Pipeline eddy current damage positioning method, system, medium and computer device

Abstract

This invention belongs to the field of pipeline eddy current damage localization technology. It proposes a method, system, medium, and computer equipment for pipeline eddy current damage localization. The method involves acquiring multi-channel original discrete signals from the pipeline, performing mean-reduction processing, and determining the dominant frequency. Based on the dominant frequency, an optimal bandwidth parameter is adaptively determined from a candidate bandwidth set using a kurtosis maximization criterion. A complex Moray wavelet function is constructed using the dominant frequency and the optimal bandwidth parameter, and a continuous wavelet transform is performed on the signal after removing the DC component to generate a multi-channel time-frequency distribution matrix. A frequency-weighted operator is applied to this time-frequency distribution matrix for energy enhancement, resulting in a multi-channel weighted energy map. Time-frequency coherent fusion is performed on the multi-channel weighted energy map, and a global energy peak is searched in the fused energy field. The coordinates corresponding to this peak are determined as the pipeline damage location. This invention effectively improves the accuracy and robustness of damage localization.

Description

Technical Field

[0001] This invention relates to the field of pipeline eddy current damage location technology, specifically to a pipeline eddy current damage location method, system, medium, and computer equipment. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As a critical infrastructure for energy transmission, the structural integrity of pipelines is vital to industrial production and public safety. Eddy current testing technology, due to its non-contact and high-sensitivity characteristics, is widely used in the field of non-destructive testing of pipeline damage. This technology induces eddy currents in the conductive pipe wall by exciting a coil. When defects such as cracks or corrosion exist in the pipe wall, they disturb the eddy current field, thereby generating identifiable signal changes in the detection coil. To effectively analyze these complex signals caused by damage, time-frequency analysis methods, especially wavelet transform, have become important signal processing tools because they can simultaneously provide time and frequency domain information, laying the technical foundation for accurately extracting damage features.

[0004] However, existing wavelet transform-based methods for locating pipeline eddy current damage have a core problem: the parameters of the wavelet basis functions (such as bandwidth) are usually preset based on human experience and cannot be adaptively adjusted according to the characteristics of the actual acquired multi-channel signals. This fixed parameter setting makes it difficult to take into account the time-frequency resolution of signals under different operating conditions, resulting in weak damage features being out of focus in time-frequency mapping. At the same time, the high-frequency transient signals caused by damage have weak energy in conventional time-frequency characterization and are easily submerged by low-frequency background noise, making it difficult to accurately and reliably fuse and locate the unique damage location coordinates from the multi-channel time-frequency distribution. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method, system, medium, and computer equipment for locating pipeline eddy current damage. By utilizing multi-channel sensors in an internal detection system, based on fast Fourier transform and wavelet basis parameter optimization, and combined with frequency-weighted energy mapping technology, the global maximum coordinates in the energy field are extracted, thereby achieving precise location of pipeline damage.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for locating pipeline eddy current damage.

[0007] A method for locating eddy current damage in pipelines includes the following steps: Acquire the original discrete signals from multiple channels of the pipeline; The original discrete multi-channel signal is processed to remove the DC component, resulting in a multi-channel signal after removing the DC component. A fast Fourier transform is performed on the multi-channel signal after removing the DC component to obtain the signal spectrum distribution and determine the dominant frequency. Based on the signal spectral distribution and dominant frequency, the optimal bandwidth parameter is determined from the bandwidth candidate set by maximizing the kurtosis evaluation criterion; Based on the dominant frequency and optimal bandwidth parameters, a complex Moray wavelet function is constructed. Combining the complex Moray wavelet function, a continuous wavelet transform is performed on the multi-channel signal after removing the DC component to generate a multi-channel time-frequency distribution matrix. Energy enhancement is performed by applying a frequency weighting operator to the multi-channel time-frequency distribution matrix to obtain a multi-channel weighted energy map. Time-frequency coherent fusion is performed based on multi-channel weighted energy maps, and the global energy peak is searched in the fused energy field. The coordinates corresponding to the global energy peak are determined as the pipeline damage location.

[0008] In one implementation of the first aspect of the present invention, the DC component removal process for the multi-channel original discrete signal includes: Calculate the mean of the original discrete signal for each channel; The DC component-free multi-channel signal is obtained by subtracting the corresponding mean from the original discrete signal of each channel.

[0009] In one implementation of the first aspect of the present invention, the frequency component with the largest amplitude is found in the signal spectrum distribution, and the frequency corresponding to the frequency component with the largest amplitude is determined as the dominant frequency.

[0010] In one implementation of the first aspect of the present invention, determining the optimal bandwidth parameter from the bandwidth candidate set by maximizing the kurtosis evaluation criterion includes: For each candidate bandwidth in the bandwidth candidate set, calculate the kurtosis value of the corresponding wavelet coefficient amplitude; Choose the candidate bandwidth that maximizes the kurtosis value as the optimal bandwidth parameter.

[0011] As a further limitation of the first aspect of the present invention, calculating the kurtosis value of the corresponding wavelet coefficient amplitude includes: Obtain the amplitude of wavelet coefficients after wavelet transform under a certain candidate bandwidth; The mean of the wavelet coefficient amplitudes obtained from the calculation; The standard deviation of the amplitude of the wavelet coefficients is calculated, and the fourth power of the standard deviation is used as the denominator. The deviation is obtained by subtracting the mean value of the wavelet coefficient amplitude from the wavelet coefficient amplitude. The expected value of the fourth power of the obtained deviation is then calculated, and finally divided by the fourth power of the standard deviation to obtain the kurtosis value.

[0012] In one implementation of the first aspect of the present invention, a multi-channel time-frequency distribution matrix is ​​generated, and a frequency-weighted operator is applied to the multi-channel time-frequency distribution matrix for energy enhancement to obtain a multi-channel weighted energy map, including: Set the frequency scanning range according to the dominant frequency; The constructed complex Molay wavelet function is used to perform integration on the multi-channel signal after removing the DC component, and a multi-channel time-frequency distribution matrix describing the signal distribution in the time and frequency domains is obtained. The weighted energy map is calculated based on the multi-channel time-frequency distribution matrix and the current frequency.

[0013] In one implementation of the first aspect of the present invention, the weighted energy map of each channel is normalized to obtain a normalized multi-channel energy map. The normalized multi-channel energy maps are fused using a geometric average method to construct a fused energy distribution map; Find the point with the highest energy value in the merged energy distribution map; The time and frequency corresponding to the point with the highest energy value are respectively determined as the characteristic moment and characteristic resonant frequency of the damage occurrence.

[0014] Secondly, the present invention provides a pipeline eddy current damage location system.

[0015] A pipeline eddy current damage location system, comprising: The signal acquisition unit is configured to acquire the multi-channel raw discrete signals of the pipeline; The signal preprocessing unit is configured to: perform DC component removal processing on the original multi-channel discrete signal to obtain the multi-channel signal after DC component removal; The spectrum analysis unit is configured to perform a fast Fourier transform on the multi-channel signal after removing the DC component, obtain the signal spectrum distribution, and determine the dominant frequency. The parameter optimization unit is configured to determine the optimal bandwidth parameters from the bandwidth candidate set based on the signal spectral distribution and dominant frequency by maximizing the kurtosis evaluation criterion. The time-frequency mapping unit is configured to: construct a complex Moray wavelet function based on the dominant frequency and the optimal bandwidth parameter; combine the complex Moray wavelet function to perform a continuous wavelet transform on the multi-channel signal after removing the DC component, and generate a multi-channel time-frequency distribution matrix. The energy enhancement unit is configured to apply a frequency weighting operator to the multi-channel time-frequency distribution matrix to enhance the energy and obtain a multi-channel weighted energy map. The damage localization unit is configured to: perform time-frequency coherent fusion based on a multi-channel weighted energy map, search for a global energy peak in the fused energy field, and determine the coordinates corresponding to the global energy peak as the pipeline damage location.

[0016] Thirdly, the present invention provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by the pipe eddy current damage localization method of the first aspect of the present invention.

[0017] Fourthly, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the pipe eddy current damage localization method of the first aspect of the present invention.

[0018] Compared with the prior art, the beneficial effects of the present invention are: To address the challenge of extracting and locating weak damage signals in pipeline eddy current testing, this invention proposes a pipeline eddy current damage localization method based on adaptive parameter optimization and weighted time-frequency mapping. This method utilizes multi-channel sensors to acquire raw signals and performs zero-drift suppression. Wavelet parameters are adaptively optimized based on the principle of maximizing kurtosis. Through refined time-frequency mapping and nonlinear scale compensation, the weak damage disturbance characteristics in the high-frequency band are effectively amplified. Combined with an energy mapping localization algorithm, the method achieves accurate identification of pipeline damage locations. This invention enhances the algorithm's adaptability in complex inspection environments and improves the reliability of pipeline inspection operations.

[0019] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 A schematic flowchart of a pipeline eddy current damage localization method provided as an exemplary embodiment of the present invention; Figure 2 A schematic diagram of the structure of a pipeline eddy current detection system provided as an exemplary embodiment of the present invention; Figure 3 A schematic diagram of different channel signals acquired as an exemplary embodiment of the present invention is provided, wherein, Figure 3 (a) in the diagram is a schematic diagram of the signal in channel 1. Figure 3 (b) in the diagram is a schematic diagram of the signal in channel 2; Figure 4 A schematic diagram of different channel signals after Fast Fourier Transform is provided as an exemplary embodiment of the present invention, wherein, Figure 4(a) in the diagram is a schematic diagram of the signal of channel 1 after the Fast Fourier Transform. Figure 4 (b) in the diagram is a schematic diagram of the signal of channel 2 after the Fast Fourier Transform; Figure 5 This is a schematic diagram illustrating kurtosis-based wavelet transform parameter optimization for different channel signals, provided as an exemplary embodiment of the present invention. Figure 5 (a) in the diagram is a schematic diagram of the optimized wavelet transform parameters for channel 1 after the Fast Fourier Transform. Figure 5 (b) in the diagram is a schematic diagram of the wavelet transform parameter optimization for channel 2 after the fast Fourier transform; Figure 6 The wavelet transform time-frequency distribution of different channel signals under optimal parameters is provided as an exemplary embodiment of the present invention, wherein, Figure 6 In the diagram, (a) shows the wavelet transform time-frequency distribution of channel 1 with optimal parameters after the Fast Fourier Transform. Figure 6 (b) in the figure represents the wavelet transform time-frequency distribution of channel 2 with optimal parameters after the Fast Fourier Transform; Figure 7 Weighted energy distribution of different channel signals provided as an exemplary embodiment of the present invention. Figure 7 In the diagram, (a) shows the weighted energy distribution of channel 1 after the Fast Fourier Transform. Figure 7 (b) in the figure represents the weighted energy distribution of channel 2 after the Fast Fourier Transform; Figure 8 Example imaging results of different channel signals provided as an exemplary embodiment of the present invention; Figure 9 A schematic diagram illustrating the principle of a pipeline eddy current damage localization method provided as an exemplary embodiment of the present invention; Figure 10 A schematic diagram of a pipeline eddy current damage localization system provided as an exemplary embodiment of the present invention; Figure 11 A schematic diagram of a computer device provided for an exemplary embodiment of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] Currently, non-destructive testing (NDT) methods for pipeline structures mainly include X-ray inspection, ultrasonic testing, and eddy current testing. Among these, eddy current testing technology has become a research focus in this field due to its potential for high-precision identification of early defects. The principle of eddy current testing is as follows: an alternating magnetic field generated by an excitation coil induces eddy currents in the conductive pipe wall. When damage such as cracks or corrosion thinning exists in the pipe wall, the distribution pattern of the eddy currents changes, thereby causing a change in the output signal of the detection coil. By extracting the characteristic shifts caused by damage in the induced signal, structural damage can be identified and located.

[0025] In actual pipeline inspection operations, the induced current disturbance signal caused by pipeline damage exhibits typical non-stationary and transient characteristics. Traditional single-frequency domain analysis methods, while capable of extracting frequency components, completely lose the temporal location information of the damage. Conventional time-domain feature extraction methods lack the ability to characterize frequency details and struggle to balance time and frequency resolution under high sampling rates, resulting in insufficient sensitivity to detect minute defects and significant spatial biases in damage localization. Furthermore, existing multidimensional signal processing techniques often rely on preset fixed parameters, lacking the ability to adaptively match varying pipeline operating conditions.

[0026] Wavelet transform, as an advanced time-frequency analysis technique, possesses multi-resolution analysis capabilities, making it particularly suitable for processing pipeline damage signals with non-stationary characteristics. By mapping the time-domain signal to the time-frequency plane, damage features hidden beneath complex background noise can be extracted. However, traditional wavelet transform applications rely on manual experience to select basis function parameters. Inappropriate parameter settings can lead to a loss of focus in time-frequency resolution, failing to accurately capture feature shifts caused by subtle damage. Furthermore, disturbance signals caused by pipeline damage often exhibit energy attenuation at high frequencies, making it difficult for conventional time-frequency characterization methods to accurately extract damage locations.

[0027] This invention addresses the challenge of extracting and locating weak damage signals in pipeline eddy current testing. It discloses a pipeline eddy current damage localization method based on adaptive parameter optimization and weighted time-frequency mapping. First, the algorithm automatically searches and locks the optimal bandwidth parameters within the parameter space based on the time-domain characteristics of the acquired signals. Second, by amplifying the weak damage components in the high-frequency band using a frequency-weighted operator, it solves the problem of small defect features being easily submerged by low-frequency background energy, significantly improving the sensitivity for identifying early-stage weak damage. Furthermore, by constructing a high-resolution weighted energy time-frequency matrix, this invention can accurately pinpoint the instantaneous location of the damage, reducing spatial positioning errors and providing reliable data support for pipeline maintenance. Figure 1As shown, the system first acquires signals through an eddy current detection system. After receiving the signals through multiple channels, the data undergoes preprocessing before entering the subsequent analysis module. The preprocessed data is used to identify the dominant frequency. This step adaptively determines the optimal wavelet basis bandwidth based on the kurtosis maximization search evaluation criterion. Then, adaptive optimization of the bandwidth parameters is performed, followed by wavelet transform time-frequency mapping to generate a time-frequency distribution. Based on this, combined with energy distribution analysis, frequency weighting is applied to the time-frequency matrix to enhance high-frequency damage characteristics, ultimately achieving pipeline eddy current damage localization. The entire process embodies a closed-loop analysis logic from data acquisition, feature extraction, parameter optimization to precise damage localization.

[0028] Specifically, it includes the following: This disclosure provides a method for locating eddy current damage in pipelines based on adaptive parameter optimization and weighted time-frequency mapping. The implementation steps are as follows: Step S1: Construct an eddy current detection signal acquisition system.

[0029] The first step of this invention is to locate pipeline damage by constructing an eddy current detection signal acquisition system.

[0030] Step S101: Construct an experimental system consisting of the pipe under test, an eddy current probe, a signal excitation source, a power amplifier, a multi-channel synchronous acquisition card, and a host computer, such as... Figure 2 As shown. The eddy current probe integrates an excitation coil and an array of detection coils. The excitation coil generates an alternating magnetic field, which induces a secondary magnetic field that varies with the structural characteristics within the wall of the pipe being tested. Step S102: On a 9.5 mm thick Q235B carbon steel experimental pipe, a detection area is defined, and an array of detection coils is arranged within this area. The detection coils are symmetrically arranged along the circumference of the pipe to ensure comprehensive monitoring of the pipe's structural condition throughout its entire circumference. In the experiment, the local electromagnetic conductivity is altered by pre-machining grooves of different depths in the pipe wall, thereby simulating localized corrosion thinning or crack damage in the pipe. Figure 3 The system displays signals collected from different channels, with the dashed lines corresponding to signal changes when passing through the damaged section of the pipeline.

[0031] Step S2: Signal preprocessing to remove DC component.

[0032] Step S2 of this invention preprocesses the original signal to ensure that damage detection of the pipeline structure can improve the accuracy of frequency identification. Before frequency detection, the original discrete signal x[n] of the pipeline structure is first acquired by a sensor. Next, the acquired original discrete signal x[n] is subjected to mean-reduction processing to eliminate the influence of zero drift and sensor static offset, as expressed by: (1); in, It is the total number of sampling points of the signal. The mean of the original signal. x[i] is the signal value of the nth sampling point after removing the DC component, and x[i] is the ith original discrete signal.

[0033] Step S3: Frequency distribution analysis and main frequency confirmation.

[0034] In step S3 of this invention, a Fast Fourier Transform is performed on the signal processed in step S2. Through coarse frequency domain probing and dominant frequency locking, the dominant frequency of the signal is identified. The result of the Fast Fourier Transform is as follows: Figure 4 As shown, the dominant frequency is determined based on the maximum frequency component.

[0035] Step S301: After removing the DC component from the signal and ensuring time-domain symmetry, the signal is first transformed into the frequency domain. The processed signal y[n] is then analyzed using a Fast Fourier Transform, and the calculation formula is as follows: (2); in, It is a frequency index. This represents the signal in the frequency domain. Through Fast Fourier Transform, the time-domain information of the original signal y[n] is converted into frequency-domain information, yielding the signal's spectral distribution.

[0036] Step S302: In the frequency domain, the dominant frequency of the signal is the frequency component with the largest amplitude. The expression for accurately identifying this frequency is as follows: (3); in, As the dominant frequency, This represents the calculated signal spectrum amplitude value. By finding the frequency component with the largest amplitude in the spectrum, the dominant frequency is automatically locked. , f Represents frequency.

[0037] Step S4: Bandwidth parameters Adaptive optimization.

[0038] The fourth step of this invention is the parameter optimization process for the subsequent wavelet transform step, which optimizes the wavelet basis bandwidth parameter by maximizing the kurtosis evaluation criterion. Adaptive optimization is performed to improve the time-frequency resolution and transient feature extraction capability of the signal.

[0039] Step S401: Kurtosis is a statistical measure of the sharpness of a signal, often used to characterize transient impulses in a signal. For each candidate library... Each bandwidth value Calculate its corresponding wavelet coefficients Kurtosis is used as an evaluation metric: (4); in, This represents the expectation operation. The amplitude of the wavelet coefficients. The mean of the coefficient amplitudes, The standard deviation of the coefficient amplitude. Through kurtosis. It can quantify the ability of different bandwidth parameters to characterize the transient features of a signal. The greater the kurtosis, the stronger the wavelet's ability to focus on the impulse component.

[0040] Step S402: Based on the above evaluation criteria, adaptive optimization of the bandwidth parameters is performed. This is done by calculating all candidate bandwidths. kurtosis value Choose the bandwidth that maximizes the kurtosis as the optimal bandwidth: (5); in, For bandwidth candidate set, The optimal bandwidth parameter is determined. This process achieves adaptive bandwidth optimization based on the kurtosis maximization criterion, enabling the transient impairment characteristics of the signal to obtain the best time-frequency resolution. The parameter optimization process is as follows: Figure 5 As shown, the highest point corresponds to Defined as the optimal bandwidth of wavelet transform.

[0041] Step S403: Obtain the optimal bandwidth parameters Then, the signal main frequency identified in the third step of the fast Fourier transform is combined with... Construct a family of complex Moray wavelets, expressed as: (6); in, For time variables, For the main frequency of the signal, For bandwidth parameters, For bandwidth-adjustable complex Moray wavelet functions. Based on optimal bandwidth parameters. The constructed wavelet basis is used for subsequent wavelet transform analysis.

[0042] Step S5: Time-frequency mapping based on wavelet transform.

[0043] The fifth step of this invention is to refine the time-frequency mapping. The optimal parameters obtained in the fourth step are used to perform a full-scale wavelet transform, and a high-resolution time-frequency distribution is obtained by calculating the time-frequency matrix.

[0044] Step S501: Based on the optimal bandwidth obtained in step S4 and the main frequency obtained in step S3 Select the most suitable complex Molay wavelet function for time-frequency decomposition and use the optimal wavelet parameters. This wavelet function can provide high-resolution analysis in the time and frequency domain, making it suitable for capturing transient features in signals.

[0045] Step S502: To ensure coverage of the main frequency components of the signal and capture of damage characteristics, set the frequency scanning range. ; Step S503: Use complex Moray wavelets to perform time-frequency decomposition and calculate the first... Time-frequency matrix of channel signal This matrix describes the distribution of the signal in the time and frequency domains. The formula for calculating the time-frequency matrix is: (7); in, The signal has undergone preprocessing. For constructing complex Moray wavelets using optimal parameters, It is a scale mapping vector. For time shift parameters, It is the conjugate of the complex Moray wavelet. Through integration, the signal distribution in the time-frequency domain can be obtained, i.e., the time-frequency matrix. ,like Figure 6 As shown.

[0046] Step S6: Weighted energy enhancement and feature localization.

[0047] Step S6 of the present invention corrects the energy attenuation of wavelet transform in the high-frequency band by frequency linear weighted mapping, thereby highlighting the weak damage characteristics.

[0048] Step S601: To compensate for the energy attenuation of the signal in the high-frequency band, a frequency linear weighted mapping is used to enhance the energy of the high-frequency components. The calculation formula for the weighted mapping is as follows: (8); in, This represents a weighted energy diagram. It is the channel number. It's frequency. It is time. It is a time-frequency matrix.

[0049] Step S602: Frequency With the amplitude of the time-frequency matrix Multiplication can increase the energy in the high-frequency range, especially in the detection of weak signals in the high-frequency band, thereby highlighting weak damage characteristics. Figure 7 The frequency-weighted channel signal energy distribution is shown.

[0050] Step S7: Time-frequency coherent fusion.

[0051] Step S7 of the present invention constructs the fused energy distribution map using geometric averaging. The expression is: Step S701: Align the normalized time-frequency energy distributions of the two detection channels and construct the fused energy distribution map using a geometric mean fusion method: (9); in, yes Normalized energy diagram; yes Normalized energy diagram; This is the weighted energy graph of channel 1. This is the weighted energy diagram of channel 2.

[0052] Step S702: Damage feature extraction and localization analysis are performed in the fused time-frequency energy matrix. The characteristic time and frequency of damage occurrence are determined by searching for global energy peaks. The decision-making process can be represented as follows: (10); in, This is the maximum value point in the weighted energy graph. It is the frequency corresponding to that point. This corresponds to the exact time. The maximum value corresponds to the precise moment when structural damage or anomalies occurred. and the characteristic resonance frequency excited by damage .

[0053] Step S703: Define the coordinates of the location of the maximum value as the damage location obtained by the algorithm. Example imaging results are as follows: Figure 8 As shown, the complete principle of this algorithm is illustrated in the diagram below. Figure 9 As shown: The pipeline eddy current detection system acquires multi-channel signals (channel signal 1 and channel signal 2) through signal acquisition, obtains the spectral distribution through Fourier transform, and then extracts the dominant frequency. and And combined with the optional parameter library , Perform kurtosis analysis and output the optimal bandwidth. and Subsequently, wavelet transforms were performed on the two channels of signals, and the weighted energy was calculated. Finally, the two weighted energies were fused to achieve damage localization.

[0054] Figure 10 A pipe eddy current damage localization system is shown, comprising: The signal acquisition unit 1001 is configured to acquire the multi-channel raw discrete signals of the pipeline; The signal preprocessing unit 1002 is configured to: perform DC component removal processing on the original multi-channel discrete signal to obtain the multi-channel signal after DC component removal; The spectrum analysis unit 1003 is configured to perform a fast Fourier transform on the multi-channel signal after removing the DC component, obtain the signal spectrum distribution, and determine the dominant frequency. The parameter optimization unit 1004 is configured to determine the optimal bandwidth parameter from the bandwidth candidate set based on the signal spectrum distribution and dominant frequency by maximizing the kurtosis evaluation criterion. The time-frequency mapping unit 1005 is configured to: construct a complex Moray wavelet function based on the dominant frequency and the optimal bandwidth parameter; combine the complex Moray wavelet function to perform a continuous wavelet transform on the multi-channel signal after removing the DC component, and generate a multi-channel time-frequency distribution matrix. The energy enhancement unit 1006 is configured to apply a frequency weighting operator to the multi-channel time-frequency distribution matrix to enhance the energy and obtain a multi-channel weighted energy map. The damage localization unit 1007 is configured to: perform time-frequency coherent fusion based on the multi-channel weighted energy map, search for the global energy peak in the fused energy field, and determine the coordinates corresponding to the global energy peak as the pipeline damage location.

[0055] It is understood that the aforementioned units can be individually or entirely merged into one or more other units, or some of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effects of the embodiments of the present invention. The aforementioned units are based on logical functional division. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of the present invention, the system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0056] According to another embodiment of the present invention, the system of this embodiment can be constructed by running a computer program (including program code) capable of performing the steps involved in the corresponding method of the present invention on a general-purpose computing device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, loaded into the aforementioned computing device through the computer-readable recording medium, and run therein.

[0057] Figure 11 A computer device is shown, which includes a processor 1101, a communication interface 1102, and a computer-readable storage medium 1103. The processor 1101, communication interface 1102, and computer-readable storage medium 1103 can be connected via a bus or other means.

[0058] The communication interface 1102 is used to receive and send data. The computer-readable storage medium 1103 can be stored in the memory of the electronic device. The computer-readable storage medium 1103 is used to store computer programs, which include program instructions. The processor 1101 is used to execute the program instructions stored in the computer-readable storage medium 1103.

[0059] The processor 1101 is the computing and control core of the electronic device. It is suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to achieve the corresponding method flow or corresponding function.

[0060] Processor 1101 is configured to perform the following procedure: Acquire the original discrete signals from multiple channels of the pipeline; The original discrete multi-channel signal is processed to remove the DC component, resulting in a multi-channel signal after removing the DC component. A fast Fourier transform is performed on the multi-channel signal after removing the DC component to obtain the signal spectrum distribution and determine the dominant frequency. Based on the signal spectral distribution and dominant frequency, the optimal bandwidth parameter is determined from the bandwidth candidate set by maximizing the kurtosis evaluation criterion; Based on the dominant frequency and optimal bandwidth parameters, a complex Moray wavelet function is constructed. Combining the complex Moray wavelet function, a continuous wavelet transform is performed on the multi-channel signal after removing the DC component to generate a multi-channel time-frequency distribution matrix. Energy enhancement is performed by applying a frequency weighting operator to the multi-channel time-frequency distribution matrix to obtain a multi-channel weighted energy map. Time-frequency coherent fusion is performed based on multi-channel weighted energy maps, and the global energy peak is searched in the fused energy field. The coordinates corresponding to the global energy peak are determined as the pipeline damage location.

[0061] This invention also provides a computer-readable storage medium, which is a memory device in an electronic device for storing programs and data. It is understood that the computer-readable storage medium here may include both built-in storage media in the electronic device and extended storage media supported by the electronic device. The computer-readable storage medium provides storage space for storing the processing system of the electronic device.

[0062] Furthermore, this storage space also contains one or more instructions suitable for loading and execution by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory; alternatively, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0063] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to perform the following process: Acquire the original discrete signals from multiple channels of the pipeline; The original discrete multi-channel signal is processed to remove the DC component, resulting in a multi-channel signal after removing the DC component. A fast Fourier transform is performed on the multi-channel signal after removing the DC component to obtain the signal spectrum distribution and determine the dominant frequency. Based on the signal spectral distribution and dominant frequency, the optimal bandwidth parameter is determined from the bandwidth candidate set by maximizing the kurtosis evaluation criterion; Based on the dominant frequency and optimal bandwidth parameters, a complex Moray wavelet function is constructed. Combining the complex Moray wavelet function, a continuous wavelet transform is performed on the multi-channel signal after removing the DC component to generate a multi-channel time-frequency distribution matrix. Energy enhancement is performed by applying a frequency weighting operator to the multi-channel time-frequency distribution matrix to obtain a multi-channel weighted energy map. Time-frequency coherent fusion is performed based on multi-channel weighted energy maps, and the global energy peak is searched in the fused energy field. The coordinates corresponding to the global energy peak are determined as the pipeline damage location.

[0064] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can implement the described functions using different methods for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0065] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic cable, digital cable) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for locating eddy current damage in a pipeline, characterized in that, Includes the following processes: Acquire the original discrete signals from multiple channels of the pipeline; The original discrete multi-channel signal is processed to remove the DC component, resulting in a multi-channel signal after removing the DC component. A fast Fourier transform is performed on the multi-channel signal after removing the DC component to obtain the signal spectrum distribution and determine the dominant frequency. Based on the signal spectral distribution and dominant frequency, the optimal bandwidth parameter is determined from the bandwidth candidate set by maximizing the kurtosis evaluation criterion; Based on the dominant frequency and optimal bandwidth parameters, a complex Moray wavelet function is constructed. Combining the complex Moray wavelet function, a continuous wavelet transform is performed on the multi-channel signal after removing the DC component to generate a multi-channel time-frequency distribution matrix. Energy enhancement is performed by applying a frequency weighting operator to the multi-channel time-frequency distribution matrix to obtain a multi-channel weighted energy map. Time-frequency coherent fusion is performed based on multi-channel weighted energy maps, and the global energy peak is searched in the fused energy field. The coordinates corresponding to the global energy peak are determined as the pipeline damage location.

2. The pipeline eddy current damage localization method as described in claim 1, characterized in that, DC component removal processing is performed on the original discrete multi-channel signals, including: Calculate the mean of the original discrete signal for each channel; The DC component-free multi-channel signal is obtained by subtracting the corresponding mean from the original discrete signal of each channel.

3. The pipeline eddy current damage localization method as described in claim 1, characterized in that, Find the frequency component with the largest amplitude in the signal spectrum distribution, and determine the frequency corresponding to the frequency component with the largest amplitude as the dominant frequency.

4. The pipeline eddy current damage localization method as described in claim 1, characterized in that, The optimal bandwidth parameters are determined from the bandwidth candidate set by maximizing the kurtosis evaluation criterion, including: For each candidate bandwidth in the bandwidth candidate set, calculate the kurtosis value of the corresponding wavelet coefficient amplitude; Choose the candidate bandwidth that maximizes the kurtosis value as the optimal bandwidth parameter.

5. The pipeline eddy current damage localization method as described in claim 4, characterized in that, Calculate the kurtosis value of the corresponding wavelet coefficient amplitude, including: Obtain the amplitude of wavelet coefficients after wavelet transform under a certain candidate bandwidth; The mean of the wavelet coefficient amplitudes obtained from the calculation; The standard deviation of the amplitude of the wavelet coefficients is calculated, and the fourth power of the standard deviation is used as the denominator. The deviation is obtained by subtracting the mean value of the wavelet coefficient amplitude from the wavelet coefficient amplitude. The expected value of the fourth power of the obtained deviation is then calculated, and finally divided by the fourth power of the standard deviation to obtain the kurtosis value.

6. The pipeline eddy current damage localization method as described in claim 1, characterized in that, A multi-channel time-frequency distribution matrix is ​​generated, and a frequency-weighted operator is applied to the multi-channel time-frequency distribution matrix for energy enhancement to obtain a multi-channel weighted energy map, including: Set the frequency scanning range according to the dominant frequency; The constructed complex Molay wavelet function is used to perform integration on the multi-channel signal after removing the DC component, and a multi-channel time-frequency distribution matrix describing the signal distribution in the time and frequency domains is obtained. The weighted energy map is calculated based on the multi-channel time-frequency distribution matrix and the current frequency.

7. The pipeline eddy current damage localization method as described in claim 1, characterized in that, The weighted energy maps of each channel are normalized to obtain the normalized multi-channel energy maps; The normalized multi-channel energy maps are fused using a geometric average method to construct a fused energy distribution map; Find the point with the highest energy value in the merged energy distribution map; The time and frequency corresponding to the point with the highest energy value are respectively determined as the characteristic moment and characteristic resonant frequency of the damage occurrence.

8. A pipeline eddy current damage location system, characterized in that, include: The signal acquisition unit is configured to acquire the multi-channel raw discrete signals of the pipeline; The signal preprocessing unit is configured to: perform DC component removal processing on the original multi-channel discrete signal to obtain the multi-channel signal after DC component removal; The spectrum analysis unit is configured to perform a fast Fourier transform on the multi-channel signal after removing the DC component, obtain the signal spectrum distribution, and determine the dominant frequency. The parameter optimization unit is configured to determine the optimal bandwidth parameters from the bandwidth candidate set based on the signal spectral distribution and dominant frequency by maximizing the kurtosis evaluation criterion. The time-frequency mapping unit is configured to: construct a complex Moray wavelet function based on the dominant frequency and optimal bandwidth parameters, and perform continuous wavelet transform on the multi-channel signal after removing the DC component to generate a multi-channel time-frequency distribution matrix; The energy enhancement unit is configured to apply a frequency weighting operator to the multi-channel time-frequency distribution matrix to enhance the energy and obtain a multi-channel weighted energy map. The damage localization unit is configured to: perform time-frequency coherent fusion based on a multi-channel weighted energy map, search for a global energy peak in the fused energy field, and determine the coordinates corresponding to the global energy peak as the pipeline damage location.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1 to 6.

10. A computer device, characterized in that, include: Processor and computer-readable storage media; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the pipe eddy current damage localization method as described in any one of claims 1 to 6.

Images