Fastica-based oil and gas pipeline pulse eddy current response signal denoising method and device

Abstract

The application provides an oil and gas pipeline pulse eddy current response signal denoising method based on FastICA, which comprises the following steps: collecting signals generated during pulse eddy current detection of an oil and gas pipeline to obtain original signal data, wherein the original signal data comprises an oil and gas pipeline pulse eddy current response signal and electromagnetic noise; using an FIR adaptive filtering algorithm to pre-process the collected original signal data to obtain pre-processed signal data; using a MEMD algorithm to decompose the pre-processed signal data, and screening the decomposed components to obtain a final decomposition result; using an MDL criterion to estimate the total number of signal sources of the pre-processed signal data; and separating the oil and gas pipeline pulse eddy current response signal and the electromagnetic noise from the final decomposition result according to the estimated total number of signal sources and using a FastICA independent component analysis algorithm. The application adopting the above scheme can significantly improve the detection accuracy and provide more reliable technical support for the safety monitoring of the oil and gas pipeline.

Description

Technical Field

[0001] The present application relates to the technical field of signal denoising, and in particular to a method and device for denoising pulsed eddy current response signals of oil and gas pipelines based on FastICA. Background Art

[0002] The safe operation of oil and gas pipelines is crucial to industrial production and energy security. However, structural defects such as corrosion and cracks generated during pipeline operation are often difficult to observe directly, requiring the use of non-destructive testing (NDT) technologies for early warning and assessment. Pulsed eddy current testing (PEC) is widely used in the non-destructive testing of oil and gas pipelines due to its sensitive and fast detection speed for defects in conductive materials. However, the signals generated during PEC testing are susceptible to interference from various factors, such as electromagnetic noise in the surrounding environment, power frequency interference signals, and nonlinear effects caused by the complexity of the pipeline structure. These noise signals mix with the effective defect signals, seriously affecting the accuracy of defect detection. Therefore, effectively suppressing and removing noise signals is key to improving detection accuracy.

[0003] To combat this noise interference, traditional noise suppression methods, such as low-pass filtering, high-pass filtering, and band-pass filtering, employ frequency-domain processing methods. While these methods can attenuate noise in specific frequency bands to a certain extent, they often rely on the frequency difference between the noise and the signal. If the frequency range of the noise signal overlaps with the frequency range of the valid signal, the effectiveness of these methods is significantly reduced, making it impossible to accurately remove noise from different sources within the mixed signal. Furthermore, time-frequency analysis techniques, such as the short-time Fourier transform (STFT) and wavelet transform, can decompose and analyze signals in the time-frequency domain. However, these methods still rely on the time-frequency differences between the signal and the noise, resulting in limited effectiveness in removing noise in complex backgrounds, particularly when dealing with non-stationary noise. Summary of the Invention

[0004] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the first purpose of this application is to propose a FastICA-based method for denoising the pulse eddy current response signal of oil and gas pipelines, which solves the technical problem that existing methods cannot accurately remove noise from different sources in mixed signals. This application can effectively separate the target pulse turbine response signal, improve the detection accuracy of oil and gas pipelines, and provide a more reliable technical guarantee for the safety monitoring of oil and gas pipelines.

[0006] The second purpose of this application is to propose a FastICA-based oil and gas pipeline pulse eddy current response signal denoising device.

[0007] The third object of this application is to provide a computer device.

[0008] A fourth object of the present application is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a FastICA-based method for denoising the pulse eddy current response signal of an oil and gas pipeline, comprising: collecting the signal generated during pulse eddy current detection of the oil and gas pipeline to obtain original signal data, wherein the original signal data includes the pulse eddy current response signal of the oil and gas pipeline and electromagnetic noise; using the FIR adaptive filtering algorithm to preprocess the collected original signal data to obtain preprocessed signal data; using the MEMD algorithm to decompose the preprocessed signal data, and screening the decomposed components to obtain the final decomposition result; using the MDL criterion to estimate the total number of signal sources of the preprocessed signal data; and using the FastICA independent component analysis algorithm to separate the oil and gas pipeline pulse eddy current response signal and electromagnetic noise from the final decomposition result based on the estimated total number of signal sources.

[0010] Optionally, in one embodiment of the present application, a sensor is used to collect signals generated when pulsed eddy current testing is performed on an oil and gas pipeline, wherein the sensor is a dual-excitation, dual-receiving focused pulsed eddy current probe.

[0011] Optionally, in one embodiment of the present application, the filter output y(n) after preprocessing using the FIR adaptive filtering algorithm is:

[0012]

[0013] Among them, x(n) is the input signal of each channel, w i (n) is the i-th weight coefficient of the filter at time n, M is the order of the filter,

[0014] The weight coefficients of the filter are adjusted by the error signal and are expressed as:

[0015] w i (n+1)=w i (n)+2μe(n)·x(ni)

[0016] Where μ is the filter step size parameter and e(n) is the error signal, which is expressed as:

[0017] e(n)=d(n)-y(n)

[0018] Wherein, d(n) is the desired signal, specifically the pulsed eddy current response signal of the oil and gas pipeline collected under low-noise conditions.

[0019] Optionally, in one embodiment of the present application, the signal data is decomposed using a MEMD algorithm to obtain a decomposition result, including:

[0020] Step S41: Select l projection directions v in the two-dimensional space i ∈R 2 , i=1,2,...,l;

[0021] Step S42: Project the pre-processed dual-channel signal x[n]=[x1(n),x2(n)] to each direction v i :

[0022]

[0023] Among them, v i,m is the component of the i-th direction vector in the m-th dimension, is the projection signal in this direction;

[0024] Step S43: Perform one-dimensional discrete empirical mode decomposition (EMD) on the projection signal in each direction, and decompose each projection signal into a series of intrinsic mode functions (IMFs):

[0025]

[0026] Where K is the number of decomposed IMFs, is the kth intrinsic mode function of the projection signal in this direction, is the residual;

[0027] Step S44: reconstruct the kth IMF of each channel by taking the geometric mean of the kth IMF in each projection direction:

[0028]

[0029] Among them, the IMF k,m (n) is the kth IMF of the mth channel after reconstruction;

[0030] Step S45: Iterate steps S42-S44 j times. After each iteration, the slowest oscillating IMF decomposed is taken as the slowest changing component and is eliminated to obtain a decomposition result, wherein the decomposition result is:

[0031]

[0032] Among them, x m (n) is the signal of each channel.

[0033] Optionally, in one embodiment of the present application, the decomposed components are screened to obtain a final decomposition result, including:

[0034] Calculate the kurtosis of each IMF series:

[0035]

[0036] Where N is the length of the IMF sequence, μ is the mean value of the IMF sequence, and σ is the standard deviation of the IMF sequence;

[0037] Set the kurtosis threshold b and filter out IMF sequences with kurtosis lower than b.

[0038] Optionally, in one embodiment of the present application, the total number of signal sources of the pre-processed signal data is estimated using the MDL criterion, including:

[0039] Integrate the preprocessed dual-channel signals into a 2×N data matrix X, where N is the length of the time domain signal collected for each channel;

[0040] Perform the district mean processing on each row of the data matrix to obtain

[0041] Compute the covariance matrix:

[0042]

[0043] Perform eigenvalue decomposition on R to obtain eigenvalues ​​λ1,λ2;

[0044] Set the maximum number of signal sources p, and traverse and calculate the MDL value of each signal source number k:

[0045]

[0046] The k value with the smallest calculated MDL is selected as the total number of estimated signal sources.

[0047] Optionally, in one embodiment of the present application, the oil and gas pipeline pulsed eddy current response signal and electromagnetic noise are separated from the final decomposition result based on the estimated total number of signal sources using the FastICA independent component analysis algorithm, including:

[0048] Step S71: Arrange the K IMFs of length N obtained by screening into a K×N matrix X;

[0049] Step S72: Center the matrix X. During the centering process, average each row of the matrix X to obtain the matrix

[0050] Step S73: Matrix Perform whitening, including: calculation The covariance matrix C of C is decomposed into eigenvalues, and the whitening matrix V is calculated. The matrix is ​​then transformed based on the whitening matrix V. Whiten the matrix X W ,in,

[0051] The covariance matrix C is:

[0052]

[0053] Perform eigenvalue decomposition on C, expressed as:

[0054] C=EDE T

[0055] Among them, E is the eigenvector matrix, D is the eigenvalue diagonal matrix,

[0056] The whitening matrix V is:

[0057]

[0058] Matrix X W for:

[0059]

[0060] Step S74: randomly initialize the unmixing matrix W, whose number of rows is the estimated signal source k;

[0061] Step S75: Iteratively update W until W converges. The update method is:

[0062] W + =E[X W tanh(W T X w )]-E[tanh′(W T X W )]W

[0063]

[0064] Step S76: Based on the matrix W and the matrix X W Determine the independent component matrix, expressed as:

[0065] S=WX W

[0066] Among them, S is the separated independent component matrix, and each row in the matrix corresponds to an independent oil and gas pipeline pulse eddy current response signal and noise from different sources.

[0067] To achieve the above objectives, a second embodiment of the present invention provides a FastICA-based device for denoising pulsed eddy current response signals of oil and gas pipelines, comprising:

[0068] The signal acquisition module is used to collect the signals generated when the pulsed eddy current test is performed on the oil and gas pipeline to obtain the original signal data, wherein the original signal data includes the pulsed eddy current response signal and electromagnetic noise of the oil and gas pipeline;

[0069] The signal preprocessing module is used to preprocess the collected original signal data using the FIR adaptive filtering algorithm to obtain preprocessed signal data;

[0070] The signal decomposition module is used to decompose the preprocessed signal data using the MEMD algorithm and filter the decomposed components to obtain the final decomposition result;

[0071] A signal source estimation module, for estimating the total number of signal sources of the preprocessed signal data using the MDL criterion;

[0072] The signal separation module is used to separate the oil and gas pipeline pulsed eddy current response signal and electromagnetic noise from the final decomposition result based on the estimated total number of signal sources using the FastICA independent component analysis algorithm.

[0073] To achieve the above-mentioned objectives, the third aspect of the present invention proposes a computer device, a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the FastICA-based oil and gas pipeline pulse eddy current response signal denoising method is implemented.

[0074] In order to achieve the above-mentioned objectives, the fourth aspect embodiment of the present invention proposes a non-temporary computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor, can execute the above-mentioned FastICA-based oil and gas pipeline pulse eddy current response signal denoising method.

[0075] The FastICA-based oil and gas pipeline pulsed eddy current response signal denoising method and device of the present embodiment uses an FIR adaptive filtering algorithm to preprocess the collected oil and gas pipeline pulsed eddy current response signal and electromagnetic noise; uses the MEMD algorithm to decompose the preprocessed signal and noise; filters the components generated by the MEMD algorithm decomposition; uses the MDL criterion to estimate the total number of signal and electromagnetic noise sources; and uses the FastICA independent component analysis algorithm to complete the separation of the signal and electromagnetic noise based on the estimated total number of signal and electromagnetic noise sources. This embodiment achieves denoising of the oil and gas pipeline pulsed eddy current response signal by introducing FastICA independent component analysis into the blind separation of oil and gas pipeline pulsed eddy current response and electromagnetic noise.

[0076] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0078] Figure 1 A flowchart of a FastICA-based method for denoising pulsed eddy current response signals of oil and gas pipelines provided in Example 1 of the present application;

[0079] Figure 2 A schematic diagram of the structure of a FastICA-based oil and gas pipeline pulse eddy current response signal denoising device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0080] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0081] The following describes, with reference to the accompanying drawings, a FastICA-based method and apparatus for denoising an oil and gas pipeline pulse eddy current response signal according to an embodiment of the present application.

[0082] Figure 1 A flow chart of a FastICA-based method for denoising pulsed eddy current response signals of oil and gas pipelines provided in Example 1 of the present application.

[0083] like Figure 1 As shown in FIG, the FastICA-based oil and gas pipeline pulsed eddy current response signal denoising method includes the following steps:

[0084] Step 101: collecting signals generated when pulsed eddy current testing is performed on an oil and gas pipeline to obtain raw signal data, wherein the raw signal data includes the pulsed eddy current response signal and electromagnetic noise of the oil and gas pipeline;

[0085] In this embodiment, the sensor used to collect the pulsed eddy current response signal and electromagnetic noise of the oil and gas pipeline is a dual-excitation, dual-receiving focused pulsed eddy current probe.

[0086] Step 102: preprocessing the collected original signal data using an FIR adaptive filtering algorithm to obtain preprocessed signal data;

[0087] In this embodiment, the specific steps of the FIR adaptive filtering algorithm are:

[0088] (1) Calculate the filter output y(n):

[0089]

[0090] Among them, x(n) is the input signal of each channel, w i (n) is the i-th weight coefficient of the filter at time n, and M is the order of the filter;

[0091] (2) Calculate the error signal e(n):

[0092] e(n)=d(n)-y(n)

[0093] Wherein, d(n) is the desired signal, specifically the pulsed eddy current response signal of the oil and gas pipeline collected under low noise conditions;

[0094] (3) Adjust the filter weight coefficient according to the error signal:

[0095] w i (n+1)=w i (n)+2μe(n)·x(ni)

[0096] Among them, μ is the filter step size parameter.

[0097] Step 103: Decompose the pre-processed signal data using the MEMD algorithm, and filter the decomposed components to obtain the final decomposition result;

[0098] In this embodiment, the specific steps of the MEMD algorithm are:

[0099] (1) Select l projection directions v in the two-dimensional space i ∈R 2 , i=1,2,...,l;

[0100] (2) Project the preprocessed dual-channel signal x[n] = [x1(n), x2(n)] to each direction v i :

[0101]

[0102] Among them, v i,m is the component of the i-th direction vector in the m-th dimension, is the projection signal in this direction;

[0103] (3) Perform one-dimensional discrete empirical mode decomposition (EMD) on the projection signal in each direction and decompose each projection signal into a series of intrinsic mode functions (IMFs):

[0104]

[0105] Where K is the number of decomposed IMFs, is the kth intrinsic mode function of the projection signal in this direction, is the residual;

[0106] (4) The kth IMF of each channel is obtained by geometrically averaging the kth IMF in each projection direction:

[0107]

[0108] Among them, the IMF k,m (n) is the kth IMF of the mth channel after reconstruction;

[0109] (5) Eliminate the slowest varying component, i.e., remove the slowest oscillating IMF decomposed. Repeat steps (2)-(4) for the remaining signal, continue decomposition, and repeat this iterative process j times;

[0110] (6) Obtain the final decomposition result, and calculate the signal x of each channel after preprocessing. m (n) There are:

[0111]

[0112] In this embodiment, the components generated by the MEMD algorithm decomposition are screened as follows:

[0113] (1) Calculate the kurtosis of each IMF series:

[0114]

[0115] Where N is the length of the IMF sequence, μ is the mean value of the IMF sequence, and σ is the standard deviation of the IMF sequence.

[0116] (2) Set the kurtosis threshold b and filter out the IMF sequences with kurtosis lower than b.

[0117] Step 104, estimating the total number of signal sources of the pre-processed signal data using the MDL criterion;

[0118] In this embodiment, the specific process of estimating the total number of signal and electromagnetic noise sources using the MDL criterion is as follows:

[0119] (1) Integrate the signals collected by the two channels into a 2×N data matrix X, where N is the length of the time domain signal collected by each channel;

[0120] (2) Take the mean of each row of the data matrix and get

[0121] (3) Calculate the covariance matrix:

[0122]

[0123] (4) Perform eigenvalue decomposition on R to obtain eigenvalues ​​λ1,λ2;

[0124] (5) Set the maximum possible number of information sources p, and traverse and calculate the MDL value of each information source number k:

[0125]

[0126] (6) Select the k value with the smallest calculated MDL, which is the estimated number of signal sources.

[0127] Step 105 : Separate the oil and gas pipeline pulsed eddy current response signal and electromagnetic noise from the final decomposition result using the FastICA independent component analysis algorithm based on the estimated total number of signal sources.

[0128] In this embodiment, the FastICA independent component analysis algorithm is used to separate the signal and electromagnetic noise. The specific process is as follows:

[0129] (1) Arrange the K IMFs of length N obtained by screening into a K×N matrix X;

[0130] (2) Center the matrix X, that is, take the mean of each row of the matrix to obtain the matrix

[0131] (3) For the matrix Perform whitening, specifically:

[0132] calculate Covariance matrix:

[0133]

[0134] Perform eigenvalue decomposition on C:

[0135] C=EDE T

[0136] Among them, E is the eigenvector matrix, D is the eigenvalue diagonal matrix,

[0137] Calculate the whitening matrix V:

[0138]

[0139] Complete the matrix Whitening:

[0140]

[0141] (4) Randomly initialize the unmixing matrix W, whose number of rows is the estimated signal source k;

[0142] (5) Iteratively update W:

[0143] W +=E[X W tanh(W T X W )]-E[tanh′(W T X W )]W

[0144]

[0145] (6) Repeat step (5) until W converges;

[0146] (7) Output independent component matrix:

[0147] S=WX w

[0148] Among them, S is the separated independent component matrix, and each row in the matrix corresponds to an independent oil and gas pipeline pulse eddy current response signal and noise from different sources.

[0149] The FastICA-based oil and gas pipeline pulsed eddy current response signal denoising method of the embodiment of the present application uses a dual-excitation, dual-receiving focused pulsed eddy current probe to collect the oil and gas pipeline pulsed eddy current response signal and electromagnetic noise at the same time; uses the FIR adaptive filtering algorithm to preprocess the collected oil and gas pipeline pulsed eddy current response signal and electromagnetic noise; uses the MEMD algorithm to decompose the preprocessed signal and noise, generating a total of 20 IMFs; filters the components generated by the MEMD algorithm decomposition, leaving 18 after filtering; uses the MDL criterion to estimate the total number of signal and electromagnetic noise sources, with an estimated value of 3; and uses the FastICA independent component analysis algorithm to complete the separation of the signal and electromagnetic noise based on the estimated total number of signal and electromagnetic noise sources. This embodiment achieves denoising of the oil and gas pipeline pulsed eddy current response signal by introducing FastICA independent component analysis into the blind separation of oil and gas pipeline pulsed eddy current response and electromagnetic noise.

[0150] In order to implement the above embodiments, the present application also proposes a FastICA-based oil and gas pipeline pulse eddy current response signal denoising device.

[0151] Figure 2 A schematic diagram of the structure of a FastICA-based oil and gas pipeline pulse eddy current response signal denoising device provided in an embodiment of the present application.

[0152] like Figure 2 As shown, the FastICA-based oil and gas pipeline pulse eddy current response signal denoising device includes:

[0153] The signal acquisition module is used to collect the signals generated when the pulsed eddy current test is performed on the oil and gas pipeline to obtain the original signal data, wherein the original signal data includes the pulsed eddy current response signal and electromagnetic noise of the oil and gas pipeline;

[0154] The signal preprocessing module is used to preprocess the collected original signal data using the FIR adaptive filtering algorithm to obtain preprocessed signal data;

[0155] The signal decomposition module is used to decompose the preprocessed signal data using the MEMD algorithm and filter the decomposed components to obtain the final decomposition result;

[0156] A signal source estimation module, for estimating the total number of signal sources of the preprocessed signal data using the MDL criterion;

[0157] The signal separation module is used to separate the oil and gas pipeline pulsed eddy current response signal and electromagnetic noise from the final decomposition result based on the estimated total number of signal sources using the FastICA independent component analysis algorithm.

[0158] It should be noted that the above explanation of the embodiment of the method for denoising the pulse eddy current response signal of an oil and gas pipeline based on FastICA is also applicable to the device for denoising the pulse eddy current response signal of an oil and gas pipeline based on FastICA in this embodiment, and will not be repeated here.

[0159] In order to implement the above embodiments, the present invention further proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in the above embodiments is implemented.

[0160] In order to implement the above embodiments, the present invention further proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method of the above embodiments is implemented.

[0161] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0162] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0163] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0164] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0165] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0166] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0167] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0168] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A FastICA-based method for denoising pulsed eddy current response signals in oil and gas pipelines, characterized in that: include: The sensor collects the signal generated when performing pulsed eddy current testing on the oil and gas pipeline to obtain raw signal data, wherein the sensor is a dual-excitation, dual-receiving focused pulsed eddy current probe, and the raw signal data includes the oil and gas pipeline pulsed eddy current response signal and electromagnetic noise; The collected original signal data is preprocessed using the FIR adaptive filtering algorithm to obtain preprocessed signal data; The pre-processed signal data is decomposed using the MEMD algorithm, and the decomposed components are screened to obtain the final decomposition result. The signal data is decomposed using the MEMD algorithm to obtain the decomposition result, including: Step S41: Select l projection directions v in the two-dimensional space j ∈R 2 , j=1,2,...,l; Step S42: Project the pre-processed dual-channel signal x[n]=[x1(n),x2(n)] to each direction v j : Among them, v j,m is the component of the j-th direction vector in the m-th dimension, is the projection signal in this direction; Step S43: Perform one-dimensional discrete empirical mode decomposition (EMD) on the projection signal in each direction, and decompose each projection signal into a series of intrinsic mode functions (IMFs): Where K is the number of decomposed IMFs, is the kth eigenmode function of the projection signal in this direction, is the residual; Step S44: reconstruct the kth IMF of each channel by taking the geometric mean of the kth IMF in each projection direction: Among them, the IMF k,m (n) is the kth IMF of the mth channel after reconstruction; Step S45: Iterate steps S42-S44 for t times. After each iteration, the slowest oscillating IMF decomposed is taken as the slowest changing component and is eliminated to obtain a decomposition result, wherein the decomposition result is: Among them, x m (n) is the signal for each channel; The decomposed components are screened to obtain the final decomposition result, including: Calculate the kurtosis of each IMF series: Where N is the length of the IMF sequence, μ is the mean value of the IMF sequence, and σ is the standard deviation of the IMF sequence; Set the kurtosis threshold b and filter out the IMF series with kurtosis lower than b; The MDL criterion is used to estimate the total number of signal sources of the preprocessed signal data, including: Integrate the preprocessed dual-channel signals into a 2×N data matrix X, where N is the length of the time domain signal collected for each channel; Take the mean of each row of the data matrix and get Compute the covariance matrix: Perform eigenvalue decomposition on R to obtain eigenvalues ​​λ1,λ2; Set the maximum number of signal sources P, and traverse and calculate the MDL value of each signal source number p: The p-value with the smallest calculated MDL is selected and used as the total number of estimated signal sources; Based on the estimated total number of signal sources, the FastICA independent component analysis algorithm is used to separate the oil and gas pipeline pulsed eddy current response signal and electromagnetic noise from the final decomposition result, including: Step S71: Arrange the K IMFs of length N obtained by screening into a K×N matrix A; Step S72: Center the matrix A. During the centering process, average each row of the matrix A to obtain the matrix Step S73: Matrix Perform whitening, including: calculation The covariance matrix C of C is decomposed into eigenvalues, and the whitening matrix V is calculated. The matrix is ​​then transformed based on the whitening matrix V. Whiten the matrix A W ,in, The covariance matrix C is: Perform eigenvalue decomposition on C, expressed as: C=EDE T Among them, E is the eigenvector matrix, D is the eigenvalue diagonal matrix, The whitening matrix V is: Matrix A W for: Step S74: randomly initialize the unmixing matrix W, whose number of rows is the estimated signal source p; Step S75: Iteratively update W until W converges. The update method is: W + =E[A W fishy(W T A W )]-E[tanh′(W T A W )]W Step S76: Based on the matrix W and the matrix A W Determine the independent component matrix, expressed as: S = WA W Among them, S is the separated independent component matrix, and each row in the matrix corresponds to an independent oil and gas pipeline pulse eddy current response signal and noise from different sources.

2. The method according to claim 1, wherein The filter output y(n) after preprocessing using the FIR adaptive filtering algorithm is: Among them, x(n) is the input signal of each channel, w i (n) is the i-th weight coefficient of the filter at time n, M is the order of the filter, The weight coefficients of the filter are adjusted by the error signal and are expressed as: w i (n+1)=w i (n)+2μe(n)·x(n-i) Where μ is the filter step size parameter and e(n) is the error signal, which is expressed as: e(n)=d(n)-y(n) Wherein, d(n) is the desired signal, specifically the pulsed eddy current response signal of the oil and gas pipeline collected under low noise conditions.

3. A FastICA-based denoising device for pulsed eddy current response signals in oil and gas pipelines, characterized in that: The device implements the method according to claim 1, and the device includes: A signal acquisition module is used to collect signals generated when pulsed eddy current testing is performed on oil and gas pipelines to obtain original signal data, wherein the original signal data includes the pulsed eddy current response signal and electromagnetic noise of the oil and gas pipelines; The signal preprocessing module is used to preprocess the collected original signal data using the FIR adaptive filtering algorithm to obtain preprocessed signal data; The signal decomposition module is used to decompose the preprocessed signal data using the MEMD algorithm and filter the decomposed components to obtain the final decomposition result; A signal source estimation module, for estimating the total number of signal sources of the preprocessed signal data using the MDL criterion; The signal separation module is used to separate the oil and gas pipeline pulse eddy current response signal and electromagnetic noise from the final decomposition result based on the estimated total number of signal sources using the FastICA independent component analysis algorithm.

4. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 2 is implemented.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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