FastICA-based oil and gas pipeline pulsed eddy current response signal denoising method and device

Abstract

The invention provides an oil and gas pipeline pulsed eddy current response signal denoising method based on FastICA, and the method comprises the steps: collecting a signal generated during the pulsed eddy current detection of an oil and gas pipeline, and obtaining original signal data which comprises an oil and gas pipeline pulsed eddy current response signal and electromagnetic noise; preprocessing the collected original signal data by using an FIR (Finite Impulse Response) adaptive filtering algorithm to obtain preprocessed signal data; decomposing the preprocessed signal data by using an MEMD algorithm, and screening decomposed components to obtain a final decomposition result; estimating the total number of signal sources of the preprocessed signal data by using an MDL criterion; and according to the estimated total number of signal sources, an oil and gas pipeline pulsed eddy current response signal and electromagnetic noise are separated from a final decomposition result by using a FastICA independent component analysis algorithm. By the adoption of the scheme, the detection precision can be remarkably improved, and a more reliable technical guarantee is provided for 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 pulse 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 the operation of pipelines are often difficult to observe directly, and non-destructive testing technology is needed for early warning and evaluation. Pulsed eddy current testing technology is widely used in non-destructive testing of oil and gas pipelines due to its sensitive and fast defect detection of conductive materials. However, the signals generated during pulsed eddy current testing are easily interfered by various factors, such as electromagnetic noise in the surrounding environment, power frequency interference signals, and nonlinear effects caused by the complexity of pipeline structures. These noise signals are mixed with effective defect signals, which seriously affects the accuracy of defect detection. Therefore, how to effectively suppress and remove noise signals becomes the key to improving detection accuracy.

[0003] In order to deal with these noise interferences, traditional noise suppression methods such as low-pass filtering, high-pass filtering, band-pass filtering and other frequency domain processing methods can weaken the noise in a specific frequency band to a certain extent, but these methods often rely on the frequency difference between noise and signal. If the frequency range of the noise signal overlaps with the effective signal, the effect of these methods will be significantly reduced, and it is impossible to accurately remove noise from different sources in the mixed signal. In addition, time-frequency analysis techniques such as short-time Fourier transform (STFT) and wavelet transform can decompose and analyze signals in the time-frequency domain, but these methods still rely on the difference in time-frequency characteristics of signals and noise, and have limited effect on noise removal in complex backgrounds, especially 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 some 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 an oil and gas pipeline, which solves the technical problem that the existing methods cannot accurately remove noise from different sources in the mixed signal. This application can effectively separate the target pulse turbine response signal, improve the detection accuracy of the oil and gas pipeline, and provide a more reliable technical guarantee for the safe monitoring of the oil and gas pipeline.

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

[0007] The third objective of the present 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 aspect of the present application proposes a FastICA-based method for denoising pulse eddy current response signals of oil and gas pipelines, comprising: collecting signals generated during pulse eddy current detection of oil and gas pipelines to obtain original signal data, wherein the original signal data includes pulse eddy current response signals of oil and gas pipelines and electromagnetic noise; preprocessing the collected original signal data using an FIR adaptive filtering algorithm to obtain preprocessed signal data; decomposing the preprocessed signal data using a MEMD algorithm, and screening the decomposed components to obtain a final decomposition result; estimating the total number of signal sources of the preprocessed signal data using the MDL criterion; and separating the oil and gas pipeline pulse eddy current response signals and electromagnetic noise from the final decomposition result using the FastICA independent component analysis algorithm based on the estimated total number of signal sources.

[0010] Optionally, in one embodiment of the present application, a signal generated when pulse eddy current testing is performed on an oil and gas pipeline is collected by a sensor, wherein the sensor is a dual-excitation, dual-receiving focused pulse 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] Among them, μ is the filter step size parameter, e(n) is the error signal, expressed as:

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

[0018] Wherein, d(n) is the expected signal, specifically the pulse 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 preprocessed 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] Among them, 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: The kth IMF of each channel is obtained by geometrically averaging and reconstructing the kth IMF in each projection direction:

[0028]

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

[0030] Step S45: Iterate steps S42-S44 for j times. After each iteration, the slowest oscillating IMF decomposed is taken as the slowest changing component and is removed 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] Compute 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 the IMF sequences with kurtosis lower than b.

[0038] Optionally, in one embodiment of the present application, the total number of signal sources of the preprocessed 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 by each channel;

[0040] Perform zone 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 information sources p, and traverse and calculate the MDL value of each information source number k:

[0045]

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

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

[0048] Step S71: Arrange the K IMFs with a length of 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, the whitening matrix V is calculated, and the matrix is ​​completed 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-mentioned purpose, the second embodiment of the present invention proposes a FastICA-based oil and gas pipeline pulse eddy current response signal denoising device, comprising:

[0068] The signal acquisition module is used to collect the signal generated when the pulse eddy current detection is performed on the oil and gas pipeline to obtain the original signal data, wherein the original signal data includes the pulse 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 screen the decomposed components to obtain the final decomposition result;

[0071] A signal source estimation module, used 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 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.

[0073] To achieve the above-mentioned purpose, 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 above-mentioned 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. When the instructions in the storage medium are executed by a processor, the above-mentioned FastICA-based oil and gas pipeline pulse eddy current response signal denoising method can be executed.

[0075] The FastICA-based oil and gas pipeline pulse eddy current response signal denoising method and device of the embodiment of the present application uses the FIR adaptive filtering algorithm to preprocess the collected oil and gas pipeline pulse eddy current response signal and electromagnetic noise; uses the MEMD algorithm to decompose the preprocessed signal and noise; screens 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 the denoising of the oil and gas pipeline pulse eddy current response signal by introducing the FastICA independent component analysis into the blind separation of the oil and gas pipeline pulse 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 the 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 schematic flow chart of a method for denoising a pulsed eddy current response signal of an oil and gas pipeline based on FastICA 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] Embodiments of the present application are described in detail below, and examples of the embodiments 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 a method and device for denoising an oil and gas pipeline pulse eddy current response signal based on FastICA according to an embodiment of the present application with reference to the accompanying drawings.

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

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

[0084] Step 101, collecting signals generated when pulse eddy current testing is performed on oil and gas pipelines to obtain original signal data, wherein the original signal data includes pulse eddy current response signals and electromagnetic noise of the oil and gas pipelines;

[0085] In this embodiment, the sensor used to collect the pulse eddy current response signal and electromagnetic noise of the oil and gas pipeline is a dual-excitation, dual-receiving focused pulse 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 expected signal, specifically the pulse eddy current response signal of the oil and gas pipeline collected under low noise conditions;

[0094] (3) Adjust the filter weight coefficients 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, using the MEMD algorithm to decompose the preprocessed signal data, and screening the decomposed components to obtain a final decomposition result;

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

[0099] (1) Select l projection directions v in 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] Among them, 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, IMF k,m (n) is the kth IMF of the mth channel after reconstruction;

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

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

[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, using the MDL criterion to estimate the total number of signal sources of the preprocessed signal data;

[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 , separating the oil and gas pipeline pulse eddy current response signal and the electromagnetic noise from the final decomposition result using the FastICA independent component analysis algorithm according to 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 pulse eddy current response signal denoising method of the embodiment of the present application uses a dual-excitation, dual-receiving focused pulse eddy current probe to collect the oil and gas pipeline pulse 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 pulse eddy current response signal and electromagnetic noise; uses the MEMD algorithm to decompose the preprocessed signal and noise, and a total of 20 IMFs are generated; the components generated by the MEMD algorithm decomposition are screened, and 18 remain after screening; the total number of signal and electromagnetic noise sources is estimated using the MDL criterion, and the estimated value is 3; and the FastICA independent component analysis algorithm is used to complete the separation of the signal and electromagnetic noise based on the estimated total number of signal and electromagnetic noise sources. This embodiment realizes the denoising of the oil and gas pipeline pulse eddy current response signal by introducing FastICA independent component analysis into the blind separation of oil and gas pipeline pulse eddy current response and electromagnetic noise.

[0150] In order to implement the above-mentioned embodiment, 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 signal generated when the pulse eddy current detection is performed on the oil and gas pipeline to obtain the original signal data, wherein the original signal data includes the pulse 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 screen the decomposed components to obtain the final decomposition result;

[0156] A signal source estimation module, used 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 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.

[0158] It should be noted that the above explanation of the embodiment of the oil and gas pipeline pulse eddy current response signal denoising method based on FastICA is also applicable to the oil and gas pipeline pulse eddy current response signal denoising device 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" etc. 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 representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0162] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0163] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment 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 not be performed in 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 technicians in the technical field to which the embodiments of the present application belong.

[0164] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, 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 in other suitable ways if necessary, and then stored in a computer memory.

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

[0166] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0167] In addition, each functional unit in each embodiment 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 one 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 can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A FastICA-based method for denoising pulsed eddy current response signals of oil and gas pipelines, characterized in that: include: Collecting signals generated when pulse eddy current testing is performed on oil and gas pipelines to obtain original signal data, wherein the original signal data includes pulse eddy current response signals and electromagnetic noise of the oil and gas pipelines; The collected original signal data is preprocessed using the FIR adaptive filtering algorithm to obtain preprocessed signal data; Use the MEMD algorithm to decompose the preprocessed signal data, and filter the decomposed components to obtain the final decomposition result; The total number of signal sources of the preprocessed signal data is estimated using the MDL criterion; According to the estimated total number of signal sources, the pulsed eddy current response signal and electromagnetic noise of the oil and gas pipeline are separated from the final decomposition result using the FastICA independent component analysis algorithm.

2. The method according to claim 1, characterized in that The signal generated when pulse eddy current detection is performed on the oil and gas pipeline is collected by a sensor, wherein the sensor is a dual-excitation, dual-receiving focused pulse eddy current probe.

3. The method according to claim 1, characterized in that 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) Among them, μ is the filter step size parameter, e(n) is the error signal, expressed as: e(n)=d(n)-y(n) Wherein, d(n) is the expected signal, specifically the pulse eddy current response signal of the oil and gas pipeline collected under low noise conditions.

4. The method according to claim 1, characterized in that Use the MEMD algorithm to decompose the signal data and obtain the decomposition results, including: Step S41: Select l projection directions v in the two-dimensional space i ∈R 2 , i=1,2,...,l; Step S42: Project the preprocessed dual-channel signal x[n]=[x1(n), x2(n)] to each direction v i : 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; 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 intrinsic mode function of the projection signal in this direction, is the residual; Step S44: The kth IMF of each channel is obtained by geometrically averaging and reconstructing the kth IMF in each projection direction: Among them, IMF k,m (n) is the kth IMF of the mth channel after reconstruction; Step S45: Iterate steps S42-S44 for j times. After each iteration, the slowest oscillating IMF decomposed is taken as the slowest changing component and is removed to obtain a decomposition result, wherein the decomposition result is: Among them, x m (n) is the signal of each channel.

5. The method according to claim 4, characterized in that The decomposed components are screened to obtain the final decomposition result, including: Compute 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 sequences with kurtosis lower than b.

6. The method according to claim 1, characterized in that The method of estimating the total number of signal sources of the preprocessed signal data using the MDL criterion includes: Integrate the preprocessed dual-channel signals into a 2×N data matrix X, where N is the length of the time domain signal collected by each channel; Perform zone mean processing on each row of the data matrix to obtain Compute the covariance matrix: Perform eigenvalue decomposition on R to obtain eigenvalues ​​λ1,λ2; Set the maximum number of information sources p, and traverse and calculate the MDL value of each information source number k: The k value with the smallest calculated MDL is selected and used as the total number of estimated signal sources.

7. The method according to any one of claims 5-6, characterized in that: The method of separating 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 includes: Step S71: Arrange the K IMFs with a length of N obtained by screening into a K×N matrix X; Step S72: Center the matrix X. During the centering process, average each row of the matrix X to obtain the matrix Step S73: Matrix Perform whitening, including: calculation The covariance matrix C of C is decomposed into eigenvalues, the whitening matrix V is calculated, and the matrix is ​​completed based on the whitening matrix V Whiten the matrix X 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 X W for: Step S74: randomly initialize the unmixing matrix W, whose number of rows is the estimated signal source k; Step S75: iteratively update W until W converges. The update method is: W + =E[X W tanh(W T X W )]-E[tanh′(W T X W )]W Step S76: Based on the matrix W and the matrix X W Determine the independent component matrix, expressed as: S=WX 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.

8. A FastICA-based oil and gas pipeline pulse eddy current response signal denoising device, characterized in that: include: A signal acquisition module is used to acquire signals generated when pulse eddy current testing is performed on oil and gas pipelines to obtain original signal data, wherein the original signal data includes pulse eddy current response signals 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 screen the decomposed components to obtain the final decomposition result; A signal source estimation module, used 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 according to the estimated total number of signal sources using the FastICA independent component analysis algorithm.

9. 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 7 is implemented.

10. 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 7 is implemented.

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