Pipeline thickness measuring method and system capable of resisting interference of multiple layers of heat preservation media

Abstract

The invention relates to the technical field of electromagnetic nondestructive testing, in particular to a pipeline thickness measuring method and system capable of resisting interference of multiple layers of heat preservation media, and the method comprises the following steps: carrying out filtering processing on pulse eddy current signals of pipelines with different thicknesses under different heat preservation media by using an EMD algorithm improved by a particle swarm algorithm; a pulse eddy current filtering signal is obtained; extracting a middle-term signal intercept characteristic value, a power spectral density peak value characteristic value and a magnetic flux characteristic value of the pulsed eddy current filtering signals of the pipelines with different thicknesses under different heat preservation media; carrying out linear compensation on the mid-term signal intercept characteristic value, the power spectral density peak value characteristic value and the magnetic flux characteristic value of the extracted pulsed eddy current signal; and establishing a multi-dimensional feature fusion pipeline thickness inversion model by using the feature values after linear compensation. Linear compensation is carried out on power spectrum density peak values, magnetic fluxes and medium-term signal intercept extracted under different heat preservation media, so that interference of different heat preservation media on thickness measurement is eliminated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electromagnetic nondestructive testing, and more particularly to a pipeline thickness measurement method and system resistant to multi-layer thermal insulation medium interference. BACKGROUND

[0002] In the fields of petroleum, chemical industry and energy, pipelines are the core carriers for transporting media such as crude oil, natural gas and high-temperature steam, and are long-term in extreme working conditions of high pressure, high temperature and strong corrosion. Most of these pipelines need to be coated with multi-layer thermal insulation medium, and the inner layer is usually thermal insulation materials such as magnesium aluminum silicate, aluminum silicate, polyurethane and foam glass, and the outer layer is a metal protective layer to achieve heat insulation, corrosion protection and mechanical protection. However, once defects such as wall thickness thinning and local corrosion occur in such pipelines, they may cause media leakage, explosion and other serious accidents. Traditional detection methods, such as ultrasonic thickness gauges, have obvious limitations. The pulsed eddy current detection technology has unique advantages in this scenario: it can achieve continuous scanning without removing the thermal insulation layer, and can complete the detection within the continuous production cycle of the oil transportation station and chemical plant.

[0003] The multi-layer thermal insulation medium interference problem of petroleum, chemical and energy pipelines is more complex. The thermal insulation layer may be partially compacted or hollow due to long-term vibration, and the high-temperature environment of energy pipelines may also cause fluctuations in the dielectric constant of the thermal insulation layer. Different thermal insulation media have different effects on pulsed eddy current signals. How to resist the interference of different types of thermal insulation media to achieve accurate measurement of pipeline thickness has become a top priority for pulsed eddy current detection. Currently, many scholars mostly use a single eigenvalue for thickness inversion, but for different thermal insulation medium interference, a single eigenvalue cannot achieve accurate measurement. Therefore, how to resist multi-layer thermal insulation medium interference when measuring pipeline thickness has become a technical problem that needs to be solved by technical personnel in the field. SUMMARY

[0004] Therefore, the present application provides a pipeline thickness measurement method and system resistant to multi-layer thermal insulation medium interference, which can eliminate the interference of different thermal insulation media on pipeline thickness measurement.

[0005] To achieve the above purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a pipeline thickness measurement method resistant to multi-layer thermal insulation medium interference, comprising the following steps:

[0007] S1: using an EMD algorithm improved by a particle swarm algorithm to filter the pulsed eddy current signals of pipelines of different thicknesses under different thermal insulation media, to obtain pulsed eddy current filtered signals;

[0008] S2: extract the mid-term signal intercept characteristic value, power spectral density peak characteristic value and magnetic flux characteristic value of the pulse eddy current filtered signal of the pipeline with different thickness under different heat preservation medium;

[0009] S3: linearly compensate the mid-term signal intercept characteristic value, power spectral density peak characteristic value and magnetic flux characteristic value of the extracted pulse eddy current signal;

[0010] S4: establish a pipeline thickness inversion model of multi-dimensional feature fusion by using the linearly compensated characteristic values.

[0011] Further, S1 comprises:

[0012] S101: fix the basic parameters of EMD algorithm, set the parameters and optimization variables of particle swarm algorithm, and the optimization variables are the screening stop threshold, the effective IMF starting layer and the effective IMF ending layer;

[0013] S102: initialize the particle swarm and calculate the initial fitness, randomly generate n groups of parameters in the constraint range, integerize the IMF layer number and ensure to meet the constraint; perform EMD decomposition for each group of parameters, accumulate the components from the effective IMF starting layer to the effective IMF ending layer to reconstruct the signal, calculate the signal-to-noise ratio (SNR) of the reconstructed signal and the original signal as the initial fitness; record the individual optimal and global optimal;

[0014] S103: update the fitness and optimal solution in iteration, linearly decrease the inertia weight in each iteration, update the speed and position according to the current position, individual optimal and global optimal of the particle, and perform constraint processing on the new position parameters; recalculate the SNR of the new parameters as the fitness, and update the individual optimal and global optimal position;

[0015] S104: when the iteration number reaches m times or the global optimal is not updated for k consecutive generations, stop optimization, output the parameters corresponding to the global optimal, use the optimal parameters to perform EMD decomposition on the original pulse eddy current signal under different heat preservation medium, reconstruct the corresponding interval IMF component, and obtain the final pulse eddy current filtered signal.

[0016] Further, in S2, the extraction step of the mid-term signal intercept characteristic value comprises:

[0017] S201: in the double logarithmic coordinate system, intercept the 10-1000 mV part of each pulse eddy current filtered signal, iterate the length and position of the intercepted signal segment, and the initial position and step of iteration are 10 mV, to find the best signal segment for calculating the mid-term signal intercept characteristic value;

[0018] S202: compare the mid-term signal intercepts of different signal segments in S21, when the characteristic values of different thickness pipelines under the same medium lift are equal, the corresponding signal segment is the best signal segment;

[0019] S203: Linear fitting is performed on the optimal signal segment of S22 to obtain the y-axis intercept of the fitting straight line as the mid-term signal intercept characteristic value of the pulsed eddy current filtered signal.

[0020] Further, in S2, the step of extracting the power spectral density peak characteristic value comprises:

[0021] S204: The pulsed eddy current filtered signal of the pipe with the maximum thickness is selected as the reference signal, and the pulsed eddy current filtered signals of the pipes with other thicknesses are respectively subtracted from the reference signal to obtain differential signals of the pipes with different thicknesses;

[0022] S205: The differential signals are intercepted at 0.01-1000 mV, and the power spectral density peaks of the differential signals at different lengths and different positions are iteratively calculated, and the initial position and step length of iteration are both 10 mV;

[0023] S206: The power spectral density peaks of the differential signals of the pipes with different thicknesses are compared, and the power spectral density peak monotonically decreases with the increase of the pipe thickness, and when the power spectral density peaks of the pipes with different thicknesses have the maximum difference, the optimal differential signal segment is obtained;

[0024] S207: The power spectral density peak of the optimal differential signal segment is calculated using the Welch method as the power spectral density peak characteristic value of the pulsed eddy current filtered signal.

[0025] Further, in S2, the step of extracting the magnetic flux characteristic value comprises:

[0026] S208: The 0.01-1000 mV part of each pulsed eddy current filtered signal is intercepted, and the signal is divided into signal segments of different lengths, and the initial position and step length are both 10 mV;

[0027] S209: The magnetic flux is calculated according to the signal segments divided in S208, and the signal segment with the maximum difference in magnetic flux of the pipes with different thicknesses is taken as the optimal signal segment;

[0028] S210: The magnetic flux of the optimal signal segment is calculated as the magnetic flux characteristic value of the pulsed eddy current filtered signal.

[0029] Further, in S3, the step of linearly compensating the power spectral density peak and the magnetic flux characteristic value comprises:

[0030] S301: The power spectral density peak characteristic values of the pipes with different thicknesses under air are taken as standard values, the power spectral density peak characteristic values of the pipes with different thicknesses under different thermal insulation media are taken as inputs, and the y=ax+b is used to fit them to obtain the compensation coefficients a and b of the power spectral density peak characteristic values under different thermal insulation media, respectively;

[0031] The peak value of the power spectrum density of the pipeline with different thicknesses under different heat preservation media and the compensation coefficients a and b are used to calculate the peak value of the power spectrum density of the pipeline with different thicknesses under different heat preservation media after compensation.

[0032] In S302, the magnetic flux characteristic value of the pipeline with different thicknesses under air is taken as a standard value, and the magnetic flux characteristic value of the pipeline with different thicknesses under different heat preservation media is taken as an input, which is fitted using y=cx+d, to obtain the compensation coefficients c and d of the magnetic flux characteristic value under different heat preservation media.

[0033] The magnetic flux characteristic value of the pipeline with different thicknesses under different heat preservation media and the compensation coefficients c and d are used to calculate the magnetic flux characteristic value of the pipeline with different thicknesses under different heat preservation media after compensation.

[0034] Further, in S3, the mutual compensation relationship of the peak value of the power spectrum density and the magnetic flux characteristic value is as follows:

[0035] The peak value of the power spectrum density and the magnetic flux characteristic value are normalized, when the pipeline thickness is 1.5-5 mm, the weight of the peak value of the power spectrum density in the thickness inversion model is increased, and the weight of the magnetic flux characteristic value in the thickness inversion model is reduced; when the pipeline thickness is 6-12 mm, the weight of the peak value of the power spectrum density in the thickness inversion model is increased, and the weight of the magnetic flux characteristic value in the thickness inversion model is reduced; and the characteristic value with high sensitivity to thickness is always taken as the dominant characteristic value of thickness inversion.

[0036] Further, in S3, the medium signal intercept characteristic value is used to replace the medium lift-off condition, and the medium signal intercept characteristic value is used as a criterion to select different compensation coefficients for the peak value of the power spectrum density and the magnetic flux characteristic value under different medium signal intercept characteristic values.

[0037] Further, S4 includes:

[0038] S401: A database of the linearly compensated peak value of the power spectrum density, the magnetic flux characteristic value and the medium signal intercept characteristic value of the pipeline with different thicknesses under different heat preservation media is established;

[0039] S402: The database is divided into a training set and a test set, a pipeline thickness inversion model based on a random forest algorithm is constructed, and training and testing are performed;

[0040] S403: The model is hyperparameter-optimized using a cross-validation grid search method;

[0041] S404: 5-fold cross-validation is performed on different parameter combinations one by one, and the parameter combination with the minimum negative mean square error is selected as the optimal solution;

[0042] S405: updating the model parameters according to the optimal solution to obtain a final pipe thickness inversion model; the input of the pipe thickness inversion model is the power spectral density peak eigenvalue, the magnetic flux eigenvalue and the intermediate signal intercept eigenvalue of the pulse eddy current signal, and the output is the pipe thickness.

[0043] In a second aspect, the present application provides a pipe thickness measurement system resistant to interference of multi-layer thermal insulation medium, which adopts the method as described above, comprising:

[0044] A signal acquisition module is configured to acquire pulse eddy current signals of pipes with different thicknesses under different thermal insulation media.

[0045] A signal filtering module is configured to filter the pulse eddy current signals of pipes with different thicknesses under different thermal insulation media using the EMD algorithm improved by the particle swarm algorithm to obtain pulse eddy current filtered signals.

[0046] A signal eigenvalue extraction module is configured to extract the intermediate signal intercept eigenvalue, the power spectral density peak eigenvalue and the magnetic flux eigenvalue of the pulse eddy current filtered signals of pipes with different thicknesses under different thermal insulation media.

[0047] A compensation module is configured to linearly compensate the intermediate signal intercept eigenvalue, the power spectral density peak eigenvalue and the magnetic flux eigenvalue of the extracted pulse eddy current signals.

[0048] A pipe thickness inversion module is configured to establish a multi-dimensional feature fusion pipe thickness inversion model using the linearly compensated eigenvalues.

[0049] According to the technical solution described above, compared with the prior art, the present application has the following beneficial effects:

[0050] 1. The EMD algorithm improved by the particle swarm algorithm is used for filtering in the present application, which can reduce noise and improve the signal-to-noise ratio.

[0051] 2. The power spectral density peak and the magnetic flux are used to compensate each other in terms of thickness and noise in the present application. The power spectral density peak has higher sensitivity at small thickness, while the magnetic flux has higher sensitivity at large thickness, and the sensitivity to thickness is always high in different thickness ranges. The two are extracted by different methods, which can reduce different types of noise.

[0052] 3. The characteristic quantities extracted under different thermal insulation media are linearly compensated in the present application, which effectively resists the interference of different thermal insulation media and improves the inversion accuracy.

[0053] 4. The intermediate signal intercept is used to replace the lift-off condition in the present application, which can reduce the influence of lift-off on inversion accuracy. The power spectral density peak, the magnetic flux and the intermediate signal intercept are used for multi-eigenvalue fusion to train the inversion model, which effectively resists the interference of different thermal insulation media and improves the inversion accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description only constitute a part of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of the provided drawings.

[0055] Figure 1 The flow chart of the pipeline thickness measurement method provided by the present application against multi-layer thermal insulation medium interference;

[0056] Figure 2 The structural schematic diagram of the pipeline with different thickness to be measured provided by the present application;

[0057] Figure 3 The structural schematic diagram of different thermal insulation media provided by the present application;

[0058] Figure 4 The pulse eddy current signal diagram of the pipeline with a thickness of 1.5-12 mm after filtering the air 50 mm away provided by the present application; DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort belong to the scope of protection of the present application.

[0060] The embodiments of the present application disclose a pipeline thickness measurement method against multi-layer thermal insulation medium interference, comprising the following steps:

[0061] S1: using the EMD algorithm improved by the particle swarm algorithm to filter and process the pulse eddy current signals of the pipelines with different thickness under different thermal insulation media, to obtain pulse eddy current filtered signals;

[0062] S2: extracting the medium-term signal intercept characteristic values, power spectral density peak characteristic values and magnetic flux characteristic values of the pulse eddy current filtered signals of the pipelines with different thickness under different thermal insulation media;

[0063] S3: performing linear compensation on the medium-term signal intercept characteristic values, power spectral density peak characteristic values and magnetic flux characteristic values of the extracted pulse eddy current signals;

[0064] S4: establishing a multi-dimensional feature fusion pipeline thickness inversion model by using the linearly compensated characteristic values.

[0065] AsFigure 2 As shown, the pipeline to be tested provided in the embodiment of the present invention is a 20# ferromagnetic steel pipe, wherein the length of the pipeline to be tested is 250 mm, the inner diameter is 50 mm, and the thicknesses are 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 10 and 12 mm respectively.

[0066] like Figure 3 As shown, the thermal insulation media provided by the embodiments of the present invention are magnesium aluminum silicate, aluminum silicate, polyurethane and foam glass, wherein each thermal insulation medium has a length of 250 mm, a width of 250 mm and a thickness of 50 mm.

[0067] The following combination Figure 1 The above steps S1-S4 are further explained.

[0068] S1: Use the EMD algorithm improved by the particle swarm algorithm to filter the pulse eddy current signals of pipes with different thicknesses under different insulation media to obtain the pulse eddy current filtered signals, specifically including:

[0069] S101: Fix the basic parameters of the EMD algorithm, set the parameters and optimization variables of the particle swarm algorithm, and the optimization variables are the screening stop threshold, the effective IMF starting layer, and the effective IMF ending layer; among them, the screening stop threshold constraint range is 0.15-0.45, with a step size of 0.05, the effective IMF starting layer range is 2-5, with a step size of 1, and the effective IMF ending layer range is 4-8, with a step size of 1, and the effective IMF ending layer must meet the requirement of effective IMF ending layer ≥ effective IMF starting layer + 1; the particle swarm parameters are 20 particles, a maximum number of iterations of 50 times, learning factors c1 and c2 are both 2.0, and the inertia weight decreases linearly from 0.8 to 0.3; the fitness function is the signal-to-noise ratio (SNR) of the filtered signal to the original signal, and the goal is to maximize the SNR.

[0070] S102: Initialize the particle swarm and calculate the initial fitness. Randomly generate n sets of parameters within the constraints, integerize the number of IMF layers and ensure that the constraints are met, where n=20. Perform EMD decomposition on each set of parameters, accumulate the components from the effective IMF starting layer to the effective IMF ending layer to reconstruct the signal, and calculate the signal-to-noise ratio (SNR) of the reconstructed signal to the original signal as the initial fitness. Record the individual optimal (the highest SNR and corresponding parameters of each particle) and the global optimal (the highest SNR and corresponding parameters among all particles).

[0071] S103: Iterate and optimize to update the fitness and optimal solution. In each iteration, the inertia weight is linearly reduced. The speed and position of the particle are updated according to the current position, individual optimal and global optimal, and the new position parameters are constrained. The signal-to-noise ratio (SNR) corresponding to the new parameters is recalculated as the fitness, and the individual optimal and global optimal positions are updated.

[0072] S104: when the number of iterations reaches m times or the global optimum is not updated for k generations in succession, stop the optimization, output the parameter corresponding to the global optimum, use the optimal parameter to perform EMD decomposition on the original pulse eddy current signals of different heat preservation media, reconstruct the corresponding interval IMF component, and obtain the final pulse eddy current filtered signal; wherein m = 50 and k = 10.

[0073] After obtaining the induced voltage signals of the pipelines with different thicknesses under different heat preservation media, the induced voltage signals are logarithmically processed. The late signal segments of the induced voltage signals of the pipelines with different thicknesses are extremely weak, usually in the order of mV. This leads to the fact that the signals of different thicknesses almost coincide in the Cartesian coordinate system, and the specific curves corresponding to the thicknesses cannot be identified. Then, the EMD algorithm improved by the particle swarm is used to optimize the filtering parameters. In this process, the parameters of the particle swarm optimization algorithm are set, the optimization variables are the screening stop threshold, the effective IMF starting layer and the effective IMF ending layer of the EMD algorithm, the screening stop threshold is constrained in the range of 0.15-0.45 with a step of 0.05, the effective IMF starting layer is in the range of 2-5 with a step of 1, the effective IMF ending layer is in the range of 4-8 with a step of 1, the fitness function is the signal-to-noise ratio (SNR) of the filtered signal and the original signal, and maximizing the SNR is taken as the optimization target of the fitness function. The size of the particle swarm is 20, the maximum number of iterations is 50, the learning factors c1 and c2 are both 2.0, the initial value of the inertia weight is 0.8, and the inertia weight is linearly decreased to 0.3 according to the number of iterations. In this embodiment, the optimal screening stop threshold obtained by the particle swarm algorithm is 0.25, the optimal effective IMF starting layer is 3, and the optimal effective IMF ending layer is 6. Therefore, the EMD algorithm with the screening stop threshold of 0.25, the effective IMF starting layer of 3 and the effective IMF ending layer of 6 is used to filter the eddy current signals, and the result is shown in FIG. 4. Figure 4

[0074] On the basis of step S1, step S2 is further performed to extract the mid-term signal intercept characteristic value, the power spectral density peak characteristic value and the magnetic flux characteristic value of the pulse eddy current filtered signals of the pipelines with different thicknesses under different heat preservation media. The extraction steps of the mid-term signal intercept characteristic value include the following steps.

[0075] S201: in the double logarithmic coordinate system, the 10-1000 mV part of each pulse eddy current filtered signal is intercepted, the length and position of the intercepted signal segment are iterated, the initial position and step of the iteration are 10 mV, and the optimal signal segment for obtaining the mid-term signal intercept characteristic value is found.

[0076] S202: the mid-term signal intercepts of the different signal segments in S21 are compared, and when the characteristic values of the pipelines with different thicknesses under the same medium lift are equal, the corresponding signal segment is the optimal signal segment.

[0077] ​S203: Linear fitting is performed on the optimal signal segment of S22 to obtain the y-axis intercept of the fitting straight line as the mid-term signal intercept characteristic value of the pulse eddy current filtered signal.

[0078] The extraction step of the power spectrum density peak characteristic value includes:

[0079] S204: The pulse eddy current filtered signal of the pipe with the maximum thickness is selected as the reference signal, and the pulse eddy current filtered signals of the pipes with other thicknesses are respectively subtracted from the reference signal to obtain differential signals of the pipes with different thicknesses.

[0080] S205: The differential signal is intercepted at 0.01-1000 mV, and the power spectrum density peak of the differential signal at different lengths and different positions is iteratively calculated, and the initial position and step length of iteration are both 10 mV.

[0081] S206: The power spectrum density peaks of the differential signals of the pipes with different thicknesses are compared, and the power spectrum density peak monotonously decreases with the increase of the pipe thickness. When the power spectrum density peaks of the pipes with different thicknesses have the maximum difference, the optimal differential signal segment is obtained.

[0082] S207: The power spectrum density peak of the optimal differential signal segment is calculated using the Welch method as the power spectrum density peak characteristic value of the pulse eddy current filtered signal.

[0083] The extraction step of the magnetic flux characteristic value includes:

[0084] S208: The 0.01-1000 mV part of each pulse eddy current filtered signal is intercepted, and the signal is divided into signal segments with different lengths, and the initial position and step length are both 10 mV.

[0085] S209: The magnetic flux is calculated according to the signal segments divided in S208, and the signal segment with the maximum difference in the magnetic flux of the pipes with different thicknesses is taken as the optimal signal segment.

[0086] S210: The magnetic flux of the optimal signal segment is calculated as the magnetic flux characteristic value of the pulse eddy current filtered signal.

[0087] After the eddy current signals of the pipes with different thicknesses are filtered, in order to extract the mid-term signal intercept characteristic value, the voltage threshold of the eddy current signal segment is found. In the double logarithmic coordinate system, the mid-term signal intercepts of the pipes with different thicknesses under the same lift-off are equal. In this embodiment, the approximate range of the mid-term signal segment is 10-1000 mV, and the signal segment length and position are iterated with 10 mV as the step length and 10 mV as the initial value to obtain the optimal signal segment.

[0088] In order to extract the power spectrum density peak characteristic value, the eddy current signal segment voltage threshold is found, the power spectrum density peak characteristic value is extracted in the late signal segment, and monotonically decreases with the increase of the pipeline thickness. In this embodiment, the approximate range of the late signal segment is 0.01-1000 mV, the maximum difference of the power spectrum density peak characteristic value of different thicknesses is taken as the target, the signal segment length and position are taken as the step of 10 mV, the initial value is 10 mV, and iteration is performed to obtain the best differential signal segment.

[0089] In order to extract the magnetic flux characteristic value, the eddy current signal segment voltage threshold is found, the magnetic flux characteristic value is extracted in the late signal segment, and monotonically increases with the increase of the pipeline thickness. In this embodiment, the approximate range of the late signal segment is 0.01-1000 mV, the maximum difference of the magnetic flux characteristic value of different thicknesses is taken as the target, the signal segment length and position are taken as the step of 10 mV, the initial value is 10 mV, and iteration is performed to obtain the best differential signal segment.

[0090] Next, step S3 is performed, and the mid-term signal intercept characteristic value, the power spectrum density peak characteristic value and the magnetic flux characteristic value of the extracted pulsed eddy current signal are linearly compensated. Among them, the power spectrum density peak and the magnetic flux characteristic value under different heat preservation media are basically equal to the power spectrum density peak and the magnetic flux characteristic value under air after linear compensation, resisting the interference of different heat preservation media on thickness measurement; the mid-term signal intercept allows the power spectrum density peak and the magnetic flux to select different compensation coefficients under different lift-offs, improves the compensation accuracy, and further resists the interference of different heat preservation media.

[0091] Specifically, the steps of linearly compensating the power spectrum density peak and the magnetic flux characteristic value include:

[0092] S301: taking the power spectrum density peak characteristic value of the pipeline under air at different thicknesses as a standard value, taking the power spectrum density peak characteristic value of the pipeline under different heat preservation media at different thicknesses as an input, using y=ax+b to fit it, and respectively obtaining the compensation coefficients a and b of the power spectrum density peak characteristic value under different heat preservation media;

[0093] The power spectrum density peak characteristic value of the pipeline under different heat preservation media at different thicknesses after compensation is calculated by using the power spectrum density peak and the compensation coefficients a and b of the pipeline under different heat preservation media at different thicknesses.

[0094] Specifically, first, in the air environment, the power spectrum density peak characteristic values corresponding to a series of different thickness pipes are measured, and these standard values are taken as x in the fitting formula. Then, the pipes are placed in different thermal insulation media, and the power spectrum density peak characteristic values corresponding to different thickness pipes are measured, and these values are taken as y in the fitting formula. For each specific thermal insulation medium, an independent fitting is performed, and the standard values x measured in the air and the corresponding values y measured in the thermal insulation medium under different pipe thicknesses are taken as data points, and a linear model y=ax+b is used to fit these data points, and then the compensation coefficients a and b of the power spectrum density peak characteristic values under the thermal insulation medium are obtained.

[0095] S302: The magnetic flux characteristic values of the pipes of different thicknesses in the air are taken as standard values, the magnetic flux characteristic values of the pipes of different thicknesses in different thermal insulation media are taken as inputs, and y=cx+d is used to fit them, and the compensation coefficients c and d of the magnetic flux characteristic values in different thermal insulation media are obtained respectively.

[0096] The magnetic flux characteristic values of the pipes of different thicknesses in different thermal insulation media after compensation are calculated by using the magnetic flux characteristic values of the pipes of different thicknesses in different thermal insulation media and the compensation coefficients c and d.

[0097] Different thermal insulation media interfere with eddy current signals differently, and the extracted characteristic values will also differ in value, which needs to be compensated. The power spectrum density peak value is extracted from the differential signal, and the differential signal amplitude of the maximum thickness pipe is always 0, and the extracted power spectrum density peak value is also 0. Only linear compensation is required for other thickness pipes. Table 1 is the power spectrum density peak characteristic value compensation coefficient under different thermal insulation media, and the magnetic flux is integrated on the eddy current signal rather than the differential signal, and there is no case where the characteristic value of a certain thickness pipe is always 0. Linear compensation is required for the magnetic flux characteristic values of all thickness pipes. Table 2 is the magnetic flux characteristic value compensation coefficient under different thermal insulation media.

[0098] Table 1: Power spectrum density peak characteristic value compensation coefficient under different thermal insulation media

[0099]

[0100] Table 2: Magnetic flux characteristic value compensation coefficient under different thermal insulation media

[0101]

[0102] Further, the mutual compensation relationship between the power spectrum density peak characteristic value and the magnetic flux characteristic value is as follows:

[0103] The power spectrum density peak characteristic value and the magnetic flux characteristic value are normalized. When the pipe thickness is 1.5-5 mm, the weight of the power spectrum density peak characteristic value in the thickness inversion model is increased, and the weight of the magnetic flux characteristic value in the thickness inversion model is reduced. Specifically, the weight of the power spectrum density peak characteristic value in the thickness inversion model can be adjusted to 70%, and the weight of the magnetic flux in the thickness inversion model can be adjusted to 30%.

[0104] When the pipe thickness is 6-12 mm, the weight of the power spectrum density peak characteristic value in the thickness inversion model is increased, and the weight of the magnetic flux characteristic value in the thickness inversion model is reduced. The weight of the power spectrum density peak in the thickness inversion model is adjusted to 30%, and the weight of the magnetic flux in the thickness inversion model is adjusted to 70%. The characteristic value with high thickness sensitivity is always used as the dominant characteristic value for thickness inversion.

[0105] The intermediate signal intercept characteristic value is related to the medium lift-off condition and is independent of the thickness. The intermediate signal intercept is used instead of the lift-off, and then the intermediate signal intercept characteristic value is used as the criterion. Different compensation coefficients are selected for the power spectrum density peak characteristic value and the magnetic flux characteristic value under different intermediate signal intercept characteristic values, so that the influence of the lift-off can be eliminated. The power spectrum density peak has high sensitivity at small thickness, and the magnetic flux has high sensitivity at large thickness. When the pipe thickness is small, the weight of the power spectrum density peak in the thickness inversion model is adjusted to be high, and the weight of the magnetic flux in the thickness inversion model is adjusted to be low. When the pipe thickness is large, the weight of the power spectrum density peak in the thickness inversion model is adjusted to be low, and the weight of the magnetic flux in the thickness inversion model is adjusted to be high. The characteristic value with high thickness sensitivity is always used as the dominant characteristic value for thickness inversion. The magnetic flux, the power spectrum density peak and the intermediate signal intercept multi-characteristic values are fused, the complex multi-layer insulation medium interference is decomposed into a sub-problem that can be independently processed, different correction methods are selected for different interferences to ensure the consistency of the inversion results.

[0106] S4: a pipeline thickness inversion model of multi-dimensional feature fusion is established by using the linearly compensated characteristic values, including:

[0107] S401: a database of linearly compensated power spectrum density peak characteristic values, magnetic flux characteristic values and intermediate signal intercept characteristic values of pipes with different thicknesses under different insulation media is established;

[0108] S402: the database is divided into a training set and a test set, a pipeline thickness inversion model based on a random forest algorithm is constructed, the pipeline thickness inversion model is trained by using the training set, and the pipeline thickness inversion model is verified by using the test set;

[0109] S403: Hyperparameter tuning is performed on the random forest model using the cross-validation grid search method, and the hyperparameters include the number of decision trees, the maximum depth of the decision tree, and the minimum number of samples required for a leaf node;

[0110] S404: The training set is evenly divided into 5 parts, 4 of which are used to train the model, and the remaining 1 part is used to verify the model performance; 5-fold cross-validation (GridSearchCV) is performed on different parameter combinations one by one, and the parameter combination with the minimum negative mean square error is selected as the optimal solution;

[0111] S405: The model parameters are updated according to the optimal solution to obtain the final pipe thickness inversion model; based on the final pipe thickness inversion model, the thickness of the pipe to be measured is inverted, and the input of the pipe thickness inversion model is the power spectral density peak eigenvalue, the magnetic flux eigenvalue and the intermediate signal intercept eigenvalue of the pulse eddy current signal, and the output is the pipe thickness.

[0112] In other embodiments, the present application also provides a pipe thickness measurement system resistant to interference of multi-layer thermal insulation medium, which adopts the above method, comprising:

[0113] A signal acquisition module is configured to acquire pulse eddy current signals of pipes with different thicknesses under different thermal insulation media;

[0114] A signal filtering module is configured to filter the pulse eddy current signals of pipes with different thicknesses under different thermal insulation media using an EMD algorithm improved by a particle swarm algorithm to obtain pulse eddy current filtered signals;

[0115] A signal eigenvalue extraction module is configured to extract the intermediate signal intercept eigenvalue, the power spectral density peak eigenvalue and the magnetic flux eigenvalue of the pulse eddy current filtered signals of pipes with different thicknesses under different thermal insulation media;

[0116] A compensation module is configured to perform linear compensation on the intermediate signal intercept eigenvalue, the power spectral density peak eigenvalue and the magnetic flux eigenvalue of the extracted pulse eddy current signals;

[0117] A pipe thickness inversion module is configured to establish a multi-dimensional feature fusion pipe thickness inversion model using the linearly compensated eigenvalues.

[0118] At this point, the entire process of the pipe thickness measurement method and system resistant to interference of multi-layer thermal insulation medium provided by the present application is completed. It can be understood that the various numbers involved in the embodiments of the present application are only for differentiation for description, and are not used to limit the scope of the embodiments of the present application.

[0119] The various embodiments described in this specification are implemented in a progressive manner, each embodiment focusing on the differences from other embodiments, and the same or similar parts between embodiments can be mutually referred to. For the apparatus disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0120] The above description of disclosed embodiments enables one of ordinary skill in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those of ordinary skill in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for measuring pipe thickness that is resistant to interference from multi-layer insulation media, characterized in that: The following steps are involved: S1: Use the EMD algorithm improved by the particle swarm algorithm to filter the pulse eddy current signals of pipes with different thicknesses under different insulation media to obtain pulse eddy current filtered signals; S2: Extract the mid-term signal intercept eigenvalue, power spectrum density peak eigenvalue and magnetic flux eigenvalue of the pulsed eddy current filter signal of pipelines with different thicknesses under different insulation media; S3: Linear compensation is performed on the mid-term signal intercept eigenvalue, power spectrum density peak eigenvalue and magnetic flux eigenvalue of the extracted pulsed eddy current signal; S4: Use the eigenvalues ​​after linear compensation to establish a pipeline thickness inversion model with multi-dimensional feature fusion.

2. The pipeline thickness measurement method resistant to interference from multi-layer insulation media according to claim 1 is characterized in that: S1 includes: S101: fix the basic parameters of the EMD algorithm, set the parameters and optimization variables of the particle swarm algorithm, and the optimization variables are the screening stop threshold, the effective IMF starting layer, and the effective IMF ending layer; S102: Initialize the particle swarm and calculate the initial fitness. Randomly generate n sets of parameters within the constraint range, integerize the number of IMF layers and ensure that the constraints are met. Perform EMD decomposition on each set of parameters, accumulate the reconstructed signal from the effective IMF starting layer to the effective IMF ending layer, and calculate the signal-to-noise ratio (SNR) of the reconstructed signal to the original signal as the initial fitness. Record individual optimality and global optimality; S103: Iterate and optimize to update the fitness and the optimal solution. In each iteration, the inertia weight is linearly reduced. The speed and position of the particle are updated according to the current position, individual optimal and global optimal, and the new position parameters are constrained. The SNR corresponding to the new parameters is recalculated as the fitness, and the individual optimal and global optimal positions are updated. S104: When the number of iterations reaches m or the global optimum is not updated for k consecutive generations, the optimization is stopped, and the parameters corresponding to the global optimum are output. The original pulsed eddy current signals of different insulation media are decomposed by EMD using the optimal parameters, and the corresponding interval IMF components are reconstructed to obtain the final pulsed eddy current filtered signal.

3. The pipeline thickness measurement method resistant to interference from multi-layer insulation media according to claim 1 is characterized in that: In S2, the steps for extracting the intercept eigenvalue of the mid-term signal include: S201: In a double logarithmic coordinate system, intercept the 10-1000 mV portion of each pulsed eddy current filter signal, iterate the length and position of the intercepted signal segment, with the initial position and step size of the iteration being 10 mV, and find the optimal signal segment for obtaining the mid-term signal intercept eigenvalue; S202: Compare the mid-term signal intercepts of different signal segments of S21. When the characteristic values ​​of pipes of different thicknesses are equal under the same medium lift-off, the corresponding signal segment is the optimal signal segment. S203: Perform linear fitting on the optimal signal segment of S22 to obtain the y-axis intercept of the fitting line as the mid-term signal intercept characteristic value of the pulsed eddy current filter signal.

4. The pipeline thickness measurement method resistant to interference from multi-layer insulation media according to claim 3 is characterized in that: In S2, the steps of extracting the peak eigenvalue of the power spectrum density include: S204: Selecting the pulsed eddy current filter signal of the pipe with the largest thickness as a reference signal, and performing subtraction between the pulsed eddy current filter signals of pipes with other thicknesses and the reference signal to obtain differential signals of pipes with different thicknesses; S205: intercepting a 0.01-1000 mV portion of the differential signal, iteratively calculating the power spectrum density peaks of the differential signal at different lengths and positions, with the initial position and step size of the iteration both being 10 mV; S206: comparing the power spectrum density peaks of the differential signals of pipelines with different thicknesses. The power spectrum density peak decreases monotonically as the pipeline thickness increases. When the difference in the power spectrum density peaks of pipelines with different thicknesses is the largest, it is the optimal differential signal segment. S207: Calculate the power spectrum density peak value of the optimal differential signal segment using the Welch method as the power spectrum density peak eigenvalue of the pulsed eddy current filtered signal.

5. The method for measuring pipe thickness resistant to interference from multi-layer insulation media according to claim 4, characterized in that: In S2, the steps of extracting the magnetic flux characteristic value include: S208: intercepting the 0.01-1000 mV portion of each pulsed eddy current filter signal and dividing it into signal segments of different lengths, with an initial position and a step size of 10 mV; S209: Calculating magnetic flux based on the signal segments divided in S208, and taking the signal segment with the largest difference in magnetic flux for pipes of different thicknesses as the optimal signal segment; S210: Calculate the magnetic flux of the optimal signal segment as the magnetic flux characteristic value of the pulsed eddy current filter signal.

6. The pipeline thickness measurement method resistant to interference from multi-layer insulation media according to claim 1 is characterized in that: In S3, the steps of linearly compensating the power spectrum density peak and the magnetic flux characteristic value include: S301: The peak eigenvalues ​​of the power spectral density of pipes with different thicknesses in air are used as standard values, and the peak eigenvalues ​​of the power spectral density of pipes with different thicknesses in different insulation media are used as inputs, and the peak eigenvalues ​​are fitted using y=ax+b to obtain compensation coefficients a and b for the peak eigenvalues ​​of the power spectral density in different insulation media, respectively. The power spectrum density peak values ​​of pipes with different thicknesses under different insulation media and the compensation coefficients a and b are used to calculate the power spectrum density peak characteristic values ​​of pipes with different thicknesses under different insulation media after compensation. S302: The magnetic flux characteristic values ​​of pipes with different thicknesses in air are taken as standard values, and the magnetic flux characteristic values ​​of pipes with different thicknesses in different insulation media are taken as input, and the magnetic flux characteristic values ​​are fitted using y=cx+d to obtain compensation coefficients c and d of the magnetic flux characteristic values ​​in different insulation media, respectively; The magnetic flux characteristic values ​​of pipes with different thicknesses under different insulation media and compensation coefficients c and d are used to calculate the magnetic flux characteristic values ​​of pipes with different thicknesses under different insulation media after compensation.

7. The pipeline thickness measurement method resistant to interference from multi-layer insulation media according to claim 6 is characterized in that: In S3, the mutual compensation relationship between the peak eigenvalue of the power spectrum density and the eigenvalue of the magnetic flux is as follows: The power spectrum density peak eigenvalue and the magnetic flux eigenvalue are normalized. When the pipeline thickness is 1.5-5 mm, the weight of the power spectrum density peak eigenvalue in the thickness inversion model is increased, and the weight of the magnetic flux eigenvalue in the thickness inversion model is reduced. When the pipeline thickness is 6-12 mm, the weight of the power spectrum density peak eigenvalue in the thickness inversion model is increased, and the weight of the magnetic flux eigenvalue in the thickness inversion model is reduced. The eigenvalue with higher sensitivity to thickness is always used as the dominant eigenvalue for thickness inversion.

8. The pipeline thickness measurement method resistant to interference from multiple layers of thermal insulation media according to claim 1 is characterized in that: In S3, the medium lift-off condition is replaced by the medium intercept characteristic value, and the medium intercept characteristic value is used as the criterion. Different compensation coefficients are selected for the power spectrum density peak characteristic value and the magnetic flux characteristic value under different medium intercept characteristic values.

9. The method for measuring pipe thickness with resistance to interference from multi-layer insulation media according to claim 1, characterized in that S4 include: S401: Establishing a database of linearly compensated power spectrum density peak eigenvalues, magnetic flux eigenvalues, and mid-term signal intercept eigenvalues ​​for pipelines of different thicknesses under different insulation media; S402: Divide the database into a training set and a test set, construct a pipeline thickness inversion model based on a random forest algorithm, and perform training and testing; S403: Use cross-validation grid search method to tune the model hyperparameters; S404: Perform 5-fold cross validation on different parameter combinations one by one, and select the parameter combination with the smallest negative mean square error as the optimal solution; S405: Update the model parameters according to the optimal solution to obtain the final pipeline thickness inversion model; the input of the pipeline thickness inversion model is the power spectrum density peak eigenvalue, magnetic flux eigenvalue and mid-term signal intercept eigenvalue of the pulsed eddy current signal, and the output is the pipeline thickness.

10. A pipe thickness measurement system resistant to interference from multi-layer insulation media, characterized in that: The method according to any one of claims 1 to 9 comprises: Signal acquisition module, used to collect pulsed eddy current signals of pipes of different thicknesses under different insulation media; The signal filtering module is used to filter the pulse eddy current signals of pipes with different thicknesses under different insulation media using the EMD algorithm improved by the particle swarm algorithm to obtain pulse eddy current filtered signals; Signal eigenvalue extraction module, used to extract the mid-term signal intercept eigenvalue, power spectrum density peak eigenvalue and magnetic flux eigenvalue of the pulsed eddy current filter signal of pipelines with different thicknesses under different insulation media; A compensation module is used to perform linear compensation on the mid-term signal intercept eigenvalue, power spectrum density peak eigenvalue and magnetic flux eigenvalue of the extracted pulsed eddy current signal; The pipeline thickness inversion module is used to establish a pipeline thickness inversion model with multi-dimensional feature fusion using the eigenvalues ​​after linear compensation.

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