Method and system for evaluating reliability of petroleum pipeline sealing structure

Abstract

The invention provides a reliability evaluation method and system for a petroleum pipeline sealing structure. The method comprises the following steps: acquiring historical failure time data in different failure modes; selecting a failure mode, and determining the trend characteristic of the corresponding failure time and the distribution entropy of the failure time; determining the failure prediction time corresponding to the failure mode through the trend characteristics, and determining the failure confidence cost based on the failure time distribution entropy; determining the failure rate of the failure prediction time, performing cost compensation on the failure rate through failure confidence cost to obtain failure confidence, and repeating the steps to obtain the failure confidence of the remaining failure modes; and carrying out pre-evaluation on the reliability based on each failure confidence coefficient. According to the scheme, reliability evaluation is performed on the sealing structure based on the failure time distribution entropies of various failure modes, and the influence of a coupling effect on reliability evaluation of the petroleum pipeline sealing structure can be avoided, so that the accuracy of reliability evaluation of the sealing structure is improved.

Description

Technical Field

[0001] The present application relates to the technical field of reliability assessment, and more specifically, to a reliability assessment method and system for a petroleum pipeline sealing structure. Background Art

[0002] Reliability assessment refers to the use of systematic methods and tools to evaluate the ability of a product, system or component to operate without failure during its expected life cycle. Reliability assessment is crucial in various fields such as engineering, manufacturing, electronics, aerospace, and automobiles. It aims to ensure that products and systems can operate normally under specific conditions and within a specified time, reduce failures and maintenance costs, and improve safety and user satisfaction.

[0003] The reliability assessment of the sealing structure of the oil pipeline is a key method to ensure the safety and durability of the pipeline operation. It can help improve the overall safety of the pipeline and optimize the maintenance plan and resource allocation by comprehensively considering various risk factors that may exist in the design, manufacture, installation and operation of the pipeline. By regularly evaluating and monitoring the sealing structure of the oil pipeline, potential problems can be discovered in time and preventive measures can be taken, thereby minimizing the risk of pipeline accidents and environmental damage. However, in the existing technology, the method of finite element calculation combined with Monte Carlo simulation is often used. The failure process of the sealing structure is simulated by finite element simulation, and then the failure probability is directly calculated by the Monte Carlo method to evaluate the reliability of the sealing structure. Under this method, the sealing structure of the oil pipeline will be coupled due to the mutual influence of multiple failure modes (for example: the occurrence of one failure mode may increase the risk of another failure mode), resulting in errors in the accuracy of the obtained failure probability. Therefore, how to avoid the influence of coupling on the reliability assessment of the sealing structure of the oil pipeline, thereby improving the accuracy of the reliability assessment of the sealing structure has become a difficult problem faced by the industry. Summary of the invention

[0004] Based on this, the present application provides a reliability assessment method and system for a petroleum pipeline sealing structure, which can avoid the influence of coupling on the reliability assessment of the petroleum pipeline sealing structure, thereby improving the accuracy of the reliability assessment of the sealing structure.

[0005] In a first aspect, the present application provides a reliability assessment method for a petroleum pipeline sealing structure, comprising the following steps:

[0006] Obtain historical failure time data of the oil pipeline sealing structure to be evaluated under different failure modes;

[0007] Select a failure mode, and determine the corresponding failure time trend characteristics and failure time distribution entropy based on the historical failure time data under the failure mode;

[0008] The time when the failure mode of the oil pipeline sealing structure to be evaluated occurs is predicted by using the trend characteristics to obtain the failure prediction time corresponding to the failure mode, and the failure confidence cost under the failure mode is determined based on the failure time distribution entropy;

[0009] Determine the failure rate of the failure mode at the failure prediction time, and then compensate the failure rate by the failure confidence cost of the failure mode to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the failure mode, repeat the above steps to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the remaining failure modes;

[0010] The reliability of the oil pipeline sealing structure to be evaluated is pre-evaluated based on the failure confidence level under various failure modes.

[0011] In some embodiments, determining the trend characteristics of the corresponding failure time and the failure time distribution entropy according to the historical failure time data under the failure mode specifically includes:

[0012] Determine the trend component corresponding to each historical failure time in the historical failure time data under the failure mode, and combine all trend components into the trend characteristics of the failure time corresponding to the historical failure time data under the failure mode;

[0013] Determine the failure probability density of each historical failure time in the historical failure time data under the failure mode;

[0014] The failure time distribution entropy corresponding to the historical failure time data under this failure mode is determined through all failure probability densities.

[0015] In some embodiments, the time when the failure mode of the oil pipeline sealing structure to be evaluated occurs is predicted by using the trend characteristics, and the failure prediction time corresponding to the failure mode is obtained specifically including:

[0016] Determine the failure time potential component of the historical failure time data at each sampling moment through the trend characteristics;

[0017] Performing linear changes on all failure time potential components to obtain a linear trend sequence of the historical failure time data;

[0018] Determine a failure prediction parameter column according to the historical failure time data and the linear trend sequence;

[0019] The failure prediction parameter list is used to predict the time when the failure mode of the oil pipeline sealing structure to be evaluated will occur, so as to obtain the failure prediction time corresponding to the failure mode.

[0020] In some embodiments, determining the failure confidence cost under this failure mode based on the failure time distribution entropy specifically includes:

[0021] Performing maximum likelihood analysis on the failure time distribution entropy to obtain a failure parameter column;

[0022] The failure confidence cost under this failure mode is determined by the failure parameter column.

[0023] In some embodiments, determining the failure rate of the failure mode at the failure prediction time specifically includes:

[0024] Obtain the failure parameter column of this failure mode;

[0025] The failure rate of this failure mode at the failure prediction time is determined based on the failure prediction time of this failure mode and the corresponding failure parameter column.

[0026] In some embodiments, the failure rate is compensated by the failure confidence cost of the failure mode to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the failure mode, specifically including:

[0027] Compensating the failure rate corresponding to the failure mode according to the failure confidence cost of the failure mode to obtain the compensated failure rate of the failure mode;

[0028] The failure confidence of the petroleum pipeline sealing structure to be evaluated under this failure mode is determined by the compensation failure rate.

[0029] In some embodiments, the reliability of the oil pipeline sealing structure to be evaluated is pre-evaluated based on the failure confidence levels under various failure modes, specifically including:

[0030] Preset reliability threshold of sealing structure;

[0031] Select a failure mode of the sealing structure of the oil pipeline;

[0032] When the failure confidence in this failure mode is greater than or equal to the reliability threshold, it is determined that this failure mode will not occur in the failure prediction time under this failure mode;

[0033] When the failure confidence in this failure mode is less than the reliability threshold, it is determined that this failure mode will occur at the failure prediction time in this failure mode, and at this time, an alarm is sent to the management center and the corresponding failure mode is recorded;

[0034] Repeat the above steps to conduct a preliminary assessment of the reliability of the remaining failure modes of the petroleum pipeline sealing structure to be assessed.

[0035] In a second aspect, the present application provides a reliability assessment system for a petroleum pipeline sealing structure, the reliability assessment system for a petroleum pipeline sealing structure comprising:

[0036] An acquisition module is used to acquire historical failure time data of the oil pipeline sealing structure to be evaluated under different failure modes;

[0037] A processing module is used to select a failure mode and determine the trend characteristics and failure time distribution entropy of the corresponding failure time according to the historical failure time data under the failure mode;

[0038] The processing module is further used to predict the time when the failure mode of the oil pipeline sealing structure to be evaluated will occur based on the trend characteristics, obtain the failure prediction time corresponding to the failure mode, and determine the failure confidence cost under the failure mode based on the failure time distribution entropy;

[0039] The processing module is further used to determine the failure rate of the failure mode at the failure prediction time, and then compensate the failure rate by the failure confidence cost of the failure mode to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the failure mode, and repeat the above steps to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the remaining failure modes;

[0040] The execution module is used to pre-evaluate the reliability of the sealing structure of the oil pipeline to be evaluated based on the failure confidence under various failure modes.

[0041] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned reliability assessment method for the oil pipeline sealing structure.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions or codes, and when the instructions or codes are run on a computer, the computer implements the above-mentioned reliability assessment method for a petroleum pipeline sealing structure when executing the computer.

[0043] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0044] The reliability assessment method and system of the oil pipeline sealing structure provided by the present application are as follows: historical failure time data of the oil pipeline sealing structure to be assessed under different failure modes are obtained; a failure mode is selected, and the trend characteristics and failure time distribution entropy of the corresponding failure time are determined according to the historical failure time data under the failure mode; the time when the failure mode will occur in the oil pipeline sealing structure to be assessed is predicted according to the trend characteristics, and the failure prediction time corresponding to the failure mode is obtained, and the failure confidence cost under the failure mode is determined based on the failure time distribution entropy; the failure rate of the failure mode at the failure prediction time is determined, and then the failure rate is compensated by the failure confidence cost of the failure mode to obtain the failure confidence of the oil pipeline sealing structure to be assessed under the failure mode, and the above steps are repeated to obtain the failure confidence of the oil pipeline sealing structure to be assessed under the remaining failure modes; the reliability of the oil pipeline sealing structure to be assessed is pre-assessed based on the failure confidence under various failure modes.

[0045] It can be seen that in this application, when there are multiple failure modes in the sealing structure of the oil pipeline, first, the historical failure time data of the sealing structure of the oil pipeline to be evaluated under different failure modes are obtained, and a failure mode is selected to determine the dispersion degree of this failure mode in historical time, that is, the failure time distribution entropy. Then, based on the trend characteristics of the historical failure time data, the time when the failure mode occurs in the sealing structure of the oil pipeline to be evaluated is predicted to obtain the failure prediction time, so as to determine the influence of the first historical failure time of the sealing structure on the overall reliability of the sealing structure through the dispersion degree of this failure mode in history, that is, the failure confidence cost, and determine the failure rate of this failure mode at the corresponding failure prediction time, and avoid compensating the failure rate of the failure prediction time through the failure confidence cost, that is, considering the influence of the first failure time of the sealing structure on the overall failure rate of the sealing structure. Finally, the probability that the sealing structure will not have this failure mode is obtained by using 1-the compensated failure rate, that is, the failure confidence degree. Repeat the above steps to obtain the failure confidence of the sealing structure of the oil pipeline to be evaluated under the remaining failure modes, and then pre-evaluate the reliability of the sealing structure of the oil pipeline to be evaluated based on the failure confidence under various failure modes. In this application, after setting the situation that the sealing structure of the oil pipeline to be evaluated has multiple failure modes, the multiple failure modes are not considered at the same time, but each failure mode is analyzed independently, and the time when various failure modes will occur is predicted based on the historical failure time data of each failure mode, and the failure confidence of each failure mode at the failure prediction time is obtained. The failure mode identification and reliability analysis of the sealing structure are performed through the failure confidence of various failure modes, thereby avoiding the coupling effect (that is, the occurrence of one failure mode may increase the risk of another failure mode) generated by the sealing structure of the oil pipeline under multiple failure modes on the reliability evaluation; in summary, the method of the present application can avoid the influence of the coupling effect on the reliability evaluation of the sealing structure of the oil pipeline, thereby improving the accuracy of the reliability evaluation of the sealing structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a reliability evaluation method of a petroleum pipeline sealing structure according to some embodiments of the present application;

[0047] Figure 2 is a schematic diagram of a process for determining a linear trend sequence according to some embodiments of the present application;

[0048] Figure 3 is a schematic diagram of a process for determining a failure prediction time according to some embodiments of the present application;

[0049] Figure 4is a structural block diagram of a reliability evaluation system for a petroleum pipeline sealing structure according to some embodiments of the present application;

[0050] Figure 5 It is an internal structure diagram of a computer device for implementing a reliability assessment method for a petroleum pipeline sealing structure according to some embodiments of the present application. DETAILED DESCRIPTION

[0051] The core of the present application is to obtain the historical failure time data of the oil pipeline sealing structure to be evaluated under different failure modes; select a failure mode, and determine the trend characteristics and failure time distribution entropy of the corresponding failure time according to the historical failure time data under this failure mode; predict the time when the failure mode of the oil pipeline sealing structure to be evaluated will occur by the trend characteristics, and obtain the failure prediction time corresponding to the failure mode, and determine the failure confidence cost under this failure mode based on the failure time distribution entropy; determine the failure rate of the failure mode at the failure prediction time, and then compensate the failure rate by the failure confidence cost of the failure mode, and obtain the failure confidence of the oil pipeline sealing structure to be evaluated under this failure mode, repeat the above steps, and obtain the failure confidence of the oil pipeline sealing structure to be evaluated under the remaining failure modes; pre-evaluate the reliability of the oil pipeline sealing structure to be evaluated based on the failure confidence under various failure modes; avoid the influence of coupling on the reliability evaluation of the oil pipeline sealing structure, thereby improving the accuracy of the reliability evaluation of the sealing structure.

[0052] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is a flow chart of a reliability assessment method for a petroleum pipeline sealing structure according to some embodiments of the present application. The reliability assessment method 100 for a petroleum pipeline sealing structure mainly includes the following steps:

[0053] In step 101, historical failure time data of the oil pipeline sealing structure to be evaluated under different failure modes are obtained.

[0054] In specific implementation, the historical failure time data of the sealing structure of the oil pipeline to be evaluated under different failure modes can be obtained through the operation monitoring log of the oil pipeline.

[0055] It should be noted that the operation monitoring log includes the failure mode category and failure date of the sealing structure of the oil pipeline during the transportation of oil. The present application obtains the failure date of the sealing structure when each failure mode occurs through the operation monitoring log, and takes a set of all failure dates corresponding to various failure modes in the order of dates as the historical failure time data of the corresponding failure mode. In addition, in some preferred embodiments, the failure mode of the oil pipeline sealing structure can be divided into human failure and non-human failure. The failure modes corresponding to human failure include improper operation, insufficient maintenance, design defects and construction defects. The failure modes corresponding to non-human failure are: material aging, mechanical wear, chemical corrosion and stress relaxation, among which chemical corrosion is a failure mode that occurs in the early stage of oil pipeline operation and gradually intensifies as the operation time of the oil pipeline increases; stress relaxation is a failure mode that occurs in the middle stage of oil pipeline operation. As the operation time of the oil pipeline increases, stress relaxation will gradually appear, resulting in a decrease in sealing performance; mechanical wear is a failure mode that occurs in the long term during the operation of the oil pipeline, and the friction between the sealing structure and other components will cause the sealing structure to wear. Damage, thereby affecting the reliability of the seal; material aging is a failure mode that occurs in the later stage of the operation of the oil pipeline. Material aging refers to the gradual loss of the original physical and chemical properties of the sealing material under long-term exposure to environmental factors (such as temperature, humidity, ultraviolet rays, etc.). In addition, the type of oil pipeline also determines the failure mode of the sealing structure. For example, the submarine oil pipeline will have a failure mode of biological erosion. The biological erosion refers to marine organisms, such as shellfish, that will adhere to the pipeline and cause local corrosion, thereby reducing the sealing effect; the low-temperature oil pipeline will have a failure mode of condensate blockage. The condensate blockage is because the low temperature environment of the oil pipeline causes the wax and other heavy components in the oil to solidify, causing the pipeline to be blocked; as a preferred embodiment, the present application selects the failure mode of non-human failure in ordinary oil pipelines as a research sample (i.e., material aging, mechanical wear, chemical corrosion and stress relaxation), so as to have higher versatility. In other embodiments, the failure mode of human failure can also be obtained from the operation monitoring log as a research sample, and the failure mode of the oil pipeline sealing structure in some specific scenarios can also be selected to achieve the effect of targeted research, which is not limited here.

[0056] Optionally, in some embodiments, the sealing structure of the oil pipeline can be monitored by a monitoring device, and features of the sealing structure image of the oil pipeline transmitted by the monitoring device can be extracted by a machine learning algorithm. For example, a scale-invariant feature transform (SIFT) feature extraction algorithm is used to extract features of the sealing structure image of the oil pipeline, and corresponding image key points and their feature descriptors are obtained as image features. The extracted image features are compared with the failure mode features in the learning library to obtain the failure mode category and failure date of the sealing structure of the oil pipeline during the transportation of oil, thereby obtaining the operation monitoring log.

[0057] In step 102, a failure mode is selected, and the trend characteristics and failure time distribution entropy of the corresponding failure time are determined according to the historical failure time data under the failure mode.

[0058] In some embodiments, determining the trend characteristics and failure time distribution entropy of the corresponding failure time according to the historical failure time data under this failure mode can be achieved by using the following steps:

[0059] Determine the trend component corresponding to each historical failure time in the historical failure time data under the failure mode, and combine all trend components into the trend characteristics of the failure time corresponding to the historical failure time data under the failure mode;

[0060] Determine the failure probability density of each historical failure time in the historical failure time data under the failure mode;

[0061] The failure time distribution entropy corresponding to the historical failure time data under this failure mode is determined through all failure probability densities.

[0062] In specific implementation, determining the trend component corresponding to each historical failure time in the historical failure time data under this failure mode can be achieved in the following manner, namely: first, after obtaining the Haar wavelet as the wavelet function, the historical failure time data is decomposed into multiple wavelet coefficients of different frequencies through the wavelet transform in the prior art; secondly, the low-frequency wavelet coefficients among all the wavelet coefficients are retained as the trend component of the historical failure time data at each sampling moment; in other embodiments, it can also be determined by other methods, which are not limited here. It should be noted that the wavelet coefficients contain the trend characteristics of the historical failure time data, and the trend characteristics of each historical failure time in the historical failure time data can be extracted by retaining the low-frequency coefficients and discarding the high-frequency coefficients, and each trend characteristic is used as the trend component of each historical failure time in the historical failure time data.

[0063] In specific implementation, determining the failure probability density of each historical failure time in the historical failure time data under this failure mode can be achieved in the following manner, namely: first, selecting a historical failure time in the historical failure time data under this failure mode, secondly, obtaining the time influence parameter and the time expansion parameter of the sealing structure, and then using the result of comparing the time influence parameter with the time expansion parameter as the proportional coefficient, comparing the historical failure time with the time expansion parameter and using the time influence parameter-1 as the exponent of the result obtained by division, thereby using the obtained result as the failure time factor, and then, comparing the historical failure time with the time expansion parameter and using the time influence parameter as the exponent of the result obtained by division, and taking the negative of the result obtained by the above calculation as the exponent of the natural constant e, thereby using the obtained result as the time exponential decay term, and finally, using the product of the proportional coefficient, the failure time factor and the time exponential decay term as the failure probability density of the historical failure time, repeating the above steps to determine the failure probability density of the remaining historical failure times in the historical failure time data under this failure mode.

[0064] It should be noted that the time influence parameter characterizes the influence of time on the failure rate of the sealing structure. The value of the time influence parameter can be divided into less than 1, equal to 1 and greater than 1. When the time influence parameter is less than 1, it means that the failure rate of the sealing structure decreases with the increase of time. When the time influence parameter is equal to 1, it means that the failure rate of the sealing structure is independent of time. When the time influence parameter is greater than 1, it means that the failure rate of the sealing structure increases with the increase of time. In addition, the time expansion parameter characterizes the distribution range of the failure time of the sealing mechanism. The larger the time expansion parameter is, the longer the life of the sealing structure is. The smaller the time expansion parameter is, the shorter the life of the sealing structure is. By determining the life of the sealing structure, the failure time distribution range of the sealing structure can be determined. When the life of the sealing structure is longer, the distribution range of the failure time is larger. When the life of the sealing structure is shorter, the distribution range of the failure time is smaller.

[0065] It should be noted that the failure probability density, that is, the density of the failure rate of this failure mode occurring at the corresponding historical failure time, is larger, and the greater the failure probability density is, the greater the density of the failure rate of this failure mode occurring at the corresponding historical failure time is, and the smaller the failure probability density is, the smaller the density of the failure rate of this failure mode occurring at the corresponding historical failure time is. In addition, the present application ensures that the sum of the failure probability densities of all failure modes obtained is 1 by using the result of comparing the time influence parameter with the time expansion parameter as a proportional coefficient, and then controls the decreasing or increasing pattern of the obtained failure rate with time by determining the failure time factor. Finally, the cumulative effect of the failure rate is represented by the time exponential decay term, thereby determining the failure probability density of the sealing structure corresponding to the historical failure time under this failure mode.

[0066] It should be noted that the failure time distribution entropy characterizes the degree of dispersion of the failure conditions of the sealing structure in history. The larger the failure time distribution entropy, the higher the degree of dispersion of the failure conditions of the sealing structure in history. The smaller the failure time distribution entropy, the lower the degree of dispersion of the failure conditions of the sealing structure in history. As a preferred embodiment, determining the failure time distribution entropy corresponding to the historical failure time data under this failure mode through all failure probability densities can be achieved in the following manner, namely: first, obtaining the failure probability density of each historical failure time in the historical failure time data under this failure mode, and secondly, using the result of multiplying all failure probability densities as the failure time distribution entropy corresponding to the historical failure time data under this failure mode. In other embodiments, it can also be determined by other methods, which are not limited here.

[0067] In step 103, the time when the failure mode of the oil pipeline sealing structure to be evaluated occurs is predicted by using the trend characteristics to obtain the failure prediction time corresponding to the failure mode, and the failure confidence cost under the failure mode is determined based on the failure time distribution entropy.

[0068] It should be noted that each historical failure time in the historical failure time data has a unique corresponding sampling time. Therefore, in some embodiments, the time when the failure mode of the oil pipeline sealing structure to be evaluated occurs is predicted by the trend characteristics, and the failure prediction time corresponding to the failure mode is obtained. The following steps can be used to achieve this:

[0069] Determine the failure time potential component of the historical failure time data at each sampling moment through the trend characteristics;

[0070] Performing linear changes on all failure time potential components to obtain a linear trend sequence of the historical failure time data;

[0071] Determine a failure prediction parameter column according to the historical failure time data and the linear trend sequence;

[0072] The failure prediction parameter list is used to predict the time when the failure mode of the oil pipeline sealing structure to be evaluated will occur, so as to obtain the failure prediction time corresponding to the failure mode.

[0073] It should be noted that, the present application eliminates the random fluctuation of the trend component of the historical failure time data at each sampling moment by accumulating the trend component of the historical failure time data at each sampling moment to obtain the failure time potential component at the corresponding sampling moment, thereby highlighting the trend change law of the historical failure time data; as a preferred embodiment, determining the failure time potential component of the historical failure time data at each sampling moment through the trend feature can be achieved in the following manner, namely: first, selecting the trend component of the historical failure time data at a sampling moment in the trend feature, and secondly, taking the sum of the trend components corresponding to all sampling moments before the sampling moment as the failure time potential component of the sampling moment, repeating the above steps to determine the failure time potential components of the remaining sampling moments, thereby obtaining the failure time potential component of the historical failure time data at each sampling moment.

[0074] Wherein, in some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining a linear trend sequence shown in some embodiments of the present application. All failure time potential components are linearly changed to obtain the linear trend sequence of the historical failure time data, which can be achieved by the following steps:

[0075] First, in step 1031, the left adjacent failure time potential component of each failure time potential component is obtained;

[0076] Then, in step 1032, a linear trend value corresponding to each failure time potential component is determined according to each failure time potential component and the corresponding left adjacent failure time potential component;

[0077] Finally, in step 1033, all linear trend values ​​are further sorted according to the chronological order of the sampling moments of the corresponding failure time potential components to form a linear trend sequence of the historical failure time data.

[0078] It should be noted that the present application uses the failure time potential component of the failure time potential component at the previous sampling moment as the left adjacent failure time potential component of the corresponding failure time potential component. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0079] In specific implementation, the linear trend value corresponding to each failure time potential component is determined according to each failure time potential component and the corresponding left adjacent failure time potential component, which can be achieved in the following manner, namely: first, each failure time potential component is added to the corresponding left adjacent failure time potential component and the resultant is divided by 2; secondly, the obtained value is used as the linear trend value of the corresponding failure time potential component; in other embodiments, it can also be determined by other methods, which are not limited here. In addition, it should be noted that the present application extracts the trend of the historical failure time data, linearizes the trend characteristics of each sampling moment, and obtains a plurality of the linear trend values; the linear trend values ​​are used to linearize the trends of all failure time potential components, thereby improving the reliability of the prediction.

[0080] In some embodiments, determining the failure prediction parameter column according to the historical failure time data and the linear trend sequence can be implemented by the following steps:

[0081] Determine a historical failure time vector according to the historical failure time data;

[0082] Determine a failure trend matrix according to the linear trend sequence;

[0083] A failure prediction parameter column is determined according to the historical failure time vector and the failure trend matrix.

[0084] It should be noted that the historical failure time vector is a vector containing the historical failure times of the historical failure time data at all sampling moments, and the failure trend matrix is ​​a matrix containing the linear trend values ​​of all historical failure times in the historical failure time data. The present application predicts the future failure time by analyzing the historical failure time vector and the failure trend matrix. As a preferred embodiment, determining the historical failure time vector based on the historical failure time data can be achieved in the following manner, namely: taking each historical failure time in the historical failure time data as a vector element in the order of sampling moments, thereby obtaining the historical failure time vector. In addition, it should be noted that the historical failure time vector is expressed as ,in, is the historical failure time vector, is the historical failure time corresponding to the historical failure time data at the first sampling moment, and so on. The historical failure time data is The historical failure time corresponding to the sampling moment, It is the total number of historical failure times in the historical failure time data.

[0085] In specific implementation, determining the failure trend matrix according to the linear trend sequence can be implemented in the following manner, namely: first, obtain a matrix with a row number of , a matrix with 2 columns and all elements in the second column are 1. Secondly, each linear trend value in the linear trend sequence is negated and used as the matrix element of the first column in the matrix in the order of the corresponding sampling moments, thereby obtaining the failure trend matrix.

[0086] In some embodiments, the failure trend matrix can be represented in the following manner:

[0087]

[0088] in, is the failure trend matrix, represents the linear trend value corresponding to the first sampling moment in the linear trend sequence, Indicates the linear trend value corresponding to the second sampling moment in the linear trend sequence, and so on. Indicates the first The linear trend value corresponding to the sampling moment.

[0089] In specific implementation, the failure prediction parameter column can be determined according to the historical failure time vector and the failure trend matrix in the following manner: first, the failure trend matrix is ​​transposed to obtain the transposed matrix of the failure trend matrix: , and then, calculate the product of the failure trend matrix and the transposed matrix of the failure trend matrix to obtain the matrix Then, perform the inverse operation to obtain the inverse matrix , and then the product of the above inverse matrix, the transposed matrix of the failure trend matrix and the historical failure time vector is used as the failure prediction parameter column. It should be noted that the failure prediction parameter column is expressed as ,in, is the time prediction coefficient, is the time base parameter.

[0090] It should be noted that the failure prediction parameter column is the result obtained after analyzing the historical failure time vector and the failure trend matrix, wherein the time prediction coefficient is used to control the development trend of the historical failure time data, the larger the time prediction coefficient is, the faster the development trend of the historical failure time data is, and the smaller the time prediction coefficient is, the slower the development trend of the historical failure time data is; the time reference parameter is the reference degree of the predicted amount of the historical failure time data, the larger the time reference parameter is, the larger the predicted amount of the historical failure time data is, and the smaller the time reference parameter is, the smaller the predicted amount of the historical failure time data is.

[0091] Wherein, in some embodiments, reference Figure 3 As shown in the figure, this figure is a schematic diagram of a process for determining a failure prediction time shown in some embodiments of the present application. The time when the failure mode of the oil pipeline sealing structure to be evaluated occurs is predicted by the failure prediction parameter list, and the failure prediction time corresponding to the failure mode can be obtained by adopting the following steps:

[0092] First, in step 1041, historical failure time data corresponding to the failure mode is obtained;

[0093] Then, in step 1042, the last historical expiration time in the historical expiration time data is selected;

[0094] Secondly, in step 1043, the matching gain coefficient of the last historical failure time in the historical failure time data is determined through the failure prediction parameter column;

[0095] Finally, in step 1044, the time when the failure mode of the oil pipeline sealing structure to be evaluated will occur is predicted based on the matching gain coefficient and the failure prediction parameter list to obtain the failure prediction time corresponding to the failure mode.

[0096] In some embodiments, determining the matching gain coefficient of the last historical failure time in the historical failure time data through the failure prediction parameter column can be implemented by the following steps:

[0097] Obtain the failure time potential component corresponding to the last historical failure time in the historical failure time data;

[0098] The matching gain coefficient of the last historical failure time in the historical failure time data is determined according to the failure time potential component corresponding to the last historical failure time and the failure prediction parameter column.

[0099] In specific implementation, determining the matching gain coefficient of the last historical failure time in the historical failure time data based on the failure time potential component corresponding to the last historical failure time and the failure prediction parameter column can be achieved in the following manner, namely: first, using the product of the number of sampling moments of the failure time potential component corresponding to the last historical failure time and the time prediction coefficient in the failure prediction parameter column as the negative exponential function power of the natural constant e, and then using the obtained result as the matching gain coefficient of the last historical failure time. In other embodiments, it can also be determined by other methods, which are not limited here.

[0100] It should be noted that the matching gain coefficient is used to control the growth rate of the failure prediction time. The larger the matching gain coefficient is, the higher the growth rate of the failure prediction time is, and the smaller the matching gain coefficient is, the lower the growth rate of the failure prediction time is. When the number of sampling moments corresponding to the historical failure time is small, the value of the matching gain coefficient approaches 1, thereby increasing the growth rate of the failure prediction time. When the number of sampling moments corresponding to the historical failure time is large, the value of the matching gain coefficient approaches 0, thereby reducing the growth rate of the failure prediction time. The present application adjusts the growth rate of the failure prediction time through the matching gain coefficient, so that the change of the failure prediction time is more in line with the actual situation.

[0101] It should be noted that each historical failure time in the historical failure time data has a unique corresponding sampling moment, and each historical failure time in the historical failure time data is sorted according to the chronological order of the corresponding sampling moments. Therefore, in some embodiments, the time when the failure mode of the oil pipeline sealing structure to be evaluated occurs is predicted according to the matching gain coefficient and the failure prediction parameter column, and the failure prediction time corresponding to the failure mode can be obtained by adopting the following steps:

[0102] Obtaining the time prediction coefficient and time reference parameter in the failure prediction parameter column;

[0103] Obtaining the failure time potential component corresponding to the historical failure time at the first sampling moment in the historical failure time data;

[0104] According to the time prediction coefficient and time reference parameter in the failure prediction parameter column, the matching gain coefficient and the failure time potential component of the first sampling moment in the historical failure time data corresponding to the historical failure time, the time when the failure mode of the oil pipeline sealing structure to be evaluated will be predicted to obtain the failure prediction time corresponding to the failure mode.

[0105] It should be noted that the failure prediction time is the predicted value of the last historical failure time at the next sampling moment; as a preferred embodiment, the time when the failure mode of the oil pipeline sealing structure to be evaluated occurs is predicted according to the time prediction coefficient and time reference parameter in the failure prediction parameter column, the matching gain coefficient and the failure time potential component of the historical failure time corresponding to the first sampling moment in the historical failure time data, and the failure prediction time corresponding to the failure mode can be obtained in the following manner, namely: the result of [the failure time potential component of the historical failure time corresponding to the first sampling moment in the historical failure time data - (time reference parameter / time prediction coefficient)] * the response gain coefficient + (time reference parameter / time prediction coefficient) is used as the failure prediction time of the occurrence of this failure mode. In other embodiments, prediction can also be performed by other methods, which are not limited here.

[0106] In some embodiments, determining the failure confidence cost under this failure mode based on the failure time distribution entropy can be implemented by using the following steps:

[0107] Performing maximum likelihood analysis on the failure time distribution entropy to obtain a failure parameter column;

[0108] The failure confidence cost under this failure mode is determined by the failure parameter column.

[0109] It should be noted that the maximum likelihood conversion, i.e., finding the parameters that must be satisfied when the probability density of failure occurring under a given historical failure time is maximized under the failure time distribution entropy, can be implemented in the following way, i.e., first, taking the logarithm of the failure time distribution entropy; secondly, taking the partial derivative of the result obtained with respect to the time influence parameter, setting the result obtained equal to 0, and obtaining the time influence parameter estimation equation; again, taking the partial derivative of the result obtained by taking the logarithm of the failure time distribution entropy with respect to the time expansion parameter, setting the result obtained equal to 0, and obtaining the time expansion parameter estimation equation; then, solving the time influence parameter estimation equation and the time expansion parameter estimation equation jointly to obtain the specific values ​​of the time influence parameter and the time expansion parameter; finally, taking the set consisting of the time influence parameter and the time expansion parameter as the failure parameter column; in other embodiments, it can also be determined by other methods, which will not be repeated here.

[0110] In specific implementation, determining the failure confidence cost under this failure mode through the failure parameter column can be achieved in the following manner, namely: first, obtaining the first historical failure time in the historical failure time data under this failure mode, and then comparing the first historical failure time with the time expansion parameter in the failure parameter column, using the time influence parameter in the failure parameter column as the index of the comparison result, and using the result obtained by the above calculation as the exponent of the natural constant e, so that the obtained result is used as the failure confidence cost under this failure mode. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0111] It should be noted that the failure confidence cost reflects the influence of the first historical failure time of the sealing structure on the overall reliability of the sealing structure. The larger the failure confidence cost, the higher the influence of the first historical failure time of the sealing structure on the overall reliability of the sealing structure. The smaller the failure confidence cost, the lower the influence of the first historical failure time of the sealing structure on the overall reliability of the sealing structure.

[0112] In step 104, the failure rate of the failure mode at the failure prediction time is determined, and then the failure rate is compensated by the failure confidence cost of the failure mode to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the failure mode. The above steps are repeated to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the remaining failure modes.

[0113] In some embodiments, determining the failure rate of the failure mode at the failure prediction time may be achieved by using the following steps:

[0114] Obtain the failure parameter column of this failure mode;

[0115] The failure rate of this failure mode at the failure prediction time is determined based on the failure prediction time of this failure mode and the corresponding failure parameter column.

[0116] It should be noted that the failure rate characterizes the probability of the corresponding failure mode occurring in the sealing structure at the failure prediction time. The larger the failure rate, the higher the probability of the corresponding failure mode occurring in the sealing structure at the failure prediction time. The smaller the failure rate, the lower the probability of the corresponding failure mode occurring in the sealing structure at the failure prediction time. As a preferred embodiment, determining the failure rate of this failure mode at the failure prediction time based on the failure prediction time of this failure mode and the corresponding failure parameter column can be achieved in the following manner, namely: first, after comparing the failure prediction time with the time expansion parameter in the failure parameter column, the time influence parameter in the failure parameter column is used as the exponent of the comparison result, and the result obtained by the above calculation is negative and used as the exponent of the natural constant e, so that the obtained result is used as the failure rate of this failure mode at the failure prediction time.

[0117] In some embodiments, the failure rate is compensated by the failure confidence cost of the failure mode, and the failure confidence of the oil pipeline sealing structure to be evaluated under the failure mode can be obtained by the following steps:

[0118] Compensating the failure rate corresponding to the failure mode according to the failure confidence cost of the failure mode to obtain the compensated failure rate of the failure mode;

[0119] The failure confidence of the petroleum pipeline sealing structure to be evaluated under this failure mode is determined by the compensation failure rate.

[0120] It should be noted that when the sealing structure fails, the failure time does not start from 0. Therefore, the present application considers the impact of the first failure time of the sealing structure on the overall failure rate of the sealing structure, that is, cost compensation; as a preferred embodiment, the failure rate corresponding to this failure mode is compensated according to the failure confidence cost of this failure mode, and the compensated failure rate of this failure mode can be achieved in the following way, that is, first, multiply the failure rate corresponding to this mode by the failure confidence cost corresponding to this failure mode, and then use the result as the compensated failure rate of this failure mode. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0121] It should be noted that, in the present application, the failure confidence is obtained by subtracting the compensation failure rate from 1, that is, the credibility of the sealing structure is obtained by subtracting the failure rate of the sealing structure from 100%. Therefore, the failure confidence characterizes the credibility of the sealing structure in the occurrence of this failure mode at the corresponding failure prediction time, that is, reliability. The higher the failure confidence, the higher the credibility of the sealing structure in the occurrence of this failure mode at the corresponding failure prediction time. The lower the failure confidence, the lower the credibility of the sealing structure in the occurrence of this failure mode at the corresponding failure prediction time. As a preferred embodiment, the failure confidence of the oil pipeline sealing structure to be evaluated in this failure mode can be determined by the compensation failure rate in the following manner, that is, the result of subtracting the compensation failure rate from the natural constant 1 is used as the failure confidence of the oil pipeline sealing structure to be evaluated in this failure mode. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0122] In step 105, the reliability of the petroleum pipeline sealing structure to be evaluated is preliminarily evaluated based on the failure confidence levels under various failure modes.

[0123] In some embodiments, the following steps may be used to pre-evaluate the reliability of the oil pipeline sealing structure to be evaluated based on the failure confidence levels under various failure modes:

[0124] Preset reliability threshold of sealing structure;

[0125] Select a failure mode of the sealing structure of the oil pipeline;

[0126] When the failure confidence in this failure mode is greater than or equal to the reliability threshold, it is determined that this failure mode will not occur in the failure prediction time under this failure mode;

[0127] When the failure confidence in this failure mode is less than the reliability threshold, it is determined that this failure mode will occur at the failure prediction time in this failure mode, and at this time, an alarm is sent to the management center and the corresponding failure mode is recorded;

[0128] Repeat the above steps to conduct a preliminary assessment of the reliability of the remaining failure modes of the petroleum pipeline sealing structure to be assessed.

[0129] It should be noted that the present application determines the reliability threshold through the normal distribution model in the prior art after setting the failure confidence of the oil pipeline sealing structure to be evaluated to satisfy the standard normal distribution. As a preferred embodiment, first, a reasonable confidence interval is determined through the normal distribution model, such as a 95% distribution interval, and then the failure confidence corresponding to the right endpoint of the confidence interval is used as the reliability threshold. In other embodiments, it can also be determined by other methods, which are not limited here.

[0130] In addition, in another aspect of the present application, in some embodiments, the present application provides a reliability assessment system for a petroleum pipeline sealing structure, referring to Figure 4 , which is a structural block diagram of a reliability evaluation system for a petroleum pipeline sealing structure according to some embodiments of the present application. The reliability evaluation system 200 for a petroleum pipeline sealing structure includes: an acquisition module 201, a processing module 202 and an execution module 203, which are described as follows:

[0131] Acquisition module 201, in this application, acquisition module 201 is mainly used to acquire historical failure time data of the oil pipeline sealing structure to be evaluated under different failure modes;

[0132] Processing module 202, in this application, processing module 202 is mainly used to select a failure mode, and determine the trend characteristics and failure time distribution entropy of the corresponding failure time according to the historical failure time data under the failure mode;

[0133] In addition, the processing module 202 in the present application is also used to predict the time when the failure mode of the oil pipeline sealing structure to be evaluated occurs through the trend characteristics, obtain the failure prediction time corresponding to the failure mode, and determine the failure confidence cost under the failure mode based on the failure time distribution entropy;

[0134] In addition, the processing module 202 in the present application is also used to determine the failure rate of the failure mode at the failure prediction time, and then compensate the failure rate by the failure confidence cost of the failure mode to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the failure mode, and repeat the above steps to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the remaining failure modes;

[0135] The execution module 203 in this application is mainly used to pre-evaluate the reliability of the oil pipeline sealing structure to be evaluated based on the failure confidence level under various failure modes.

[0136] The above describes in detail an example of a reliability assessment method for a petroleum pipeline sealing structure provided by an embodiment of the present application. It can be understood that, in order to realize the above functions, the corresponding device includes a hardware structure and / or software module corresponding to each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0137] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned reliability assessment method of the oil pipeline sealing structure.

[0138] In some embodiments, reference Figure 5 , which is an internal structure diagram of a computer device according to some embodiments of the present application using a reliability evaluation method for a petroleum pipeline sealing structure. The reliability evaluation method for a petroleum pipeline sealing structure in the above embodiments can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0139] The processor 301 may be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more processors for controlling the execution of the reliability assessment method of the oil pipeline sealing structure in the present application.

[0140] The communication bus 302 is used to transmit information between the above components.

[0141] The memory 303 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0142] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The reliability assessment method of the oil pipeline sealing structure in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0143] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0144] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0145] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0146] In addition, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the above-mentioned reliability assessment method of the oil pipeline sealing structure is implemented when the computer executes them.

[0147] In summary, the reliability assessment method and system of the oil pipeline sealing structure disclosed in the embodiment of the present application are as follows: by obtaining the historical failure time data of the oil pipeline sealing structure to be assessed under different failure modes; selecting a failure mode, and determining the trend characteristics and failure time distribution entropy of the corresponding failure time according to the historical failure time data under the failure mode; predicting the time when the failure mode of the oil pipeline sealing structure to be assessed occurs by the trend characteristics, obtaining the failure prediction time corresponding to the failure mode, and determining the failure confidence cost under the failure mode based on the failure time distribution entropy; determining the failure rate of the failure mode at the failure prediction time, and then compensating the failure rate by the failure confidence cost of the failure mode, obtaining the failure confidence of the oil pipeline sealing structure to be assessed under the failure mode, repeating the above steps, and obtaining the failure confidence of the oil pipeline sealing structure to be assessed under the remaining failure modes; pre-assessing the reliability of the oil pipeline sealing structure to be assessed based on the failure confidence under various failure modes; avoiding the influence of coupling on the reliability assessment of the oil pipeline sealing structure, thereby improving the accuracy of the reliability assessment of the sealing structure.

[0148] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0149] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A reliability assessment method for a petroleum pipeline sealing structure, characterized in that: The steps include: Obtain historical failure time data of the oil pipeline sealing structure to be evaluated under different failure modes; Select a failure mode, and determine the corresponding failure time trend characteristics and failure time distribution entropy based on the historical failure time data under the failure mode; The time when the failure mode of the oil pipeline sealing structure to be evaluated occurs is predicted by using the trend characteristics to obtain the failure prediction time corresponding to the failure mode, and the failure confidence cost under the failure mode is determined based on the failure time distribution entropy; Determine the failure rate of the failure mode at the failure prediction time, and then compensate the failure rate by the failure confidence cost of the failure mode to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the failure mode, repeat the above steps to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the remaining failure modes; The reliability of the oil pipeline sealing structure to be evaluated is pre-evaluated based on the failure confidence level under various failure modes.

2. The method according to claim 1, characterized in that Determining the trend characteristics of the corresponding failure time and the failure time distribution entropy based on the historical failure time data under this failure mode specifically includes: Determine the trend component corresponding to each historical failure time in the historical failure time data under the failure mode, and combine all trend components into the trend characteristics of the failure time corresponding to the historical failure time data under the failure mode; Determine the failure probability density of each historical failure time in the historical failure time data under the failure mode; The failure time distribution entropy corresponding to the historical failure time data under this failure mode is determined through all failure probability densities.

3. The method according to claim 1, characterized in that The time when the failure mode of the oil pipeline sealing structure to be evaluated occurs is predicted by using the trend characteristics, and the failure prediction time corresponding to the failure mode is obtained, which specifically includes: Determine the failure time potential component of the historical failure time data at each sampling moment through the trend characteristics; Performing linear changes on all failure time potential components to obtain a linear trend sequence of the historical failure time data; Determine a failure prediction parameter column according to the historical failure time data and the linear trend sequence; The failure prediction parameter list is used to predict the time when the failure mode of the oil pipeline sealing structure to be evaluated will occur, so as to obtain the failure prediction time corresponding to the failure mode.

4. The method according to claim 1, characterized in that Determining the failure confidence cost under this failure mode based on the failure time distribution entropy specifically includes: Performing maximum likelihood analysis on the failure time distribution entropy to obtain a failure parameter column; The failure confidence cost under this failure mode is determined by the failure parameter column.

5. The method according to claim 1, characterized in that Determining the failure rate of the failure mode at the failure prediction time specifically includes: Obtain the failure parameter column of this failure mode; The failure rate of this failure mode at the failure prediction time is determined based on the failure prediction time of this failure mode and the corresponding failure parameter column.

6. The method according to claim 1, characterized in that The failure rate is compensated by the failure confidence cost of the failure mode, and the failure confidence of the oil pipeline sealing structure to be evaluated under the failure mode is obtained, which specifically includes: Compensating the failure rate corresponding to the failure mode according to the failure confidence cost of the failure mode to obtain the compensated failure rate of the failure mode; The failure confidence of the petroleum pipeline sealing structure to be evaluated under this failure mode is determined by the compensation failure rate.

7. The method according to claim 1, characterized in that The reliability of the oil pipeline sealing structure to be evaluated is pre-evaluated based on the failure confidence level under various failure modes, including: Preset reliability threshold of sealing structure; Select a failure mode of the sealing structure of the oil pipeline; When the failure confidence in this failure mode is greater than or equal to the reliability threshold, it is determined that this failure mode will not occur in the failure prediction time under this failure mode; When the failure confidence in this failure mode is less than the reliability threshold, it is determined that this failure mode will occur at the failure prediction time in this failure mode, and at this time, an alarm is sent to the management center and the corresponding failure mode is recorded; Repeat the above steps to conduct a preliminary assessment of the reliability of the remaining failure modes of the petroleum pipeline sealing structure to be assessed.

8. A reliability assessment system for a petroleum pipeline sealing structure, characterized in that: The reliability evaluation system of the oil pipeline sealing structure comprises: An acquisition module is used to acquire historical failure time data of the oil pipeline sealing structure to be evaluated under different failure modes; A processing module is used to select a failure mode and determine the trend characteristics and failure time distribution entropy of the corresponding failure time according to the historical failure time data under the failure mode; The processing module is further used to predict the time when the failure mode of the oil pipeline sealing structure to be evaluated will occur based on the trend characteristics, obtain the failure prediction time corresponding to the failure mode, and determine the failure confidence cost under the failure mode based on the failure time distribution entropy; The processing module is further used to determine the failure rate of the failure mode at the failure prediction time, and then compensate the failure rate by the failure confidence cost of the failure mode to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the failure mode, and repeat the above steps to obtain the failure confidence of the oil pipeline sealing structure to be evaluated in the remaining failure modes; The execution module is used to pre-evaluate the reliability of the sealing structure of the oil pipeline to be evaluated based on the failure confidence under various failure modes.

9. A computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, wherein: The processor is used to call and run the computer program from the memory, so that the computer device executes the reliability assessment method of the petroleum pipeline sealing structure according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein instructions or codes are stored in the computer-readable storage medium, characterized in that: When the instructions or codes are executed on a computer, the reliability assessment method for a petroleum pipeline sealing structure according to any one of claims 1 to 7 is implemented by the computer.

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