A method for predicting the service life of buried polyethylene gas pipelines based on mechanical properties

Abstract

The present invention relates to a method for predicting the service life of buried gas polyethylene pipelines based on mechanical properties, belonging to the technical field of pipeline service life prediction. Among them, the method includes obtaining a creep-aged polyethylene specimen from a gas polyethylene pipe through a hydrostatic test and obtaining a polyethylene ultrasonic pulse signal through ultrasonic detection, and then statistically extracting polyethylene ultrasonic spectrum coefficient parameters from the ultrasonic signal; obtaining polyethylene mechanical property parameters from the creep-aged polyethylene specimen through mechanical testing and constructing a polyethylene pipeline creep-aging data set in combination with the polyethylene ultrasonic spectrum coefficient parameters, and then obtaining an ultrasonic non-destructive mechanical property prediction model through non-linear modeling; obtaining a real-time polyethylene ultrasonic pulse signal and obtaining real-time polyethylene mechanical property parameters through the ultrasonic non-destructive mechanical property prediction model, and then obtaining the remaining service life of the gas polyethylene pipeline through a polyethylene mechanical degradation model, so as to ensure the safe operation of in-service gas polyethylene pipelines.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipeline life prediction, and particularly relates to a method for predicting the life of buried gas polyethylene pipelines based on mechanical properties. Background Technique

[0002] Due to advantages such as strong corrosion resistance, good machining performance, and long service life, polyethylene pipelines are widely used in natural gas transportation. However, polyethylene gas pipelines are buried underground for a long time and continuously subjected to various factors such as soil pressure, thermo-oxidative aging, and internal loads. As the service time increases, the pipe material will gradually undergo creep aging, resulting in a decline in mechanical properties and ultimately losing its use value. To ensure the structural and functional integrity of buried gas polyethylene pipelines throughout their life cycle and achieve the goal of pipeline inherent safety, it is extremely urgent to carry out research on the life prediction of buried gas polyethylene pipelines.

[0003] However, most of the existing life prediction models for buried gas polyethylene pipelines are constructed based on samples obtained under single-variable control, without fully considering the complex and variable factors faced by pipelines during actual operation, resulting in deviations between the prediction results and the actual situation; at the same time, due to the hidden characteristics of buried pipelines deep underground, it is extremely difficult to obtain their status data. Facing these limited and messy data, traditional data processing technologies are difficult to effectively extract the information that truly reflects the key status of pipelines from them, greatly restricting the accuracy of life prediction; in addition, the current exploration of the aging mechanism of polyethylene pipe materials is still shallow, especially for creep aging caused by internal pressure, which is the main aging form of buried gas polyethylene pipe materials. There is a lack of relevant life evaluation research, and it is difficult to achieve non-destructive detection for buried gas pipelines.

[0004] Therefore, it is urgent to further study the life prediction model of buried gas polyethylene pipelines to improve the accuracy and reliability of prediction and provide a strong guarantee for the safe operation of gas pipelines. Summary of the Invention

[0005] To solve the above problems existing in the prior art, the present invention proposes a method for predicting the life of buried gas polyethylene pipelines based on mechanical properties.

[0006] The object of the present invention can be achieved by the following technical solutions:

[0007] A method for predicting the life of buried gas polyethylene pipelines based on mechanical properties, comprising:

[0008] Obtain a creep-aged polyethylene specimen, obtain a polyethylene ultrasonic pulse signal through ultrasonic detection according to the creep-aged polyethylene specimen, and obtain a polyethylene ultrasonic spectrum coefficient parameter through ultrasonic signal statistics extraction according to the polyethylene ultrasonic pulse signal;

[0009] Obtain the polyethylene mechanical property parameters from the creep-aged polyethylene specimen through mechanical testing, and construct a polyethylene pipeline creep-aging dataset based on the polyethylene mechanical property parameters and the polyethylene ultrasonic spectrum coefficient parameters;

[0010] Obtain an ultrasonic non-destructive mechanical property prediction model through non-linear modeling based on the polyethylene pipeline creep-aging dataset;

[0011] Obtain the real-time polyethylene ultrasonic pulse signal, obtain the real-time polyethylene mechanical property parameters through the ultrasonic non-destructive mechanical property prediction model based on the real-time polyethylene ultrasonic pulse signal, and obtain the remaining life of the gas polyethylene pipeline through the polyethylene mechanical degradation model based on the real-time polyethylene mechanical property parameters.

[0012] Preferably, the extraction of the polyethylene ultrasonic spectrum coefficient parameters includes:

[0013] Obtain the polyethylene ultrasonic pulse signal from the creep-aged polyethylene specimen through the single-probe pulse reflection echo method;

[0014] Sample the polyethylene ultrasonic pulse signal through an analog-to-digital converter to obtain a discrete polyethylene ultrasonic pulse signal sequence;

[0015] Decompose the discrete polyethylene ultrasonic pulse signal sequence through lifting wavelet transform to obtain the polyethylene pulse signal detail coefficients and the polyethylene pulse signal approximation coefficients;

[0016] Obtain the polyethylene ultrasonic pulse denoising signal through reconstruction error compensation based on the polyethylene pulse signal detail coefficients and the polyethylene pulse signal approximation coefficients;

[0017] Obtain the polyethylene ultrasonic spectrum through ultrasonic spectrum analysis based on the polyethylene ultrasonic pulse denoising signal;

[0018] Obtain the ultrasonic spectrum coefficient parameters through ultrasonic spectrum statistics extraction based on the polyethylene ultrasonic spectrum.

[0019] Preferably, the mathematical expression of the polyethylene ultrasonic pulse denoising signal is:

[0020] ,

[0021] where S(n) is the polyethylene ultrasonic pulse denoising signal, S0(n) is the original polyethylene ultrasonic pulse signal, S1(n) is the polyethylene ultrasonic pulse reconstruction signal, n is the discrete index of the polyethylene ultrasonic pulse signal, and α is the reconstruction error compensation coefficient.

[0022] Preferably, the ultrasonic non-destructive mechanical property prediction model includes:

[0023] Receive the polyethylene pipe creep aging data set through the input layer;

[0024] Obtain the polyethylene ultrasonic spectrum coefficient parameter sequence features through the feature extraction layer according to the polyethylene pipe creep aging data set;

[0025] Output the predicted value of the polyethylene mechanical property parameters through the output layer according to the polyethylene ultrasonic spectrum coefficient parameter sequence features, and the output layer is a fully connected layer with three neurons.

[0026] Preferably, the calculation steps for the remaining life of the gas polyethylene pipe include:

[0027] Preset the polyethylene mechanical property failure criterion coefficient;

[0028] Construct a polyethylene mechanical degradation model through comprehensive degradation according to the polyethylene mechanical property failure criterion coefficient and the polyethylene mechanical property parameters;

[0029] Preset the polyethylene mechanical multi-dimensional failure comprehensive index, and obtain the remaining life of the gas polyethylene pipe through the polyethylene mechanical degradation model according to the polyethylene mechanical multi-dimensional failure comprehensive index and the real-time polyethylene mechanical property parameters.

[0030] Preferably, the mathematical expression of the polyethylene mechanical degradation model is:

[0031] ,

[0032] where D(t) is the polyethylene mechanical degradation index at time t, A1 is the critical threshold of elongation at break, B1 is the critical threshold of tensile yield stress, C1 is the critical threshold of impact strength, β1 is the weight coefficient of elongation at break, β2 is the weight coefficient of tensile yield stress, β3 is the weight coefficient of impact strength, A(t) is the elongation at break degradation model, B(t) is the tensile yield stress degradation model, C(t) is the impact strength degradation model respectively, A0 is the initial value of elongation at break, B0 is the initial value of tensile yield stress, and C0 is the initial value of impact strength.

[0033] Preferably, obtaining the remaining life of the gas polyethylene pipe through the polyethylene mechanical degradation model includes:

[0034] Back-calculate the value at time t through the polyethylene mechanical degradation index according to the polyethylene mechanical multi-dimensional failure comprehensive index;

[0035] Calculate the remaining life of the gas polyethylene pipe through the current time value according to the value at time t and the real-time polyethylene mechanical property parameters.

[0036] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method for predicting the service life of buried gas polyethylene pipelines based on mechanical properties is implemented.

[0037] A storage medium containing computer-executable instructions, which are used to execute the above-mentioned method for predicting the service life of buried gas polyethylene pipelines based on mechanical properties when executed by a computer processor.

[0038] The beneficial effects of the present invention are as follows:

[0039] (1) By fully considering the combined effects of complex factors such as moisture, heat, oxygen, and pressure, a scientific prediction of the remaining life of buried polyethylene pipelines is carried out, providing an effective evaluation method for accurately assessing the creep aging problem of in-service polyethylene pipelines caused by pressure and the resulting life loss.

[0040] (2) By means of analog-to-digital conversion sampling, lifting wavelet transform decomposition, and reconstruction error compensation, the noise in the polyethylene ultrasonic pulse signal is effectively removed, reducing the information loss in the reconstruction process during denoising processing, providing higher-quality data for subsequent ultrasonic spectrum analysis, and thus improving the accuracy of mechanical property prediction.

[0041] (3) By constructing a polyethylene pipeline creep aging dataset through polyethylene mechanical property parameters and polyethylene ultrasonic spectrum coefficient parameters, and then obtaining an ultrasonic non-destructive mechanical property prediction model through non-linear modeling, the real-time prediction of accurate polyethylene mechanical property parameters is achieved without damaging the polyethylene pipe material.

[0042] (4) By comprehensively constructing a polyethylene mechanical degradation model, considering the degradation of multiple mechanical property parameters, a more comprehensive evaluation of mechanical property degradation is provided. Allowing the setting of critical thresholds according to specific application scenarios and requirements, enhancing the adaptability and flexibility of the model, introducing weight coefficients to adjust the importance of different mechanical property parameters in the degradation model, making the model more in line with the actual degradation law, and being able to accurately predict the remaining life of gas polyethylene pipelines based on real-time mechanical property parameters, improving the safety and reliability of the operation of polyethylene pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0044] Figure 1 It is a schematic flow chart of a method for predicting the service life of buried gas polyethylene pipelines based on mechanical properties of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0045] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and their effects according to the present invention as follows.

[0046] Please refer to Figure 1 , a method for predicting the service life of buried gas polyethylene pipelines based on mechanical properties, including:

[0047] S1: Obtain creep-aged polyethylene specimens from gas polyethylene pipes through a hydrostatic test;

[0048] S2: Obtain polyethylene ultrasonic pulse signals from the creep-aged polyethylene specimens through ultrasonic detection, and extract polyethylene ultrasonic spectrum coefficient parameters from the polyethylene ultrasonic pulse signals through ultrasonic signal statistics;

[0049] S3: Obtain polyethylene mechanical property parameters from the creep-aged polyethylene specimens through mechanical testing, and construct a polyethylene pipeline creep-aging data set based on the polyethylene mechanical property parameters and the polyethylene ultrasonic spectrum coefficient parameters;

[0050] S4: Obtain an ultrasonic non-destructive mechanical property prediction model through non-linear modeling based on the polyethylene pipeline creep-aging data set;

[0051] S5: Obtain real-time polyethylene ultrasonic pulse signals, obtain real-time polyethylene mechanical property parameters through the ultrasonic non-destructive mechanical property prediction model based on the real-time polyethylene ultrasonic pulse signals, and obtain the remaining service life of the gas polyethylene pipeline through a polyethylene mechanical degradation model based on the real-time polyethylene mechanical property parameters.

[0052] In this embodiment, the step of obtaining creep-aged polyethylene specimens from gas polyethylene pipes through a hydrostatic test is specifically implemented through the following steps:

[0053] The gas polyethylene pipes are PE100 grade pipes of the same manufacturer and batch, with a pipe diameter of DN110×10mm and a pipe length of 650mm. The hydrostatic test is used to prepare creep-aged polyethylene specimens through a hydrostatic test machine;

[0054] Specifically, by setting three temperature gradients of 90°C, 60°C, and 30°C, at each of the three temperature gradients, 30 observations distributed at 10 pressure levels are obtained, and at each pressure level, there are 3 observation records; the interval distribution of the test data in terms of failure time is achieved by reasonably selecting the test hoop stress;

[0055] The selection of the said pressure level follows the following principles: the failure times of at least 4 observations exceed 7000 hours, and the failure time of at least 1 observation exceeds 9000 hours; at any temperature, the observations with failure times within 10 hours are discarded; at 30°C, an observation with a failure time between 100 - 1000 hours is selected.

[0056] The determination of the maximum test hoop stress follows the following principles: at 90°C and 60°C, the minimum internal pressure that makes the test failure time exceed 10h is the upper limit of the test internal pressure; at 30°C, the minimum pressure that makes the failure time exceed 100h is the upper limit of the test internal pressure, and the said maximum test hoop stress can be determined in advance through early preliminary tests.

[0057] During the hydrostatic test, the said other test hoop stresses are dynamically adjusted according to the test temperature and in combination with the GB / T15558.2 - 2023 standard. Specifically, a minimum test hoop stress is estimated. Generally, the higher the temperature, the smaller the difference between the maximum test hoop stress and the minimum test hoop stress. Then, a series of hoop stresses are selected between the two hoop stresses, and tests are carried out at the corresponding pressures. As the test progresses, the test pressure is continuously corrected according to the test requirements and data are supplemented. When the polyethylene pipe ruptures or the outer diameter deformation of the polyethylene pipe reaches 100%, it is regarded as the index for the polyethylene pipe to lose its service performance, and the test is stopped at this time, so as to systematically obtain creep - aged polyethylene specimens for subsequent analysis and testing.

[0058] It should be noted that the mathematical expression of the said test pressure is where, is the test pressure, is the hoop stress, is the wall thickness of the pipe;

[0059] According to the time - temperature equivalence principle, the hydrostatic test accelerates the aging process of polyethylene pipes by increasing the temperature and the hoop stress. Under the test condition of 30°C, on - line real - time detection is adopted, that is, the ultrasonic probe is fixed at the position of the pipe sample for detection, data are automatically collected and saved locally, and the collected data are sent to the remote end within a certain period; the ultrasonic monitoring workstation works continuously and cannot lose power, otherwise it may cause the loss of unsaved data; for the samples under the test conditions of 90°C and 60°C, the regular manual sampling and detection method is adopted to obtain data through a high - temperature ultrasonic probe. When testing, the sample is taken out under pressure and suspended on the hanger, and the polyethylene ultrasonic pulse signal can be collected only after the sample is stable.

[0060] In this embodiment, the polyethylene ultrasonic pulse signal is obtained through ultrasonic detection according to the said creep - aged polyethylene specimen, and the polyethylene ultrasonic spectrum coefficient parameters are statistically extracted according to the said polyethylene ultrasonic pulse signal, which are specifically implemented through the following steps:

[0061] S201: Obtain the polyethylene ultrasonic pulse signal by the single-probe pulse reflection echo method based on the creep-aged polyethylene specimen;

[0062] It should be noted that during the acquisition of the polyethylene ultrasonic pulse signal, to ensure the comprehensiveness and accuracy of the data, no less than 200 groups of polyethylene ultrasonic pulse signals under various aging states need to be acquired.

[0063] S202: Obtain the denoised polyethylene ultrasonic pulse signal through signal denoising preprocessing based on the polyethylene ultrasonic pulse signal;

[0064] S202-1: Sample the polyethylene ultrasonic pulse signal through an analog-to-digital converter to obtain a discrete polyethylene ultrasonic pulse signal sequence;

[0065] S202-2: Decompose the discrete polyethylene ultrasonic pulse signal sequence through lifting wavelet transform to obtain the detail coefficients of the polyethylene pulse signal and the approximation coefficients of the polyethylene pulse signal;

[0066] S202-3: Obtain the denoised polyethylene ultrasonic pulse signal through reconstruction error compensation based on the detail coefficients of the polyethylene pulse signal and the approximation coefficients of the polyethylene pulse signal;

[0067] Specifically, remove the noise through adaptive threshold segmentation based on the detail coefficients of the polyethylene pulse signal and the approximation coefficients of the polyethylene pulse signal, perform signal reconstruction on the coefficients after threshold processing through inverse discrete wavelet transform to obtain the reconstructed polyethylene ultrasonic pulse signal, and obtain the denoised polyethylene ultrasonic pulse signal through reconstruction error compensation based on the reconstructed polyethylene ultrasonic pulse signal. The mathematical expression of the denoised polyethylene ultrasonic pulse signal is:

[0068] ,

[0069] where S(n) is the denoised polyethylene ultrasonic pulse signal, S0(n) is the original polyethylene ultrasonic pulse signal, S1(n) is the reconstructed polyethylene ultrasonic pulse signal, n is the discrete index of the polyethylene ultrasonic pulse signal, and α is the reconstruction error compensation coefficient.

[0070] S202-4: Obtain the polyethylene ultrasonic spectrum through ultrasonic spectrum analysis based on the denoised polyethylene ultrasonic pulse signal;

[0071] S202-5: Extract the ultrasonic spectrum coefficient parameters through ultrasonic spectrum statistics based on the polyethylene ultrasonic spectrum. The ultrasonic spectrum coefficient parameters include the ultrasonic attenuation spectrum, the ultrasonic phase spectrum, the ultrasonic sound velocity spectrum, and the ultrasonic reflection coefficient phase spectrum.

[0072] Specifically, according to the polyethylene ultrasonic spectrum, the time-domain signal is converted into a frequency-domain signal through fast Fourier transform to obtain the polyethylene ultrasonic spectrum, and ultrasonic spectrum coefficient parameters are extracted from the polyethylene ultrasonic spectrum.

[0073] In this embodiment, obtaining the polyethylene mechanical property parameters by mechanical testing on the creep-aged polyethylene specimens, and constructing the polyethylene pipeline creep aging data set according to the polyethylene mechanical property parameters and the polyethylene ultrasonic spectrum coefficient parameters are specifically implemented through the following steps:

[0074] The mechanical testing includes obtaining the polyethylene mechanical property parameters through tensile tests and impact tests. The polyethylene mechanical property parameters include elongation at break, tensile yield stress, and impact strength.

[0075] It should be noted that the elongation at break is the maximum elongation that polyethylene can withstand before fracture, the tensile yield stress is the critical point stress at which polyethylene transitions from elastic deformation to plastic deformation during the tensile process, and the impact strength is the ability of polyethylene to resist fracture under the action of impact loads.

[0076] Specifically, for the tensile test, specimens are prepared from the middle part of the aged pipe sample by mechanical processing. For the impact test, samples are axially taken from the center of the aged pipe sample. The length of the sample strip is 100 mm, the width is 10 mm, and the thickness is the wall thickness of the pipe. Sample strips are evenly taken along the circumference of the pipe section, and a single-edge notch with a depth of 2 mm is prepared at the center. The failure type of the sample and the impact strength should be recorded during the test.

[0077] According to the polyethylene mechanical property parameters and the polyethylene ultrasonic spectrum coefficient parameters, a polyethylene pipeline creep aging data set is constructed through the corresponding relationship of each aging degree.

[0078] In this embodiment, obtaining the ultrasonic non-destructive mechanical property prediction model by non-linear modeling according to the polyethylene pipeline creep aging data set is specifically implemented through the following steps:

[0079] The ultrasonic non-destructive mechanical property prediction model receives the polyethylene ultrasonic spectrum coefficient parameters in the polyethylene pipeline creep aging data set through an input layer, extracts the primary features of the polyethylene ultrasonic spectrum coefficient parameters through a convolutional layer with a convolutional kernel size of 3×3, and reduces the feature dimension through a max-pooling layer with a pooling window size of 2×2. After repeating the convolution and pooling operations twice, a fully connected layer is connected to flatten the features, and the sequence features of the polyethylene ultrasonic spectrum coefficient parameters are extracted through an LSTM layer with 100 hidden units and a fully connected layer. Finally, the predicted values of the polyethylene mechanical property parameters are output through a fully connected layer with three neurons.

[0080] Specifically, the ultrasonic non-destructive mechanical property prediction model uses the mean squared error as the loss function to measure the difference between the model prediction value and the actual value. The initial learning rate is set to 0.001 to provide an initial step size for updating the model parameters. The Adam optimizer is used to optimize the learning rate, and its adaptive learning rate adjustment mechanism is utilized to improve the training efficiency and stability. L2 regularization is introduced, and the weight decay coefficient is set to 0.01 to prevent the model from overfitting and enhance the generalization ability of the model.

[0081] During the model training process, the polyethylene pipeline creep aging dataset is divided into a polyethylene pipeline creep aging training set and a polyethylene pipeline creep aging validation set. Iterative training is performed through multiple epochs according to the polyethylene pipeline creep aging training set. Each epoch includes forward propagation, loss calculation, backpropagation, and parameter update steps. After each epoch, the validation set is used to evaluate the model performance, and the validation set loss is recorded. When the validation set loss has not improved within 10 consecutive epochs, it is considered that the model has reached the best performance, and the training is stopped to obtain the trained ultrasonic non-destructive mechanical property prediction model.

[0082] In this embodiment, the obtaining of the real-time polyethylene ultrasonic pulse signal, obtaining the real-time polyethylene mechanical property parameters through the ultrasonic non-destructive mechanical property prediction model according to the real-time polyethylene ultrasonic pulse signal, and obtaining the remaining life of the gas polyethylene pipeline through the polyethylene mechanical degradation model according to the real-time polyethylene mechanical property parameters are specifically implemented through the following steps:

[0083] S501: Preset the polyethylene mechanical property failure criterion coefficient;

[0084] S502: Construct a polyethylene mechanical degradation model through comprehensive degradation according to the polyethylene mechanical property failure criterion coefficient and the polyethylene mechanical property parameters. The polyethylene mechanical property parameters are the polyethylene mechanical property parameters in the polyethylene pipeline creep aging dataset;

[0085] The mathematical expression of the polyethylene mechanical degradation model is:

[0086] ,

[0087] where D(t) is the polyethylene mechanical degradation index at time t, A1 is the critical threshold of elongation at break, B1 is the critical threshold of tensile yield stress, C1 is the critical threshold of impact strength, β1 is the weight coefficient of elongation at break, β2 is the weight coefficient of tensile yield stress, β3 is the weight coefficient of impact strength, A(t) is the elongation at break degradation model, B(t) is the tensile yield stress degradation model, C(t) is the impact strength degradation model respectively, A0 is the initial value of elongation at break, B0 is the initial value of tensile yield stress, and C0 is the initial value of impact strength;

[0088] The elongation at break decay model is as follows:

[0089] ,

[0090] where k a is the elongation at break decay rate coefficient, and n is the elongation at break decay index.

[0091] The tensile yield stress decay model is as follows:

[0092] ,

[0093] where k b is the tensile yield stress decay rate coefficient, and m is the tensile yield stress decay index.

[0094] The impact strength decay model is as follows:

[0095] ,

[0096] where k c is the impact strength decay rate coefficient, and p is the impact strength decay index.

[0097] It should be noted that multiple groups of polyethylene mechanical property parameters obtained through experiments are used to determine the unknown independent variables of the polyethylene mechanical decay model by data fitting;

[0098] S503: Preset a multi-dimensional failure comprehensive index of polyethylene mechanics, and obtain the remaining life of the gas polyethylene pipeline according to the multi-dimensional failure comprehensive index of polyethylene mechanics and the real-time polyethylene mechanical property parameters through the polyethylene mechanical decay model;

[0099] Specifically, taking the multi-dimensional failure comprehensive index of polyethylene mechanics as the result value of the polyethylene mechanical decay model, the value at time t is deduced backwards, and the remaining life of the gas polyethylene pipeline is calculated through the value at time t and the current value of the real-time polyethylene mechanical property parameters.

[0100] The computer storage medium of the embodiments of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may, for example, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, device, or component.

[0101] The computer-readable signal media may include data signals propagated in a baseband or as part of a carrier wave, which carry computer-readable program codes. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, which can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component.

[0102] The program codes contained on the computer-readable media may be transmitted using any appropriate media, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above. The computer program codes for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program codes may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., connected through the Internet using an Internet service provider).

[0103] The above are only the preferred embodiments of the present invention and do not impose any formal restrictions on the present invention. Although the present invention has been disclosed above in the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for predicting the service life of buried polyethylene gas pipelines based on mechanical properties, characterized in that, Including: Obtain a creep-aged polyethylene sample, obtain a polyethylene ultrasonic pulse signal through ultrasonic detection based on the creep-aged polyethylene sample, and obtain polyethylene ultrasonic spectrum coefficient parameters through ultrasonic signal statistics extraction based on the polyethylene ultrasonic pulse signal; Obtain polyethylene mechanical property parameters through mechanical detection based on the creep-aged polyethylene sample, and construct a polyethylene pipeline creep aging data set based on the polyethylene mechanical property parameters and the polyethylene ultrasonic spectrum coefficient parameters; Obtain an ultrasonic non-destructive mechanical property prediction model through non-linear modeling based on the polyethylene pipeline creep aging data set; Obtain a real-time polyethylene ultrasonic pulse signal, obtain real-time polyethylene mechanical property parameters through the ultrasonic non-destructive mechanical property prediction model based on the real-time polyethylene ultrasonic pulse signal, and obtain the remaining life of the gas polyethylene pipeline through a polyethylene mechanical degradation model based on the real-time polyethylene mechanical property parameters.

2. The method for predicting the service life of buried gas polyethylene pipelines based on mechanical properties according to claim 1, wherein The extraction of the polyethylene ultrasonic spectrum coefficient parameters includes: Obtain a polyethylene ultrasonic pulse signal through the single-probe pulse reflection echo method based on the creep-aged polyethylene sample; Sample the polyethylene ultrasonic pulse signal through an analog-to-digital converter to obtain a discrete polyethylene ultrasonic pulse signal sequence; Decompose the discrete polyethylene ultrasonic pulse signal sequence through lifting wavelet transform to obtain polyethylene pulse signal detail coefficients and polyethylene pulse signal approximation coefficients; Obtain a polyethylene ultrasonic pulse denoising signal through reconstruction error compensation based on the polyethylene pulse signal detail coefficients and the polyethylene pulse signal approximation coefficients; Obtain a polyethylene ultrasonic spectrum through ultrasonic spectrum analysis based on the polyethylene ultrasonic pulse denoising signal; Obtain polyethylene ultrasonic spectrum coefficient parameters through ultrasonic spectrum statistics extraction based on the polyethylene ultrasonic spectrum.

3. The method for predicting the service life of buried polyethylene gas pipelines based on mechanical properties according to claim 2, wherein The mathematical expression of the polyethylene ultrasonic pulse denoising signal is: ， Where S(n) is the polyethylene ultrasonic pulse denoising signal, S0(n) is the original polyethylene ultrasonic pulse signal, S1(n) is the polyethylene ultrasonic pulse reconstruction signal, n is the discrete index of the polyethylene ultrasonic pulse signal, and α is the reconstruction error compensation coefficient.

4. The method for predicting the service life of buried gas polyethylene pipelines based on mechanical properties according to claim 1, wherein The ultrasonic non-destructive mechanical property prediction model includes: Receive the polyethylene pipeline creep aging data set through the input layer; Obtain the polyethylene ultrasonic spectrum coefficient parameter sequence feature through the feature extraction layer based on the polyethylene pipeline creep aging data set; Output the predicted value of the polyethylene mechanical property parameter through the output layer based on the polyethylene ultrasonic spectrum coefficient parameter sequence feature, and the output layer is a fully connected layer with three neurons.

5. The method for predicting the service life of buried gas polyethylene pipelines based on mechanical properties according to claim 1, characterized in that, The calculation steps of the remaining life of the gas polyethylene pipeline include: Preset the polyethylene mechanical property failure criterion coefficient; Construct a polyethylene mechanical degradation model through comprehensive degradation based on the polyethylene mechanical property failure criterion coefficient and the polyethylene mechanical property parameters; Preset the polyethylene mechanical multi-dimensional failure comprehensive index, and obtain the remaining life of the gas polyethylene pipeline through the polyethylene mechanical degradation model based on the polyethylene mechanical multi-dimensional failure comprehensive index and the real-time polyethylene mechanical property parameters.

6. The method for predicting the service life of buried gas polyethylene pipelines based on mechanical properties according to claim 5, characterized in that, The mathematical expression of the polyethylene mechanical degradation model is: ， Wherein, D(t) is the mechanical degradation index of polyethylene at time t, A1 is the critical threshold of elongation at break, B1 is the critical threshold of tensile yield stress, C1 is the critical threshold of impact strength, β1 is the weight coefficient of elongation at break, β2 is the weight coefficient of tensile yield stress, β3 is the weight coefficient of impact strength, A(t) is the degradation model of elongation at break, B(t) is the degradation model of tensile yield stress, C(t) is the degradation model of impact strength respectively, A0 is the initial value of elongation at break, B0 is the initial value of tensile yield stress, and C0 is the initial value of impact strength.

7. The method for predicting the service life of buried polyethylene gas pipelines based on mechanical properties according to claim 5, characterized in that, The method for obtaining the remaining life of the gas polyethylene pipeline through the mechanical degradation model of polyethylene includes: Back-calculating the value at time t from the mechanical degradation index of polyethylene according to the multi-dimensional failure comprehensive index of polyethylene mechanics; Calculating the remaining life of the gas polyethylene pipeline through the value at the current time according to the value at time t and the real-time mechanical property parameters of polyethylene.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for predicting the life of the buried gas polyethylene pipeline based on mechanical properties as described in any one of claims 1-7.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the method for predicting the life of the buried gas polyethylene pipeline based on mechanical properties as described in any one of claims 1-7 when executed by a computer processor.

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