11 This is a schematic diagram illustrating the fault levels corresponding to different health conditions according to one embodiment of this application;

[0036] Figure 12 This is a schematic diagram illustrating the overall health and single-point health of a system according to an embodiment of this application;

[0037] Figure 13This is a schematic diagram of the structure of a cathodic protection system according to an embodiment of this application;

[0038] Figure 14 This is a system architecture diagram of a software design platform according to an embodiment of this application;

[0039] Figure 15 This is a schematic diagram of the software interface of a health diagnosis cloud platform according to an embodiment of this application;

[0040] Figure 16 This is a schematic diagram illustrating the details of a health diagnosis of a single test pile according to one embodiment of this application;

[0041] Figure 17 This is an example diagram of a fault diagnosis device for a cathodic protection system for oil and gas pipelines according to an embodiment of this application;

[0042] Figure 18 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0043] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0044] The following description, with reference to the accompanying drawings, outlines a fault diagnosis method, apparatus, and electronic equipment for an oil and gas pipeline cathodic protection system according to embodiments of this application. Addressing the issues raised in the background section regarding the lack of objective, accurate, and universally applicable health assessment methods in pipeline cathodic protection systems, which often lead to inaccurate identification, high false alarm rates, and missed alarm rates during practical use, this application provides a fault diagnosis method for an oil and gas pipeline cathodic protection system. In this method, based on a preset hierarchical model of system health, the weight ratios between the de-energization potential, protection potential, and IR drop of each test pile are determined. The health of each test pile is calculated based on these weight ratios, and the overall health of the oil and gas pipeline cathodic protection system is obtained, thus yielding the fault diagnosis result. This method solves the problems of inaccurate identification, high false alarm rates, and missed alarm rates in pipeline cathodic protection systems due to the lack of objective, accurate, and universally applicable health assessment methods, reducing the workload of manual analysis and improving the accuracy of system fault and interference identification and the timeliness of early warning decisions.

[0045] Specifically, Figure 1 This is a flowchart illustrating a fault diagnosis method for a cathodic protection system for oil and gas pipelines, as provided in an embodiment of this application.

[0046] like Figure 1 As shown, the fault diagnosis method for the cathodic protection system of the oil and gas pipeline includes the following steps:

[0047] In step S101, the power-off potential, protection potential, and current resistance IR drop of multiple test piles are obtained.

[0048] Among them, the de-energized potential of the test pile can be understood as the potential difference of the test pile relative to the ground after the circuit is de-energized, the protective potential of the test pile can be understood as the potential value required to stop metal corrosion when cathodic protection of oil and gas pipelines, IR drop is the difference between the energized potential and the de-energized potential, and the IR drop should not be 0. The energized potential of the test pile refers to the potential difference of the test pile relative to the ground in the circuit.

[0049] Specifically, in this embodiment of the application, the power failure potential, protection potential, and IR drop data of multiple test piles within the most recent week (more than 48 hours) are obtained from the database for fault diagnosis of multiple test piles.

[0050] Optionally, in some embodiments, after acquiring the power-off potential, protection potential, and IR drop of multiple test piles, the method further includes: for each test pile, determining whether the power-off potential and protection potential meet their respective valid conditions; if the power-off potential and protection potential do not meet their respective valid conditions, issuing a data missing anomaly alert; otherwise, determining whether the IR drop meets a preset existence condition; if the IR drop does not meet the preset existence condition, issuing an IR drop anomaly alert.

[0051] It should be understood that, such as Figure 2 As shown in the embodiment of this application, it is determined whether the power-off potential and protection potential data of each test pile can be continuously collected. If they can be continuously collected, it is determined that the power-off potential and protection potential meet the valid conditions. It is further determined whether the IR drop of each test pile exists. If the IR drop of each test pile exists, it is determined that the IR drop meets the preset existence condition. If it does not exist, it is determined that the IR drop does not meet the preset existence condition, and an IR drop abnormality reminder is output. If data cannot be continuously collected, it is determined that the power-off potential and protection potential do not meet their respective valid conditions. At this time, a data missing abnormality reminder is output.

[0052] Furthermore, in some embodiments, after determining that the IR drop meets the preset existence conditions, the method further includes: inputting the de-energization potential, protection potential, and IR drop of the target test pile that meets the preset existence conditions into a preset single intelligent test pile steady-state identification model to obtain the steady-state identification result of the target test pile; if it is determined from the steady-state identification result that the test pile does not contain the target steady-state data, then inputting the data corresponding to the steady-state identification result of the target test pile into a preset joint test pile similarity matching model to obtain the waveform similarity matching degree of the target test pile, and determining whether the waveform similarity matching degree is greater than a preset threshold; if the waveform similarity matching degree is not greater than the preset threshold, then inputting the de-energization potential, protection potential, and IR drop of the target test pile into a preset fault identification model, and obtaining the fault type of the target test pile when the identification result is greater than the preset threshold.

[0053] The target steady-state data includes both steady-state waveforms and irregular waveforms. The preset threshold can be a user-defined threshold, a threshold obtained through a limited number of experiments, or a threshold obtained through a limited number of computer simulations; no specific limitations are imposed here. Fault types include reference electrode faults, damage to the anti-corrosion coating, other faults, and unknown faults.

[0054] Before inputting the power-off potential, protection potential, and IR drop of the target test pile that meet the preset existence conditions into the preset single intelligent test pile steady-state identification model, the embodiments of this application annotate the steady-state waveform, the disturbed waveform, and various fault waveforms.

[0055] Furthermore, in this embodiment, the de-energization potential, protection potential, and IR drop of the target test pile that meet the preset IR drop conditions are input into a preset single-intelligent test pile steady-state identification model. The MFCC algorithm is used to extract features from the waveform, and the steady-state identification result is output, thereby establishing a feature library, such as... Figure 3 As shown. If the steady-state identification result determines that the test pile does not contain the target steady-state data, then the data corresponding to the steady-state identification result of the target test pile is input into the preset joint test pile similarity matching model, and the waveform similarity matching degree of the target test pile is calculated by matching it with the feature library through the DTW algorithm.

[0056] Furthermore, in this embodiment, the waveform similarity is matched for each test pile and its neighbors based on the geographical distribution of the smart test piles. If the waveform similarity matching degree is not greater than a preset threshold, the power-off potential, protection potential and IR drop of the target test pile are input into a preset fault identification model and matched with the fault features in the fault management system to obtain the identification result. If the identification result is greater than a preset threshold, the fault type of the target test pile is obtained.

[0057] The MFCC feature extraction algorithm process is as follows: Figure 4As shown, it is specifically used to perform MFCC training on the steady-state recognition model, interference recognition model, and fault diagnosis model to adjust their respective suitable window sizes and operation coefficients, so as to achieve better recognition effects in various modes and minimize the total distance between waveforms within each state; the DTW algorithm is as Figure 5 shown. The DTW algorithm is used for similarity calculation, which can ignore the influence of waveforms in the time-frequency dimension and pay more attention to the overall change trend of the waveforms themselves.

[0058] Specifically, the MFCC feature extraction algorithm specifically boosts the high frequency of the input waveform signal through a high-pass filter, performs frame windowing processing on the waveform signal, and performs discrete Fourier transform on the waveform signal to obtain a Mel filter. After calculating the logarithmic energy output by each Mel filter, discrete cosine transform is performed, and cepstrum operation is performed on the waveform signal to output features.

[0059] The DTW algorithm is specifically as follows: calculate the shortest distance of QC, that is, find the shortest path starting from the origin to (Qn, Cm).

[0060] Q = q1, q2,..., qi,..., qn;

[0061] C = c1, c2,..., cj,..., cm;

[0062] The similarity distance calculation formula of QC is:

[0063]

[0064] Among them, the denominator K compensates for regularized paths of different lengths, W is the path, Wk = (i, j), k is the kth element of W, and the alignment relationship between the kth path point Q and C.

[0065] Among them, W = w1, w2,..., wk,..., wK, max(m, n) = <K < m + n - 1, and W1 = (1, 1), WK = (m, n).

[0066] Optionally, in some embodiments, after obtaining the steady-state recognition result of the target test pile, it further includes: if it is determined according to the steady-state recognition result that the test pile contains target steady-state data and meets the preset continuous steady-state condition, it is determined that the target test pile has no fault; otherwise, the off-potential, protection potential, and IR drop of the target test pile are input into a preset interference recognition model to obtain the interference type of the target test pile.

[0067] Among them, in some embodiments, the interference types include DC high-voltage transmission interference, subway interference, and other interference other than DC high-voltage transmission interference and subway interference.

[0068] Specifically, if the test pile contains the target steady-state data and can meet the preset continuous steady-state conditions, the output indicates that the target test pile is fault-free. If the test pile contains the target steady-state data, the power-off potential, protection potential, and IR drop of the target test pile are input into a preset interference identification model for interference identification. Feature extraction is performed using the MFCC algorithm to obtain DC high-voltage transmission interference feature values, subway interference feature values, and other interference feature values, which are then saved to the feature library to obtain the interference type of the target test pile. As production is updated, new interferences that have been manually verified are added to the feature library, updating the feature library accordingly.

[0069] Optionally, in some embodiments, after determining whether the waveform similarity matching degree is greater than a preset threshold, the method further includes: if the waveform similarity matching degree is greater than the preset threshold, then inputting the power-off potential, protection potential, and IR drop of the target test pile into a preset interference identification model to obtain the interference type of the target test pile.

[0070] Understandably, if the waveform similarity matching degree is greater than the preset threshold, it is determined that this area is affected by interference. The power-off potential, protection potential and IR drop of the target test pile are input into the preset interference identification model for interference identification, thereby obtaining the interference type of the target test pile.

[0071] Optionally, in some embodiments, the fault diagnosis method of the above-mentioned cathodic protection system for oil and gas pipelines further includes: obtaining the user's auxiliary identification result when the identification result is less than or equal to a preset threshold; and determining the fault type of the target test pile based on the user's auxiliary identification result.

[0072] Understandably, if the identification result is less than or equal to the preset threshold, it means that the fault type of the test pile is an unknown fault. At this time, the user enters the unknown fault management library to obtain the auxiliary identification result, and then obtains the fault type of the target test pile based on the auxiliary identification result.

[0073] As new faults are generated, they are updated. New faults obtained from the unknown fault management library are added to the feature library after manual verification, and the feature library is updated. In this way, the cathodic protection system of oil and gas pipelines can achieve the purpose of identifying new anomalies at a relatively low cost, thereby realizing the self-updating of the fault identification system.

[0074] In step S102, based on the preset hierarchical model of system health, the weight ratio between the power-off potential, protection potential and IR drop of each test pile is determined.

[0075] Among them, system health refers to the ability of a cathodic protection system for oil and gas pipelines to provide protective current that meets the relevant standards for long-distance oil and gas pipelines.

[0076] Specifically, such as Figure 6 and Figure 7 As shown in the embodiments of this application, addressing the problem that the maintenance of cathodic protection equipment relies heavily on domain experience and that evaluation indicators are singular and not intuitive, a pre-defined hierarchical structure model of system health is proposed. This model constructs evaluation dimensions and attributes based on relevant experience in the field and integrates AHP (Analytic Hierarchy Process) to determine the weighting indices of multi-attribute health. When this method is applied to maintenance decisions for cathodic protection equipment, the model can comprehensively analyze expert assessments and equipment data characteristics, intuitively and accurately reflecting the system's health status and providing effective output results.

[0077] Among them, the power outage potential, protection potential, and IR drop collected by the cathodic protection equipment all have corresponding national standards. However, considering the loss of current provided by the potentiostat during actual pipeline transmission, the system introduces a self-learning threshold algorithm. This algorithm can analyze equipment indicators under different physical environments, extracting steady-state data under stable operating conditions as its actual threshold range. Further considering both national standards and actual conditions, the system produces final indicator thresholds that conform to the actual operating conditions of each piece of equipment. Figure 8 As shown.

[0078] Taking the power failure potential of the test pile equipment as an example, the parameters are set as follows: the data from 285 minutes of stable operation is set as the standard value, and the fluctuation range of 0.025 is taken as the normal fluctuation range. Compared with the single-point judgment method, an additional 10% of abnormal piles can be identified, further reducing the missed detection rate.

[0079] Furthermore, in this embodiment, the weight ratios of multiple attributes are determined using the Analytic Hierarchy Process (AHP). For example... Figure 9 As shown, the target layer is set as the system health level, the focus layer is all the intelligent test piles under the system, and the indicator layer consists of the three core indicators of the intelligent test piles, which are also the indicators for verifying whether the current applied by the potentiostat is accurate and effective. These are the power-off potential, protection potential, and IR drop.

[0080] Furthermore, by establishing a pre-defined hierarchical model of system health, a pairwise comparison matrix is ​​constructed for consistency verification. This ultimately yields a relatively objective weighting ratio between the de-energization potential, protection potential, and IR drop for each test pile, i.e., de-energization potential: energization potential: IR drop = 6:3:1. Figure 10 As shown.

[0081] In step S103, the health of each test pile is calculated based on the power-off potential, protection potential, and IR drop of each test pile, as well as the weight ratio between the power-off potential, protection potential, and IR drop of each test pile. The health of the cathodic protection system of the oil and gas pipeline is obtained based on the health of each test pile, and the fault diagnosis result of the cathodic protection system of the oil and gas pipeline is obtained based on the health of the cathodic protection system of the oil and gas pipeline.

[0082] It is understood that in this embodiment, the health level of each test pile is determined according to a weighted ratio. The health level ranges from 0 to 100. The higher the health level, the better the cathodic protection effect of the pipeline and the lower the probability of corrosion. Conversely, the lower the health level, the worse the cathodic protection effect. 100 indicates that the system is very healthy and there is no external interference. 0 indicates a system-level fault, which has lost the ability to provide normal cathodic protection. The range of 0 to 100 is divided into five levels: healthy, good, attention, deterioration, and fault, to characterize the health level of the system at different stages of its life cycle.

[0083] Specifically, such as Figure 11 As shown, when the health level is greater than or equal to 0 and less than or equal to 20, the corresponding fault diagnosis result is "fault"; when the health level is greater than 20 and less than or equal to 40, the corresponding fault diagnosis result is "deterioration"; when the health level is greater than 40 and less than or equal to 60, the corresponding fault diagnosis result is "attention fault"; when the health level is greater than 60 and less than or equal to 80, the corresponding fault diagnosis result is "good"; and when the health level is greater than 80 and less than or equal to 100, the corresponding fault diagnosis result is "healthy".

[0084] For example, assuming the potentiostat is functioning normally, two intelligent test piles are deployed on the pipeline. The overall health of the cathodic protection system and the health at a single point are calculated. The calculations show that the health of test pile 1 is 80%*60% + 70%*30% + 100%*10% = 0.79, and the health of test pile 2 is 20%*60% + 30%*40% + 100%*10% = 0.34. The final system health is 50%*0.79 + 50%*0.34 = 0.565. It can be seen that the overall system health is in the "pay attention" range, mainly affected by the health of test pile 2, which has a health of only 0.34, placing it in the deterioration range. Combining this with the existing fault identification and anomaly identification functions of the big data platform, the location and cause of system anomalies can be quickly located, assisting maintenance personnel in decision-making. Figure 12 As shown.

[0085] Therefore, this application introduces the MFCC algorithm and DTW algorithm to learn the correlation between test piles and between test piles and potentiostats, effectively diagnosing fault waveforms and interference waveforms of the cathodic protection system. Based on the diagnostic results, a hierarchical structure model of system health is established to obtain the health status of the cathodic protection system of oil and gas pipelines. This provides a relatively objective, accurate, and universal method for assessing the health status of cathodic protection systems of oil and gas pipelines, changing the existing "qualitative" judgment to a "quantitative" assessment, and expanding from the existing single-point judgment to the system-level assessment, making the evaluation system more objective and accurate.

[0086] In addition, to prevent and mitigate pipeline corrosion, cathodic protection devices are typically installed around the pipeline using an impressed current. A complete cathodic protection system includes a potentiostat (one main and one backup) and numerous intelligent test piles buried around the pipeline, working together to protect the pipeline's safety throughout its entire lifecycle.

[0087] Specifically, such as Figure 13 As shown, a total of 2 potentiostats and 5 smart test piles were deployed around the pipeline. By analyzing the mileage of the potentiostats and smart test piles deployed on the pipeline, two cathodic protection systems were obtained. System 1 consists of potentiostat 1 and test piles 1 and 2, and system 2 consists of potentiostat 2 and test piles 3, 4 and 5. The systems do not affect each other and are jointly responsible for the safety of the entire pipeline section.

[0088] Both the potentiostat and the test piles have undergone intelligent upgrades. They regularly collect potential data from themselves and the surrounding pipelines, transmitting it wirelessly to an intelligent monitoring cloud platform. The cloud platform then performs all data analysis, including calculating system health, identifying faults and anomalies, and ultimately presenting the data in an informative manner.

[0089] This application's embodiments are based on the MVC (Model-View-Controller) design philosophy, with the software redesigned and developed using a B / S (Browser-Server) framework. It employs the lightweight Flask Web framework, including a front-end web page display, a Python-based back-end model processing module, and a MySQL-based data storage module. The software system architecture is as follows: Figure 14 As shown, the software system page is as follows Figure 15 As shown, the interface for health diagnosis of a single test pile is as follows: Figure 16 As shown.

[0090] The fault diagnosis method for the cathodic protection system of oil and gas pipelines proposed in this application, based on a preset hierarchical model of system health, determines the weight ratio among the de-energization potential, protection potential, and IR drop of each test pile. The health of each test pile is calculated based on the de-energization potential, protection potential, and IR drop, as well as the weight ratio among these factors. The health of the oil and gas pipeline cathodic protection system is then obtained based on the health of each test pile, leading to the fault diagnosis result. This solves the problems in related technologies where the lack of objective, accurate, and universal health assessment methods in pipeline cathodic protection systems easily leads to inaccurate identification, high false alarm rates, and missed alarm rates during actual use. It reduces the workload of manual analysis, improves the accuracy of system fault and interference identification, and enhances the timeliness of early warning decisions. For enterprises, it provides a more accurate judgment method, further reducing labor costs, improving efficiency, and better supporting digital transformation.

[0091] Next, with reference to the accompanying drawings, a fault diagnosis device for a cathodic protection system for oil and gas pipelines according to an embodiment of this application is described.

[0092] Figure 17 This is a block diagram of a fault diagnosis device for a cathodic protection system for oil and gas pipelines according to an embodiment of this application.

[0093] like Figure 17 As shown, the fault diagnosis device 10 of the cathodic protection system for oil and gas pipelines includes: a first acquisition module 100, a second acquisition module 200, and a diagnosis module 300.

[0094] The system comprises: a first acquisition module 100, used to acquire the de-energization potential, protection potential, and current resistance IR drop of multiple test piles; a second acquisition module 200, used to determine the weight ratio between the de-energization potential, protection potential, and IR drop of each test pile based on a preset hierarchical model of system health; and a diagnosis module 300, used to calculate the health of each test pile based on the de-energization potential, protection potential, and IR drop, as well as the weight ratio between them, and to obtain the health of the cathodic protection system for the oil and gas pipeline based on the health of each test pile, so as to obtain the fault diagnosis result of the cathodic protection system for the oil and gas pipeline based on the health of the cathodic protection system for the oil and gas pipeline.

[0095] Optionally, in some embodiments, after acquiring the power-off potential, protection potential, and IR drop of multiple test piles, the first acquisition module 100 is further configured to: for each test pile, determine whether the power-off potential and protection potential meet their respective valid conditions; if the power-off potential and protection potential do not meet their respective valid conditions, issue a data missing anomaly alert; otherwise, determine whether the IR drop meets a preset existence condition; if the IR drop does not meet the preset existence condition, issue an IR drop anomaly alert.

[0096] Optionally, in some embodiments, after determining that the IR drop meets the preset existence conditions, the first acquisition module 100 is further configured to: input the power-off potential, protection potential, and IR drop of the target test pile whose IR drop meets the preset existence conditions into a preset single intelligent test pile steady-state identification model to obtain the steady-state identification result of the target test pile; if it is determined from the steady-state identification result that the test pile does not contain the target steady-state data, then input the data corresponding to the steady-state identification result of the target test pile into a preset joint test pile similarity matching model to obtain the waveform similarity matching degree of the target test pile, and determine whether the waveform similarity matching degree is greater than a preset threshold; if the waveform similarity matching degree is not greater than the preset threshold, then input the power-off potential, protection potential, and IR drop of the target test pile into a preset fault identification model, and when the identification result is greater than the preset threshold, obtain the fault type of the target test pile.

[0097] Optionally, in some embodiments, after obtaining the steady-state identification result of the target test pile, the first acquisition module 100 is further configured to: if it is determined from the steady-state identification result that the test pile contains target steady-state data and meets the preset continuous steady-state conditions, then determine that the target test pile is fault-free; otherwise, input the power-off potential, protection potential and IR drop of the target test pile into the preset interference identification model to obtain the interference type of the target test pile.

[0098] Optionally, in some embodiments, the interference types include DC high-voltage transmission interference, subway interference, and other interference besides DC high-voltage transmission interference and subway interference.

[0099] Optionally, in some embodiments, after determining whether the waveform similarity matching degree is greater than a preset threshold, the first acquisition module 100 is further configured to: if the waveform similarity matching degree is greater than the preset threshold, input the power-off potential, protection potential and IR drop of the target test pile into a preset interference identification model to obtain the interference type of the target test pile.

[0100] Optionally, in some embodiments, the fault diagnosis device 10 of the above-mentioned oil and gas pipeline cathodic protection system further includes: a determination module, used to obtain the user's auxiliary identification result when the identification result is less than or equal to a preset threshold; and to determine the fault type of the target test pile based on the user's auxiliary identification result.

[0101] It should be noted that the explanation of the aforementioned embodiment of the fault diagnosis method for the cathodic protection system of oil and gas pipelines also applies to the fault diagnosis device of the cathodic protection system of oil and gas pipelines in this embodiment, and will not be repeated here.

[0102] The fault diagnosis device for the cathodic protection system of oil and gas pipelines proposed in this application determines the weight ratio between the de-energization potential, protection potential, and IR drop of each test pile based on a preset hierarchical model of system health. It then calculates the health of each test pile based on the weight ratio of these parameters, and obtains the health status of the oil and gas pipeline cathodic protection system, thereby yielding the fault diagnosis result. This solves the problems in related technologies where the lack of objective, accurate, and universal health assessment methods in pipeline cathodic protection systems leads to inaccurate identification, high false alarm rates, and missed alarm rates during actual use. It reduces the workload of manual analysis and improves the accuracy of system fault and interference identification and the timeliness of early warning decisions.

[0103] Figure 18 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0104] The memory 1801, the processor 1802, and the computer program stored on the memory 1801 and executable on the processor 1802.

[0105] When the processor 1802 executes the program, it implements the fault diagnosis method for the cathodic protection system of oil and gas pipelines provided in the above embodiments.

[0106] Furthermore, electronic devices also include:

[0107] Communication interface 1803 is used for communication between memory 1801 and processor 1802.

[0108] Memory 1801 is used to store computer programs that can run on processor 1802.

[0109] The memory 1801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0110] If the memory 1801, processor 1802, and communication interface 1803 are implemented independently, then the communication interface 1803, memory 1801, and processor 1802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 18 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0111] Optionally, in a specific implementation, if the memory 1801, processor 1802, and communication interface 1803 are integrated on a single chip, then the memory 1801, processor 1802, and communication interface 1803 can communicate with each other through an internal interface.

[0112] The processor 1802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0113] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described fault diagnosis method for the cathodic protection system of oil and gas pipelines.

[0114] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0115] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0116] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0118] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0119] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0120] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0121] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A fault diagnosis method for a cathodic protection system for oil and gas pipelines, characterized in that, Includes the following steps: Acquire the power-off potential, protection potential, and IR drop of multiple test piles; Based on the pre-defined hierarchical model of system health, the weight ratios among the power outage potential, protection potential, and IR drop of each test pile are determined. The health status of each test pile is calculated based on the power-off potential, protection potential, and IR drop of each test pile, as well as the weight ratio among the power-off potential, protection potential, and IR drop of each test pile. The health status of the cathodic protection system for oil and gas pipelines is obtained based on the health status of each test pile. The fault diagnosis result of the cathodic protection system for oil and gas pipelines is obtained based on the health status of the cathodic protection system for oil and gas pipelines. After obtaining the power-off potential, protection potential, and IR drop of the multiple test piles, the process also includes: For each test pile, determine whether the power-off potential and the protection potential meet their respective valid conditions; If the power-off potential and the protection potential do not meet their respective valid conditions, a data missing anomaly alert will be issued; otherwise, it will be determined whether the IR drop meets the preset existence conditions. If the IR drop does not meet the preset existence conditions, an IR drop anomaly alert will be issued; After determining that the IR drop satisfies the preset existence condition, the method further includes: The de-energization potential, protection potential, and IR drop of the target test pile that meet the preset existence conditions are input into the preset single intelligent test pile steady-state identification model to obtain the steady-state identification result of the target test pile. If it is determined from the steady-state identification result that the test pile does not contain the target steady-state data, then the data corresponding to the steady-state identification result of the target test pile is input into the preset joint test pile similarity matching model to obtain the waveform similarity matching degree of the target test pile, and it is determined whether the waveform similarity matching degree is greater than the first preset threshold. If the waveform similarity matching degree is not greater than the first preset threshold, the power-off potential, protection potential and IR drop of the target test pile are input into the preset fault identification model, and when the identification result is greater than the second preset threshold, the fault type of the target test pile is obtained.

2. The method according to claim 1, characterized in that, After obtaining the steady-state identification results of the target test pile, the process also includes: If the steady-state identification result determines that the test pile contains the target steady-state data and meets the preset continuous steady-state conditions, then the target test pile is determined to be fault-free. If the steady-state identification result determines that the test pile contains the target steady-state data but does not meet the preset continuous steady-state conditions, then the power-off potential, protection potential, and IR drop of the target test pile are input into the preset interference identification model to obtain the interference type of the target test pile.

3. The method according to claim 2, characterized in that, The types of interference include DC high-voltage transmission interference and subway interference.

4. The method according to claim 2, characterized in that, After determining whether the waveform similarity matching degree is greater than the first preset threshold, the method further includes: If the waveform similarity matching degree is greater than the first preset threshold, the power-off potential, protection potential and IR drop of the target test pile are input into the preset interference identification model to obtain the interference type of the target test pile.

5. The method according to claim 2, characterized in that, Also includes: When the recognition result is less than or equal to the second preset threshold, the user's auxiliary recognition result is obtained; The fault type of the target test pile is determined based on the user's assisted identification results.

6. A fault diagnosis device for an oil and gas pipeline cathodic protection system, suitable for using the fault diagnosis method for an oil and gas pipeline cathodic protection system according to any one of claims 1-5, characterized in that, include: The first acquisition module is used to acquire the power-off potential, protection potential, and IR drop of multiple test piles; The second acquisition module is used to determine the weight ratio between the power-off potential, protection potential and IR drop of each test pile based on the preset hierarchical structure model of system health. The diagnostic module is used to calculate the health status of each test pile based on the power-off potential, protection potential, and IR drop of each test pile, as well as the weight ratio among the power-off potential, protection potential, and IR drop of each test pile, and to obtain the health status of the cathodic protection system of the oil and gas pipeline based on the health status of each test pile, so as to obtain the fault diagnosis result of the cathodic protection system of the oil and gas pipeline based on the health status of the cathodic protection system of the oil and gas pipeline.

7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the fault diagnosis method for an oil and gas pipeline cathodic protection system as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the fault diagnosis method for the cathodic protection system of oil and gas pipelines as described in any one of claims 1-5.

Images