A pipeline leakage point identification and positioning method based on fiber vibration signal features

Abstract

The present application relates to the technical field of optical fiber sensing and pipeline safety monitoring, and specifically discloses a pipeline leakage point identification and positioning method based on optical fiber vibration signal features, which comprises the following steps: collecting pipeline vibration signals along the line, locking suspected leakage sections based on energy anomaly features, and intercepting double-channel signals to be analyzed; performing dispersion feature analysis on the double-channel signals, determining the geological layer type of the suspected sections through the phase-frequency relationship of mutual power spectrum; for non-uniform geological layer signals, extracting the instantaneous frequency main ridge line through time-frequency rearrangement transformation; realizing nonlinear alignment of feature curves by using a dynamic time warping algorithm, reconstructing the distortion correction signal, and extracting the time delay difference; combining the benchmark wave velocity of dynamic calibration and the hyperbolic positioning method to calculate the spatial position of the leakage point, and simultaneously determining the leakage type based on the cumulative distance threshold and the regularized path features, thereby solving the problems of signal dispersion distortion under non-uniform geological layers, large positioning deviation, and difficult differentiation of leakage types.

Description

Technical Field

[0001] This invention relates to the field of fiber optic sensing and pipeline safety monitoring technology, specifically to a method for identifying and locating pipeline leaks based on the characteristics of fiber optic vibration signals. Background Technology

[0002] With the acceleration of urbanization and rapid industrial development, pipelines, as core infrastructure for water resource transportation and energy transmission, are of paramount importance for safe and stable operation. However, during long-term use, pipelines are affected by factors such as geological subsidence, corrosion, and external impacts.

[0003] Currently, pipeline leakage monitoring technology based on fiber optic vibration sensing is widely used due to its advantages such as high sensitivity, long-distance monitoring, and resistance to electromagnetic interference. However, in actual engineering scenarios, especially in non-uniform geological layers (such as clay-sand interlayers and miscellaneous fill areas), this technology faces many challenges: First, the propagation speed of vibration waves of different frequencies in non-uniform geological layers is different, resulting in signal dispersion effects and a non-linear phase-frequency relationship. Conventional monitoring methods do not specifically address this issue, leading to distortion in signal feature extraction. Second, non-uniform geological layers cause non-linear time distortion and waveform misalignment in dual-channel signals. Directly using cross-correlation analysis to extract time delay errors results in large errors and low accuracy in leak point location. Third, existing methods struggle to distinguish between pore leakage, crack leakage, and micro-leakage, failing to provide targeted repair guidelines for maintenance. Fourth, the energy anomaly threshold and cumulative distance threshold settings lack adaptability to actual on-site conditions, easily leading to false alarms and missed alarms.

[0004] Therefore, this invention provides a method for identifying and locating pipeline leaks based on the characteristics of optical fiber vibration signals. Summary of the Invention

[0005] The purpose of this invention is to provide a method for identifying and locating pipeline leaks based on the characteristics of optical fiber vibration signals, so as to solve the aforementioned background problems.

[0006] The objective of this invention can be achieved through the following technical solution: a method for identifying and locating pipeline leaks based on the characteristics of optical fiber vibration signals, comprising the following steps:

[0007] Vibration signals along the pipeline are collected by a distributed fiber optic vibration sensing system. Suspected leakage sections are identified based on energy anomaly characteristics. The time-domain signals of two adjacent sensing channels within the suspected leakage section are extracted as dual-channel signals to be analyzed.

[0008] Dispersion characteristic analysis was performed on the dual-channel signals to be analyzed, and linear analysis of the phase-frequency curve of the cross power spectrum was conducted to determine whether the geological layer type of the suspected seepage section was a non-uniform geological layer.

[0009] If so, perform time-frequency rearrangement transformation on the dual-channel signal to be analyzed, extract the high-resolution time spectrum, and connect the frequency components with the highest energy along the time axis to form the main ridge of the instantaneous frequency.

[0010] Using one of the main ridge lines as a reference, a dynamic time warping algorithm is used to nonlinearly align the other main ridge line to obtain the optimal warping path and cumulative distance. The original time domain signal is then reconstructed based on the optimal warping path to extract the effective time delay difference.

[0011] By combining the reference wave velocity and effective time delay difference of dynamic calibration, the spatial location of the leakage point is calculated by the hyperbolic positioning method, and the leakage type is identified based on the cumulative distance of the optimal regular path and the local expansion and contraction characteristics.

[0012] Furthermore, the method for identifying the suspected leakage section is as follows:

[0013] The distributed optical fiber is divided into several continuous measurement point groups along the pipeline axis. Each group is a sensing channel. Two adjacent channels, one upstream and one downstream, are selected and the channel coordinates are calibrated.

[0014] The distributed fiber optic vibration sensing system continuously collects vibration signals from all measuring points along the pipeline at a fixed sampling frequency and outputs raw time-domain data in the format of timestamp-measuring point location-vibration amplitude.

[0015] The original time-domain data is bandpass filtered to remove extremely low frequency drift and high frequency electromagnetic interference, retaining the frequency band related to leakage. The mean value of the unit time sliding window of the filtered signal is calculated as the background baseline. The original time-domain data is subtracted from the background baseline to eliminate signal baseline drift and obtain the corrected signal.

[0016] The short-time energy accumulation value is calculated for the corrected signal according to spatial segmentation. If the energy value of a certain segment meets the requirements for more than 3 consecutive time windows, it is judged as a suspected leakage segment.

[0017] The time-domain signals of two sensing channels within the suspected leakage section were extracted as the signals to be analyzed.

[0018] Furthermore, the method for determining whether the geological layer type of the suspected leakage section is a non-uniform geological layer is as follows:

[0019] A fast Fourier transform is performed on the dual-channel signal to be analyzed, converting the time-domain signal into a frequency-domain signal, and obtaining the amplitude and phase of each frequency component.

[0020] Calculate the cross-power spectrum of the dual-channel signal to be analyzed, and extract the phase value of the cross-power spectrum to obtain the phase-frequency curve;

[0021] Linear fitting is performed on the phase-frequency curve, and the goodness of fit is calculated. If the goodness of fit meets the requirements, it is a homogeneous geological layer; otherwise, it is a non-homogeneous geological layer.

[0022] Furthermore, the time-frequency rearrangement transformation process is as follows:

[0023] Short-time Fourier transforms are performed on the dual-channel signals to be analyzed, with Hanning windows as the window function. The window length and step size are set, and the amplitude and phase corresponding to each time point-frequency point are output to form a preliminary time spectrum.

[0024] Calculate the local instantaneous frequency and group delay of each time-frequency point in the short-time Fourier transform result, redistribute the spectral energy to the true time-frequency position, and obtain a high-resolution rearranged time-frequency spectrum.

[0025] Furthermore, the instantaneous frequency main ridge is formed as follows:

[0026] From the time-frequency rearrangement transformed time spectrum, the frequency components with the highest energy are extracted moment by moment along the time axis, and these frequency points are connected in chronological order to form the main ridge line of instantaneous frequency.

[0027] Furthermore, the nonlinear alignment process is as follows:

[0028] For suspected leakage areas:

[0029] Set the instantaneous frequency ridge of one channel as the reference sequence and mark the corresponding channel as the reference channel; set the instantaneous frequency ridge of the other channel as the test sequence and mark the corresponding channel as the test channel.

[0030] A distance matrix between the reference sequence and the test sequence is constructed using Euclidean distance, and the frequency difference at corresponding time points is calculated.

[0031] The dynamic time warping algorithm is used to search for the path with the minimum cumulative distance in the distance matrix, i.e. the optimal warping path, and outputs the optimal warping path and cumulative distance of the time mapping relationship.

[0032] Furthermore, the method for reconstructing the original time-domain signal is as follows:

[0033] Extract the time mapping relationship of the optimal normalization path, that is, the time point of each test channel corresponding to the time point of the baseline channel. For each time point of the baseline channel and the test channel in the optimal normalization path:

[0034] If the test channel time point is less than the reference channel time point, the test channel signal is lagging, and signal compression is performed on the test channel, that is, the amplitude of adjacent time points is combined.

[0035] If the test channel time point is greater than the reference channel time point, the test channel signal is ahead, and the test channel signal is stretched, i.e., the difference is supplemented with amplitude.

[0036] The distortion-corrected reconstructed signal of the output test channel is precisely aligned with the original signal of the reference channel on the time axis.

[0037] Furthermore, the effective delay difference is extracted as follows:

[0038] Cross-correlation calculations are performed on the original signal of the reference channel and the reconstructed signal of the test channel to obtain the cross-correlation function curve;

[0039] Extract the time difference corresponding to the peak value of the curve, that is, the time delay difference between the leakage signal propagating from the leakage point to the reference channel and the test channel;

[0040] If the cross-correlation curve has multiple peaks, select the peak with the largest amplitude and the narrowest width as the effective peak.

[0041] Furthermore, the spatial location of the leakage point is calculated as follows:

[0042] The reference wave velocity is dynamically calibrated by taking the wave velocity given in the geological survey report as the initial value and combining the pipeline operating pressure and soil moisture content.

[0043] The distance difference between the two channels and the leakage point is calculated based on the effective time delay difference. The spatial coordinates of the leakage point are then solved by combining the channel coordinates with the pipeline GIS data using the hyperbolic positioning method.

[0044] Furthermore, the method for identifying the leakage type is as follows:

[0045] Set a first threshold and a second threshold for cumulative distance based on historical leakage data;

[0046] If the cumulative distance is less than the first threshold of cumulative distance and the local expansion and contraction of the regular path is less than the set value, it is determined to be a hole leakage;

[0047] If the cumulative distance is greater than the second threshold of the cumulative distance and the local expansion and contraction of the regular path is greater than the set value, it is determined to be crack leakage.

[0048] If the cumulative distance is between the first threshold and the second threshold of cumulative distance, it is determined to be a micro-leak or a suspected leak.

[0049] The beneficial effects of this invention are as follows:

[0050] Accurate identification of non-uniform geological layers is achieved through dispersion feature analysis. Time-frequency rearrangement transformation and dynamic time warping algorithms are specifically adopted to effectively solve the core pain points of signal dispersion distortion and dual-channel signal asynchrony under non-uniform geological layers, and significantly improve the accuracy of leakage signal feature extraction and positioning precision.

[0051] By adopting a multi-judgment mechanism that combines energy anomaly threshold, cumulative distance threshold, and regular path characteristics, and dynamically setting thresholds based on historical data, the false alarm rate and false alarm rate are significantly reduced. This enables accurate differentiation between hole leakage, crack leakage, and micro-leakage / suspected leakage, providing targeted repair basis for pipeline network operation and maintenance.

[0052] The reference wave velocity adopts an initial value + dynamic calibration method, which is adjusted in real time by combining geological survey data, pipeline operating pressure and soil moisture content. This avoids the positioning deviation caused by fixed wave velocity in traditional methods and further improves the reliability of leakage point location.

[0053] The methodology takes into account different working conditions of homogeneous and heterogeneous geological layers, and only performs complex correction procedures on heterogeneous geological layers. It improves the overall analysis efficiency while ensuring monitoring accuracy, and is suitable for various scenarios such as municipal pipelines and industrial pipelines, and has broad application prospects. Attached Figure Description

[0054] The invention will now be further described with reference to the accompanying drawings.

[0055] Figure 1 This is a flowchart of a method for identifying and locating pipeline leaks based on the characteristics of optical fiber vibration signals according to the present invention.

[0056] Figure 2 This is a logical judgment diagram in the present invention for determining whether the geological layer type of a suspected seepage section is a non-uniform geological layer. Detailed Implementation

[0057] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0058] Example: Please refer to Figure 1 As shown, the present invention discloses a method for identifying and locating pipeline leaks based on optical fiber vibration signal characteristics. For the identification and location of pipeline leaks, a distributed optical fiber vibration sensing system collects vibration signals along the pipeline. After preprocessing and energy anomaly analysis, suspected leak sections are identified. The geological layer type is determined based on the phase-frequency relationship of the cross-power spectrum. For non-uniform geological layers, time-frequency rearrangement transformation is used to extract the distortion-resistant instantaneous frequency ridge. A dynamic time warping algorithm is used to achieve nonlinear alignment of characteristic curves, reconstructing the distortion-free signal and extracting the precise time delay difference. Combined with a dynamically calibrated reference wave velocity, the leak point is located. The leak type is determined based on a threshold set from historical data and the warped path characteristics. Specifically, the method includes the following steps:

[0059] Step 1: Collect vibration signals along the pipeline using a distributed fiber optic vibration sensing system, identify suspected leakage sections based on energy anomaly characteristics, and extract the time-domain signals of two adjacent sensing channels within the suspected leakage section as dual-channel signals to be analyzed.

[0060] In step one, the identification process of the suspected leakage section includes:

[0061] The distributed optical fiber is divided into several continuous measurement point groups along the pipeline axis, and each group is a sensing channel (for example, one channel every 50m). At least two adjacent channels upstream and downstream are selected, and the channel coordinates are calibrated.

[0062] The distributed fiber optic vibration sensing system continuously collects vibration signals from all measuring points along the pipeline at a fixed sampling frequency (10-20kHz), and outputs raw time-domain data in the format of timestamp-measuring point location-vibration amplitude. Time synchronization must be ensured during the acquisition process to avoid signal timing misalignment between channels.

[0063] The original time-domain data is bandpass filtered to remove extremely low frequency drift and high frequency electromagnetic interference, retaining the frequency band related to leakage. The mean value of the unit time sliding window of the filtered signal is calculated as the background baseline. The original time-domain data is subtracted from the background baseline to eliminate signal baseline drift and obtain the corrected signal.

[0064] The corrected signal is divided into spatial segments (each 10m is an analysis segment) to calculate the short-time energy accumulation value (time window 0.5s, step size 0.1s).

[0065] Set an energy anomaly threshold (usually three times the average background energy). If the energy value of a certain section exceeds the energy anomaly threshold for more than three consecutive time windows, it is determined to be a suspected leakage section.

[0066] Extract the time-domain signals from at least two sensor channels within the suspected leakage section as the signals to be analyzed;

[0067] Understandably, the logic for identifying suspected leakage sections is as follows: when the pipeline is operating normally, there is only low-intensity, stable background vibration along the pipeline (such as slight disturbance of water flow inside the pipe and natural micro-vibration of the soil), and the energy value is in a stable low-level range. When the pipeline leaks, the leaking water flow (especially high-pressure jet) will continuously impact the damaged part of the pipe wall and the surrounding soil, generating a continuous, local vibration energy source. The vibration energy in this area will be significantly and continuously higher than the background energy.

[0068] It should be noted that the role of suspected leakage section identification is as follows: to quickly locate suspected leakage sections with abnormal energy from long-distance, full-time raw signals, and to eliminate most areas without abnormalities; the pre-processed signals eliminate noise and baseline drift, providing clean input data for subsequent dispersion feature analysis and time-frequency rearrangement; and to clarify the spatiotemporal range of the signal to be analyzed (specific section + dual channels), avoiding redundancy in full pipeline data analysis.

[0069] Step 2: Perform dispersion characteristic analysis on the dual-channel signals to be analyzed, and determine whether the geological layer type of the suspected leakage section is a non-uniform geological layer by performing linear analysis on the phase-frequency curve of the cross power spectrum.

[0070] Please see Figure 2 As shown, in step two, the process of determining whether the suspected leakage section is located in a non-uniform geological layer includes:

[0071] A fast Fourier transform is performed on the dual-channel signal to be analyzed, converting the time-domain signal into a frequency-domain signal, and obtaining the amplitude and phase of each frequency component.

[0072] The cross-power spectrum of the two-channel signals to be analyzed is calculated to reflect the correlation between the two signals at different frequencies, and the phase value of the cross-power spectrum is extracted to obtain the phase-frequency curve.

[0073] A linear fitting method is used to calculate the goodness of fit of the phase-frequency curve, and the goodness of fit is compared with a preset threshold. If the goodness of fit is lower than the preset threshold, the phase-frequency relationship is determined to be non-linear, and otherwise it is linear.

[0074] If the curve is linear, it indicates that the propagation time delay of different frequency components is consistent, the vibration wave propagates in a homogeneous medium, and it is determined to be a homogeneous geological layer.

[0075] If the curve is nonlinear, it indicates that the time delays of different frequency components are inconsistent (i.e., dispersion effect), and the vibration wave is propagating in a non-uniform medium, which is determined to be a non-uniform geological layer.

[0076] It should be noted that the logic for determining whether a suspected leakage section is located in a non-uniform geological layer is as follows: In a non-uniform geological layer (such as clay-sand interlayer, miscellaneous fill), vibration waves of different frequencies have different propagation speeds, resulting in a non-linear phase change with frequency. Therefore, if the phase-frequency relationship is non-linear, that is, the time delays of different frequency components are inconsistent, then the suspected leakage section is determined to be located in a non-uniform geological layer.

[0077] It should be noted that the purpose of determining whether a suspected leakage section is located in a non-uniform geological layer is to accurately identify the geological layer type of the suspected section, avoid over-correction in the case of a uniform geological layer or uncorrection in the case of a non-uniform geological layer, perform complex correction procedures only on the signals of non-uniform geological layers, improve the overall analysis efficiency, and the dispersion characteristics are a direct physical characterization of the non-uniformity of the geological layer. The judgment results can be cross-verified with the geological survey report to ensure the necessity of subsequent correction.

[0078] Step 3: If so, perform time-frequency rearrangement transformation on the dual-channel signal to be analyzed, extract the high-resolution time spectrum, and connect the frequency components with the highest energy along the time axis to form the main ridge of the instantaneous frequency.

[0079] In step three, the time-frequency rearrangement transformation process includes:

[0080] The first point to clarify is that for the dual-channel signals to be analyzed, which are determined to be non-homogeneous geological layers, short-time Fourier transforms are performed separately, specifically as follows:

[0081] The window function is the Hanning window (to improve time-frequency focus). Set the window length and step size, for example: window length 0.02s (covering the main frequency components of the leakage signal), step size 0.005s;

[0082] Output the amplitude and phase corresponding to each time-frequency point to form a preliminary time spectrum (time × frequency × amplitude).

[0083] Understandably, the logic of the short-time Fourier transform is as follows: non-uniform geological layers cause the signal frequency to change dynamically over time. The short-time Fourier transform can capture time-varying features, but the conventional short-time Fourier transform has poor clustering and needs to be rearranged and optimized.

[0084] Secondly, it should be noted that the preliminary time spectrum obtained from the short-time Fourier transform undergoes a time-frequency rearrangement transformation to improve time-frequency cohesion, specifically as follows:

[0085] Calculate the local instantaneous frequency and group delay (reflecting the true time-frequency location of the energy) at each time-frequency point in the short-time Fourier transform result, including:

[0086] Local instantaneous frequency: df / dt, the rate of change of frequency with time, where f is the frequency and t is the time;

[0087] Group delay: -dφ / dω, the rate of change of phase with angular frequency, where ω=2πf and φ is the phase;

[0088] Understandably, the core idea of ​​time-frequency rearrangement is to redistribute the spectral energy of the short-time Fourier transform to the frequency points corresponding to the instantaneous frequency plus the group delay, thereby obtaining a high-resolution rearranged time-frequency spectrum. The logic is that non-uniform geological layers cause the time-frequency characteristics of the signal to diffuse, and after rearrangement, the energy gathers towards the true characteristics, which can clearly extract the frequency change pattern.

[0089] The spectral energy of the short-time Fourier transform is redistributed from the original time-frequency point to the time-frequency point corresponding to the instantaneous frequency plus the group delay, resulting in a high-resolution rearranged time-frequency spectrum.

[0090] In step three, the process of obtaining the instantaneous frequency main ridge line includes:

[0091] From the time-frequency rearranged spectrum, the frequency components with the highest energy are extracted moment by moment along the time axis. These frequency points are connected in time order to form the instantaneous frequency ridge (characteristic curve), which reflects the change of signal frequency with time.

[0092] It is understandable that the logic for extracting the main ridge line of instantaneous frequency is that the main ridge line has strong robustness to waveform distortion caused by non-uniform geological layers, and can still retain the core characteristics of the leakage signal even if dispersion occurs during signal propagation.

[0093] It should be noted that the purpose of time-frequency rearrangement transformation to extract the signal feature curves under non-uniform geological layers is to solve the problem of time-frequency feature dispersion of signals under non-uniform geological layers. Through rearrangement transformation, a high-resolution time spectrum is obtained. The extracted instantaneous frequency ridge is a unique feature of the leakage signal, avoiding the complex distortion of the original signal. The feature curve only retains the frequency change law over time, and its dimension is much lower than that of the original time domain signal, which greatly reduces the computational complexity of subsequent DTW.

[0094] Step 4: Using the main ridge line of one channel as a reference, the main ridge line of the other channel is nonlinearly aligned using a dynamic time warping algorithm to obtain the optimal warping path and cumulative distance. The original time domain signal is then reconstructed based on the optimal warping path to extract the effective time delay difference.

[0095] In step four, the nonlinear alignment process includes:

[0096] For suspected leakage areas:

[0097] Set the instantaneous frequency ridge of one channel as the reference sequence and mark the corresponding channel as the reference channel; set the instantaneous frequency ridge of the other channel as the test sequence and mark the corresponding channel as the test channel.

[0098] Set a maximum distortion factor (dynamically adjusted according to the degree of geological heterogeneity: factor = 0.3 for many interlayers, factor = 0.1 for slight heterogeneity) to constrain the search range of the dynamic time warping algorithm (to avoid over-scaling).

[0099] A distance matrix between the reference sequence and the test sequence is constructed using Euclidean distance, and the frequency difference at corresponding time points is calculated.

[0100] The dynamic time warping algorithm is used to search for the path with the minimum cumulative distance in the distance matrix, i.e. the optimal warping path, which reflects how the test channel needs to be stretched / compressed to match the benchmark channel.

[0101] Output the optimal normalized path and cumulative distance of the time mapping relationship (refer to the overall similarity between the benchmark sequence and the test sequence).

[0102] It is understandable that the logic of nonlinear alignment in the dynamic time warping algorithm is as follows: non-uniform geological layers cause nonlinear time distortion in dual-channel signals, and the dynamic time warping algorithm can adaptively compensate for this distortion to achieve accurate alignment of feature curves.

[0103] In step four, the process of reconstructing the original time-domain signal includes:

[0104] Extract the time mapping relationship of the optimal regularized path, that is, the time point of the benchmark channel corresponding to each time point of the test channel;

[0105] Based on the time mapping relationship of the optimal normalized path, the original time-domain data of the test channel is nonlinearly stretched / compressed, specifically as follows:

[0106] For each time point in the baseline and test channels within the optimal normalization path:

[0107] If the test channel time point is less than the reference channel time point, the test channel signal is lagging, and signal compression is performed on the test channel, that is, the amplitude of adjacent time points is combined.

[0108] If the test channel time point is greater than the reference channel time point, the test channel signal is ahead, and the test channel signal is stretched, i.e., the difference is supplemented with amplitude.

[0109] The distortion-corrected reconstructed signal of the output test channel is precisely aligned with the original signal of the reference channel on the time axis;

[0110] Understandably, the logic of reconstructing the original signal is to apply the correction results of the characteristic curve to the original time domain signal to eliminate the time domain waveform misalignment caused by the non-uniform geological layer.

[0111] In step four, the extraction process of the effective time delay difference includes:

[0112] Cross-correlation calculations are performed on the original signal of the reference channel and the reconstructed signal of the test channel to obtain the cross-correlation function curve;

[0113] Extract the time difference corresponding to the peak value of the curve, that is, the time delay difference between the leakage signal propagating from the leakage point to the reference channel and the test channel;

[0114] If the cross-correlation curve has multiple peaks, select the peak with the largest amplitude and the narrowest width as the effective peak (non-uniform geological layers are prone to false peaks due to multipath reflection).

[0115] It should be noted that the role of dynamic time warping correction in obtaining the reconstructed signal and time delay difference after distortion correction is to solve the core pain point of nonlinear distortion and asynchrony of dual-channel signals under non-uniform geological layers. It is the key to improving positioning accuracy. It transforms feature-level correction into time-domain signal correction, providing a synchronous and distortion-free signal foundation for subsequent positioning calculations.

[0116] Step 5: Combining the reference wave velocity and effective time delay difference of dynamic calibration, calculate the spatial location of the leakage point using the hyperbolic positioning method, and identify the leakage type based on the cumulative distance of the optimal regular path and local expansion and contraction characteristics.

[0117] In step five, the calculation process for the spatial location of the leakage point includes:

[0118] The reference wave velocity v of non-uniform geological layers needs to be dynamically calibrated.

[0119] Initial values: Refer to the geological survey report (e.g., clay v=1500m / s, sand v=1800m / s);

[0120] Correction: Combining pipeline operating pressure (v increases by 5%-10% for higher pressure) and soil moisture content (v decreases by 3%-8% for higher moisture content), the dynamic equivalent wave velocity v of the suspected leak section is obtained. 动 ;

[0121] Based on the time delay difference Δt, calculate the distance difference between the test channel and the reference channel to the leakage point: Δd = v × Δt;

[0122] Given the coordinates of the midpoint of the reference channel (X1, Y1, Z1) and the midpoint of the test channel (X2, Y2, Z2), and the distance between the two channels... (Difference in burial depth in the Z direction is negligible);

[0123] Let the coordinates of the leakage point be (X, Y, Z), satisfying: (Hyperbolic positioning), combined with pipeline GIS data (pipeline direction), the spatial coordinates of the leakage point are obtained;

[0124] In step five, the process of determining the leakage type includes:

[0125] Based on historical data, a first threshold and a second threshold for cumulative distance are set, specifically as follows:

[0126] The system acquires leakage data from non-uniform geological sections and different operating pressures covering the target pipeline during historical periods, including raw vibration signals and DTW cumulative distances for pore leakage, crack leakage, and micro-leakage / suspected leakage, and integrates them into pore leakage dataset, crack leakage dataset, and micro-leakage / suspected leakage dataset.

[0127] First threshold for cumulative distance: Take the 95th percentile of the cumulative distance of the hole leakage dataset, that is, 95% of the hole leakage cumulative distances are less than the first threshold for cumulative distance, to ensure that hole leakage is rarely misjudged as micro-leakage;

[0128] Second threshold for cumulative distance: Take the 5th percentile of the cumulative distance of the crack leakage dataset, that is, 95% of the cumulative distances of crack leakage are greater than the second threshold for cumulative distance, to ensure that crack leakage is rarely misclassified as micro-leakage;

[0129] Ensure that the cumulative distance first threshold is less than the cumulative distance second threshold, and that 90% of the cumulative distance of the micro-leakage / suspected leakage dataset falls within the range of [cumulative distance first threshold, cumulative distance second threshold];

[0130] The rules for determining the type of leakage are as follows:

[0131] If the cumulative distance D < the first threshold of cumulative distance, and the regular path is approximately diagonal (local stretching < 5%): it is judged as porosity leakage (the signal distortion of porosity leakage under non-uniform geological layers is slight and the frequency characteristics are stable).

[0132] If the cumulative distance D > the second threshold of cumulative distance, and the regular path shows severe local bending (local stretching > 20%): it is judged as crack leakage (the broadband signal of crack leakage is severely distorted in non-uniform geological layers, and the frequency characteristics fluctuate greatly).

[0133] If the first threshold of cumulative distance ≤ D ≤ the second threshold of cumulative distance: it is determined to be a micro-leak / suspected leak, and continuous monitoring is triggered;

[0134] It should be noted that the purpose of leak point location and type determination is to output the final spatial coordinates of the leak point and the leak type, which directly serves the operation and maintenance of the pipeline network. In view of the characteristics of non-uniform geological layers, the reference wave velocity is calibrated, which solves the deviation problem caused by the fixed speed of traditional location methods. Combined with the cumulative distance and path characteristics of dynamic programming algorithm, the leak type can be accurately determined, avoiding misjudgment under non-uniform geological layers.

[0135] The technical solution and advantages of this application are as follows: Vibration signals along the pipeline are collected using a distributed optical fiber vibration sensing system. Suspected leakage sections are identified based on energy anomaly characteristics, and the time-domain signals of two adjacent sensing channels within the suspected leakage section are extracted as dual-channel signals to be analyzed. Dispersion characteristic analysis is performed on the dual-channel signals, and linear analysis of the cross-power spectrum phase-frequency curve is conducted to determine whether the geological layer type of the suspected leakage section is a non-uniform geological layer. If so, time-frequency rearrangement transformation is performed on the dual-channel signals to extract a high-resolution time spectrum, and the line connecting the frequency components with the highest energy along the time axis forms the instantaneous frequency ridge. Using one channel's ridge as a reference, a dynamic time warping algorithm is used to non-linearly align the other channel's ridge to obtain the optimal warping path and cumulative distance. The original time-domain signal is reconstructed based on the optimal warping path to extract the effective time delay difference. Combining the dynamically calibrated reference wave velocity and the effective time delay difference, the spatial location of the leakage point is calculated using the hyperbolic positioning method. Based on the cumulative distance of the optimal warping path and the understanding of local expansion and contraction characteristics, the leakage type is identified. This application collects vibration signals along the pipeline, identifies suspected leakage sections based on energy anomaly characteristics, and extracts dual-channel signals for analysis. It performs dispersion characteristic analysis on the dual-channel signals and determines the geological layer type of the suspected section through the phase-frequency relationship of the cross-power spectrum. For signals from non-uniform geological layers, it extracts the instantaneous frequency ridge line through time-frequency rearrangement transformation. A dynamic time warping algorithm is used to achieve nonlinear alignment of characteristic curves, reconstructing the distortion correction signal and extracting the time delay difference. The spatial location of the leakage point is calculated by combining dynamically calibrated reference wave velocity and hyperbolic positioning method. Simultaneously, the leakage type is determined based on the cumulative distance threshold and warped path characteristics. This solves the problems of signal dispersion distortion, large positioning deviation, and difficulty in distinguishing leakage types under non-uniform geological layers.

[0136] The embodiments of the present invention have been described in detail above, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent changes and improvements made in accordance with the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for identifying and locating pipeline leaks based on fiber optic vibration signal characteristics, characterized in that: Includes the following steps: Vibration signals along the pipeline are collected by a distributed fiber optic vibration sensing system. Suspected leakage sections are identified based on energy anomaly characteristics. The time-domain signals of two adjacent sensing channels within the suspected leakage section are extracted as dual-channel signals to be analyzed. Dispersion characteristic analysis was performed on the dual-channel signals to be analyzed, and linear analysis of the phase-frequency curve of the cross power spectrum was conducted to determine whether the geological layer type of the suspected seepage section was a non-uniform geological layer. If so, perform time-frequency rearrangement transformation on the dual-channel signal to be analyzed, extract the high-resolution time spectrum, and connect the frequency components with the highest energy along the time axis to form the main ridge of the instantaneous frequency. The time-frequency rearrangement transformation process is as follows: Short-time Fourier transforms are performed on the dual-channel signals to be analyzed, with Hanning windows as the window function. The window length and step size are set, and the amplitude and phase corresponding to each time point-frequency point are output to form a preliminary time spectrum. Calculate the local instantaneous frequency and group delay of each time-frequency point in the short-time Fourier transform result, redistribute the spectral energy to the true time-frequency position, and obtain a high-resolution rearranged time-frequency spectrum; Using one of the main ridge lines as a reference, a dynamic time warping algorithm is used to nonlinearly align the other main ridge line to obtain the optimal warping path and cumulative distance. The original time domain signal is then reconstructed based on the optimal warping path to extract the effective time delay difference. The nonlinear alignment process is as follows: For suspected leakage areas: Set the instantaneous frequency ridge of one channel as the reference sequence and mark the corresponding channel as the reference channel; set the instantaneous frequency ridge of the other channel as the test sequence and mark the corresponding channel as the test channel. A distance matrix between the reference sequence and the test sequence is constructed using Euclidean distance, and the frequency difference at corresponding time points is calculated. The dynamic time warping algorithm is used to search for the path with the minimum cumulative distance in the distance matrix, i.e. the optimal warping path, and outputs the optimal warping path and cumulative distance of the time mapping relationship. By combining the reference wave velocity and effective time delay difference of dynamic calibration, the spatial location of the leakage point is calculated by the hyperbolic positioning method, and the leakage type is identified based on the cumulative distance of the optimal regular path and the local expansion and contraction characteristics. The spatial location of the leakage point is calculated as follows: The reference wave velocity is dynamically calibrated by taking the wave velocity given in the geological survey report as the initial value and combining the pipeline operating pressure and soil moisture content. The distance difference between the two channels and the leakage point is calculated based on the effective time delay difference. The spatial coordinates of the leakage point are then solved by combining the channel coordinates with the pipeline GIS data using the hyperbolic positioning method.

2. The method for identifying and locating pipeline leaks based on fiber optic vibration signal characteristics according to claim 1, characterized in that: The method for identifying the suspected leakage section is as follows: The distributed optical fiber is divided into several continuous measurement point groups along the pipeline axis. Each group is a sensing channel. Two adjacent channels, one upstream and one downstream, are selected and the channel coordinates are calibrated. The distributed fiber optic vibration sensing system continuously collects vibration signals from all measuring points along the pipeline at a fixed sampling frequency and outputs raw time-domain data in the format of timestamp-measuring point location-vibration amplitude. The original time-domain data is bandpass filtered to remove extremely low frequency drift and high frequency electromagnetic interference, retaining the frequency band related to leakage. The mean value of the unit time sliding window of the filtered signal is calculated as the background baseline. The original time-domain data is subtracted from the background baseline to eliminate signal baseline drift and obtain the corrected signal. The short-time energy accumulation value is calculated for the corrected signal according to spatial segmentation. If the energy value of a certain segment meets the requirements for more than 3 consecutive time windows, it is judged as a suspected leakage segment. The time-domain signals of two sensing channels within the suspected leakage section were extracted as the signals to be analyzed.

3. The method for identifying and locating pipeline leaks based on fiber optic vibration signal characteristics according to claim 1, characterized in that: The method for determining whether the geological layer type of the suspected seepage zone is a non-uniform geological layer is as follows: A fast Fourier transform is performed on the dual-channel signal to be analyzed, converting the time-domain signal into a frequency-domain signal, and obtaining the amplitude and phase of each frequency component. Calculate the cross-power spectrum of the dual-channel signal to be analyzed, and extract the phase value of the cross-power spectrum to obtain the phase-frequency curve; Linear fitting is performed on the phase-frequency curve, and the goodness of fit is calculated. If the goodness of fit meets the requirements, it is a homogeneous geological layer; otherwise, it is a non-homogeneous geological layer.

4. The method for identifying and locating pipeline leaks based on fiber optic vibration signal characteristics according to claim 1, characterized in that: The instantaneous frequency ridge is formed in the following way: From the time-frequency rearrangement transformed time spectrum, the frequency components with the highest energy are extracted moment by moment along the time axis, and these frequency points are connected in chronological order to form the main ridge line of instantaneous frequency.

5. The method for identifying and locating pipeline leaks based on fiber optic vibration signal characteristics according to claim 1, characterized in that: The method for reconstructing the original time-domain signal is as follows: Extract the time mapping relationship of the optimal normalization path, that is, the time point of each test channel corresponding to the time point of the baseline channel. For each time point of the baseline channel and the test channel in the optimal normalization path: If the test channel time point is less than the reference channel time point, the test channel signal is lagging, and signal compression is performed on the test channel, that is, the amplitude of adjacent time points is combined. If the test channel time point is greater than the reference channel time point, the test channel signal is ahead, and the test channel signal is stretched, i.e., the difference is supplemented with amplitude. The distortion-corrected reconstructed signal of the output test channel is precisely aligned with the original signal of the reference channel on the time axis.

6. The method for identifying and locating pipeline leaks based on fiber optic vibration signal characteristics according to claim 5, characterized in that: The effective delay difference is extracted as follows: Cross-correlation calculations are performed on the original signal of the reference channel and the reconstructed signal of the test channel to obtain the cross-correlation function curve; Extract the time difference corresponding to the peak value of the curve, that is, the time delay difference between the leakage signal propagating from the leakage point to the reference channel and the test channel; If the cross-correlation curve has multiple peaks, select the peak with the largest amplitude and the narrowest width as the effective peak.

7. The method for identifying and locating pipeline leaks based on fiber optic vibration signal characteristics according to claim 1, characterized in that: The method for identifying the type of leakage is as follows: Set a first threshold and a second threshold for cumulative distance based on historical leakage data; If the cumulative distance is less than the first threshold of cumulative distance and the local expansion and contraction of the regular path is less than the set value, it is determined to be a hole leakage; If the cumulative distance is greater than the second threshold of the cumulative distance and the local expansion and contraction of the regular path is greater than the set value, it is determined to be crack leakage. If the cumulative distance is between the first threshold and the second threshold of cumulative distance, it is determined to be a micro-leak or a suspected leak.

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