A method for removing pressure signal trend in pipeline leak monitoring based on signal segmentation

Abstract

The application discloses a pipeline leakage monitoring pressure signal trend elimination method based on signal segmentation, relates to the technical field of oil and gas pipeline leakage monitoring, and comprises the following steps: performing polynomial fitting processing on the pressure signal of a to-be-processed pipeline section in a fixed period to obtain a first pressure signal; determining the maximum value and the minimum value of the first pressure signal in the fixed period, and segmenting the first pressure signal in the fixed period according to the maximum value and the minimum value; dividing the first pressure signal into an ascending stage and a descending stage, and when the first pressure signal contains an ascending stage signal, performing trend elimination on the ascending stage signal by using a high-pass filtering method. The application performs automatic segmentation processing on the pressure signal containing trend change and leakage information, optimizes the trend change signal elimination processing method, highlights the leakage information signal, and thus helps to reduce the occurrence of false alarms in a leakage monitoring system and improve the leakage identification accuracy.

Description

Technical Field

[0001] This invention relates to the field of oil and gas pipeline leakage monitoring technology, and in particular to a method for eliminating pressure signal trends in pipeline leakage monitoring based on signal segmentation. Background Technology

[0002] Pipeline transportation, as an efficient, economical, and environmentally friendly mode of transport, possesses unique advantages in transporting resources such as oil and natural gas, and has become a major means of transporting oil and gas resources both domestically and internationally. Monitoring pipelines during their service life is a crucial aspect of ensuring the safe transport of oil and gas. Methods for monitoring oil and gas pipeline leaks mainly include flow statistics, pressure gradient methods, and negative pressure wave methods. The negative pressure wave method has high sensitivity and is widely used; however, it requires monitoring and analyzing pressure signals to determine whether a leak has occurred.

[0003] The pressure signal caused by a leak is a pressure drop. Due to the complexity of pipeline systems, even when no leak occurs, the pressure signal may exhibit numerous upward or downward trends. These trends severely impact leak detection, easily leading to false alarms and leak alarms. Only by minimizing these trends in the signal to achieve a stable state and preserving the shape of the pressure drop caused by a leak as much as possible can the leak pressure drop be accurately identified, thereby reducing false alarms.

[0004] Commonly used trend elimination methods include the finite difference method, filtering method, fitting method, model method, wavelet method, and empirical mode decomposition method. The finite difference method is simple, but determining the delay constant requires manual intervention, and different delay constants have a significant impact on trend elimination. In the filtering method, different filter passband frequencies result in different trend elimination effects, and current technology cannot automatically adjust the passband frequency. The fitting method requires pre-assuming the type of trend term in the signal, making it unsuitable for analyzing complex real-world signals. The model method first requires establishing a state-space model through learning, secondly, the processing method is complex, and thirdly, the determination of parameters significantly affects the trend elimination effect. In the wavelet method, the selection of wavelet basis functions and the determination of the wavelet decomposition scale both require prior prediction. Empirical mode decomposition methods suffer from mode aliasing and endpoint effects during decomposition; in the process of reconstructing the trend signal, it is generally necessary to identify which components are valid components of the trend term through methods such as manually setting thresholds.

[0005] Existing trend elimination methods for pipeline pressure signals target a single frame of pressure signal as a whole. When a single frame of signal has a pressure rise signal at the beginning and a pressure drop signal caused by slow leakage at the end, the aforementioned trend elimination method will also treat the pressure drop signal caused by slow leakage as a trend signal and eliminate it, which will cause a leak alarm. Summary of the Invention

[0006] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically by providing a method for eliminating the trend of pipeline leakage monitoring pressure signals based on signal segmentation, as detailed below: 1) In a first aspect, the present invention provides a method for eliminating the trend of pipeline leakage monitoring pressure signals based on signal segmentation, the specific technical solution of which is as follows: S1, perform polynomial fitting on the pressure signal of the pipe section to be processed within a fixed period to obtain the first pressure signal; S2, determine the maximum and minimum values ​​of the first pressure signal within the fixed period, and segment the first pressure signal within the fixed period according to the maximum and minimum values ​​to generate multiple signal segments and mark each signal segment according to the pressure value. S3, the first pressure signal is divided into rising and falling phases. When any segment of the signal contains a signal in the rising phase, the signal in the rising phase is trend-eliminating using the Butterworth high-pass filter. The signal in the rising phase is the signal in which the pressure value continuously increases, and the signal in the falling phase is the signal in which the pressure value continuously decreases.

[0007] The beneficial effects of the pipeline leakage monitoring pressure signal trend elimination method based on signal segmentation provided by this invention are as follows: The system automatically segments pressure signals containing trend changes and leakage information, optimizes the trend change signal elimination method, and highlights leakage information signals, thereby helping to reduce the occurrence of leakage alarms in the leakage monitoring system and improve the accuracy of leakage identification.

[0008] Based on the above solution, the present invention can be further improved as follows.

[0009] Furthermore, it also includes: Repeat S3 and calculate whether the peak value of the cross-correlation coefficient between the pressure signal after the (k-1)th trend elimination and the pressure signal after the kth trend elimination meets the preset condition. Stop executing S3 when the preset condition is met.

[0010] Furthermore, it also includes: The pressure signal obtained after trend elimination is used as the signal of the rising phase in the original first pressure signal to generate the second pressure signal.

[0011] Furthermore, it also includes: When each signal segment is a falling phase signal, return to S1.

[0012] Furthermore, it also includes: When the number of times S3 is executed reaches the preset number, an early warning message is generated.

[0013] 2) Secondly, the present invention also provides a pipeline leakage monitoring pressure signal trend elimination system based on signal segmentation, the specific technical solution of which is as follows: The fitting processing module is used to: perform polynomial fitting processing on the pressure signal of the pipe segment to be processed within a fixed period to obtain the first pressure signal; The determination module is used to: determine the maximum and minimum values ​​of the first pressure signal within the fixed period, and segment the first pressure signal within the fixed period according to the maximum and minimum values, generate multiple signal segments, and mark each signal segment according to the magnitude of the pressure value; The elimination module is used to: divide the first pressure signal into rising and falling phases; when any segment of the signal contains a signal in the rising phase, perform trend elimination on the signal in the rising phase using the Butterworth high-pass filtering method; the signal in the rising phase is the signal in which the pressure value continuously increases, and the signal in the falling phase is the signal in which the pressure value continuously decreases.

[0014] Based on the above solution, the present invention can be further improved as follows.

[0015] Furthermore, it also includes: The repeat module is used to: repeat the elimination module and calculate whether the peak value of the cross-correlation coefficient between the pressure signal after the (k-1)th trend elimination and the pressure signal after the kth trend elimination meets the preset condition. If the preset condition is met, the elimination module is not repeated.

[0016] Furthermore, it also includes: The replacement module is used to: take the pressure signal obtained after trend elimination as the signal of the rising phase in the original first pressure signal, and generate a second pressure signal.

[0017] Furthermore, it also includes: The return module is used to return to the fitting processing module when each signal segment is a signal in the falling phase.

[0018] Furthermore, it also includes: The early warning module is used to generate early warning information when the number of times the repeat module is executed reaches a preset number.

[0019] It should be noted that the beneficial effects of the technical solution of the second aspect of the present invention and the corresponding possible implementation can be found in the above description of the technical effects of the first aspect and its corresponding possible implementation, and will not be repeated here. Attached Figure Description

[0020] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic flowchart of a pipeline leakage monitoring pressure signal trend elimination method based on signal segmentation according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the original pressure signal containing trend and leakage information, according to an embodiment of the present invention, which is a method for eliminating the trend of pipeline leakage monitoring pressure signal based on signal segmentation. Figure 3 This is a schematic diagram of polynomial fitting of a pressure signal containing trend and leakage information, which is a method for eliminating the trend of pipeline leakage monitoring pressure signal based on signal segmentation according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the state division of pressure signal P1 according to an embodiment of the present invention, which is a method for eliminating the trend of pipeline leakage monitoring pressure signal based on signal segmentation. Figure 5 This is a schematic diagram of the segmentation result of a pressure signal P according to an embodiment of the present invention, which is a method for eliminating the trend of pipeline leakage monitoring pressure signal based on signal segmentation. Figure 6 This invention relates to a method for eliminating pressure signal trends in pipeline leakage monitoring based on signal segmentation, specifically the extraction of pressure signals containing an upward pressure trend. Schematic diagram; Figure 7 This is a schematic diagram of the pressure signal with an upward trend after two high-pass filters, according to an embodiment of the present invention, for a pipeline leakage monitoring pressure signal trend elimination method based on signal segmentation. Figure 8 This is a schematic diagram of the peak cross-correlation coefficient curve in the iterative high-pass filtering process of a pipeline leakage monitoring pressure signal trend elimination method based on signal segmentation according to an embodiment of the present invention. Figure 9 This is a schematic diagram of the pressure signal after eliminating the upward trend in a pipeline leakage monitoring pressure signal trend elimination method based on signal segmentation, according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0022] like Figure 1 As shown in the figure, a method for eliminating the trend of pipeline leakage monitoring pressure signals based on signal segmentation according to an embodiment of the present invention includes the following steps: S1, perform polynomial fitting on the pressure signal of the pipe section to be processed within a fixed period to obtain the first pressure signal.

[0023] Wherein: the pipe segment to be processed is any pipe segment in the pipeline network, and a pressure sensor is installed in the pipe segment to collect pressure signals. The pressure sensor is installed at the corner.

[0024] The fixed period is a manually set time period, such as 15 minutes.

[0025] Polynomial fitting specifically involves finding a polynomial function to approximate given data points, thereby identifying the optimal fitting curve. In practical applications, polynomial fitting is widely used in data analysis, pattern recognition, and signal processing. The least squares method is commonly used in polynomial fitting. In this scheme, a polynomial fitting tool is used to fit the pressure signal acquired by the pressure sensor, generating a fitting curve for the pressure signal within a fixed period, i.e., the first pressure signal.

[0026] S2, determine the maximum and minimum values ​​of the first pressure signal within the fixed period, and segment the first pressure signal within the fixed period according to the maximum and minimum values ​​to generate multiple signal segments, and mark each signal segment according to the pressure value. Wherein: Determine the maximum and minimum values ​​of the fitted curve within a fixed period, and divide the fitted curve according to the maximum and minimum values.

[0027] For example, if the maximum value of the fitted curve in the 15-30 min time range is 50 Pa and the minimum value is 5 Pa, then the fitted curve can be divided into three segments: 5-20 Pa is the first segment, labeled 1; 20-35 Pa is the second segment, labeled 2; and 35-50 Pa is the third segment, labeled 3. It should be further noted that within each segment, there is more than one corresponding line on the fitted curve. That is, the segment can correspond to at least two discontinuous lines. Specifically, all pressure signals at low pressure levels are represented by 0, all pressure signals at medium pressure levels by 1, and pressure signals at high pressure levels by 2.

[0028] S3, the first pressure signal is divided into an upward phase and a downward phase. When the first pressure signal contains a signal in the upward phase, the Butterworth high-pass filter is used to eliminate the trend of the signal in the upward phase. The signal in the upward phase is the signal in which the pressure value continuously increases, and the signal in the downward phase is the signal in which the pressure value continuously decreases. Wherein: If the result obtained from S2 indicates that the signal contains an upward phase, then the Butterworth high-pass filter is used to eliminate the trend of the upward phase signal.

[0029] When the pressure signal changes from 0 to 1, or from 1 to 2, the corresponding signal is classified as the pressure rising phase; conversely, when the pressure signal changes from 2 to 1, or from 1 to 0, it belongs to the pressure falling phase.

[0030] For example, in the 15-30 min range, the rising phase signals are: 15min15s-16min15s and 26min-28min. The maximum value of the fitted curve is 50 Pa, and the minimum value is 5 Pa. Therefore, the fitted curve is divided into three segments: 5-20 Pa is the first segment (labeled 1), 20-35 Pa is the second segment (labeled 2), and 35-50 Pa is the third segment (labeled 3). Within the first segment (5-20 Pa), there are three lines: 7-10 min, 13-18 min, and 26-30 min. It can be seen that the signal corresponding to the 13-18 min line in the first segment contains the rising phase signal corresponding to 15min15s-16min15s. Therefore, a Butterworth high-pass filter is used to perform trend elimination processing on the signal corresponding to the 13-18 min line, yielding the result after trend elimination.

[0031] In another embodiment of this solution, the solution may further include an evaluation process for the trend elimination effect, specifically: The peak value of the cross-correlation coefficient of the pressure signals after the (k-1)th and kth high-pass filtering is denoted as maxPeak(k), and the peak value of the cross-correlation coefficient of the pressure signals after the kth and (k+1)th high-pass filtering is denoted as maxPeak(k+1). The threshold for the difference between the peak values ​​of the two cross-correlation coefficients is set to 0.005. If maxPeak(k) - maxPeak(k+1) <= 0.005, then the upward trend in the pressure signal is considered to have been eliminated.

[0032] The peak value of the cross-correlation coefficient is the maximum value of the cross-correlation coefficient curves of the pressure signals after two high-pass filterings, which is obtained by cross-correlation calculation of the two pressure signals.

[0033] In another embodiment of this solution, the solution may further include a pressure signal substitution process, specifically: The original, extracted pressure signal containing the upward trend is replaced with the pressure signal after the upward trend has been eliminated, and this new signal is combined with the pressure decline phase to obtain the pressure signal after the trend has been eliminated. In other words, in the example above, the signal corresponding to the 13-18 minute line is replaced with the result after the trend elimination process to generate a new signal.

[0034] The beneficial effects of the trend elimination method for pressure signals based on signal segmentation provided by this invention are as follows: The system automatically segments pressure signals containing trend changes and leakage information, optimizes the trend change signal elimination method, and highlights leakage information signals, thereby helping to reduce the occurrence of leakage alarms in the leakage monitoring system and improve the accuracy of leakage identification.

[0035] Furthermore, it also includes: Repeat S3 and calculate whether the peak value of the cross-correlation coefficient between the pressure signal after the (k-1)th trend elimination and the pressure signal after the kth trend elimination meets the preset condition. Stop executing S3 when the preset condition is met.

[0036] Furthermore, it also includes: The pressure signal obtained after trend elimination is used as the signal of the rising phase in the original first pressure signal to generate the second pressure signal.

[0037] Furthermore, it also includes: When each signal segment is a falling phase signal, return to S1.

[0038] Furthermore, it also includes: When the number of times S3 is executed reaches the preset number, an early warning message is generated.

[0039] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0040] Example 1 illustrates the implementation of the present invention using a signal frame where the front end represents a pressure increase signal (trend signal) and the rear end represents a pressure decrease signal caused by leakage as an example. The steps are as follows: Figure 2 The original pressure signal containing trend and leakage information is denoted as signal P; the pressure signal is subjected to polynomial fitting, and the resulting pressure signal is denoted as P1, as follows. Figure 3 As shown. The three-stage state division of the fitted signal involves dividing the pressure signal P1 obtained from polynomial fitting into three stages: low pressure, medium pressure, and high pressure, labeled as 0, 1, and 2 respectively. Figure 4 As shown in the figure. The original signal rise and fall phases are divided according to the fitted signal state division results. The original pressure signal P is divided into rise and fall phases, and the results are as follows. Figure 5As shown. The first stage S1 is the pressure rise stage, and the second stage S2 is the pressure fall stage. Trend cancellation is performed on the pressure signal during the pressure rise stage. If a frame of signal contains a pressure rise stage, it indicates that the signal contains trend information. Trend cancellation is performed on the pressure signal during the pressure rise stage. Trend cancellation of signals containing trends uses the Butterworth high-pass filtering method, where R is set to 20*10. 5 Ohm, C is 47uF, cutoff frequency is f c =1 / (2*Pi*R*C). During the iterative filtering process, the resistance is adjusted iteratively. The cutoff frequency of the high-pass filter is adjusted using the method of (where k is the number of iterations). The extracted upward trend segment of the pressure signal is denoted as... ,like Figure 6 As shown. The trend elimination effect is evaluated. The pressure signal of the kth high-pass filter is denoted as k The pressure signal after the (k+1)th high-pass filtering is denoted as k+1 The pressure signal with an upward trend after two high-pass filters is as follows: Figure 7 As shown. For k and k+1 Perform cross-correlation calculations to obtain the corresponding peak curves of cross-correlation coefficients during the iterative filtering process, as shown below. Figure 8 As shown. Let maxPeak(k) be the peak value of the cross-correlation coefficient between the pressure signals after the (k-1)th and kth high-pass filtering, and let maxPeak(k+1) be the peak value of the cross-correlation coefficient between the pressure signals after the kth and (k+1)th high-pass filtering. Set the threshold for the difference between the peak values ​​of the two cross-correlation coefficients to Epsilong = 0.005. If maxPeak(k) - maxPeak(k+1) Epsilong, on the other hand, believes that the upward trend in the pressure signal has been eliminated.

[0041] The pressure signal is replaced. The original truncated pressure signal containing the upward trend is replaced with the pressure signal after the upward trend has been eliminated, resulting in the pressure signal after the trend has been eliminated, as shown below. Figure 9 As shown, the curve formed by the asterisk * represents the pressure signal after eliminating the upward trend, while the linear curve represents the pressure drop portion of the original pressure signal (reference). Figure 5 ).

[0042] This invention also provides a pipeline leakage monitoring pressure signal trend elimination system based on signal segmentation, the specific technical solution of which is as follows: The fitting processing module is used to: perform polynomial fitting processing on the pressure signal of the pipe segment to be processed within a fixed period to obtain the first pressure signal; The determination module is used to: determine the maximum and minimum values ​​of the first pressure signal within the fixed period, and segment the first pressure signal within the fixed period according to the maximum and minimum values, generate multiple signal segments, and mark each signal segment according to the magnitude of the pressure value; The elimination module is used to: divide the first pressure signal into rising and falling phases; when any segment of the signal contains a signal in the rising phase, perform trend elimination on the signal in the rising phase using the Butterworth high-pass filtering method; the signal in the rising phase is the signal in which the pressure value continuously increases, and the signal in the falling phase is the signal in which the pressure value continuously decreases.

[0043] Based on the above solution, the present invention can be further improved as follows.

[0044] Furthermore, it also includes: The repeat module is used to: repeat the elimination module and calculate whether the peak value of the cross-correlation coefficient between the pressure signal after the (k-1)th trend elimination and the pressure signal after the kth trend elimination meets the preset condition. If the preset condition is met, the elimination module is not repeated.

[0045] Furthermore, it also includes: The replacement module is used to: take the pressure signal obtained after trend elimination as the signal of the rising phase in the original first pressure signal, and generate a second pressure signal.

[0046] Furthermore, it also includes: The return module is used to return to the fitting processing module when each signal segment is a signal in the falling phase.

[0047] Furthermore, it also includes: The early warning module is used to generate early warning information when the number of times the repeat module is executed reaches a preset number.

[0048] It should be noted that the beneficial effects of the pipeline leakage monitoring pressure signal trend elimination system based on signal segmentation provided in the above embodiments are the same as those of the pipeline leakage monitoring pressure signal trend elimination method based on signal segmentation described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0049] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0050] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0051] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for eliminating the trend of pipeline leakage monitoring pressure signals based on signal segmentation, characterized in that, include: S1, perform polynomial fitting on the pressure signal of the pipe section to be processed within a fixed period to obtain the first pressure signal; S2, determine the maximum and minimum values ​​of the first pressure signal within the fixed period, and segment the first pressure signal within the fixed period according to the maximum and minimum values ​​to generate multiple signal segments and mark each signal segment according to the pressure value. S3, the first pressure signal is divided into rising phase and falling phase. When the first pressure signal contains a signal in the rising phase, the trend of the signal in the rising phase is eliminated by Butterworth high-pass filtering. The signal in the rising phase is the signal in which the pressure value continuously increases, and the signal in the falling phase is the signal in which the pressure value continuously decreases. Also includes: Repeat S3 and calculate whether the peak value of the cross-correlation coefficient between the pressure signal after the (k-1)th trend elimination and the pressure signal after the kth trend elimination meets the preset condition. Stop executing S3 when the preset condition is met. Also includes: The pressure signal obtained after trend elimination is used as the signal of the rising phase in the original first pressure signal to generate the second pressure signal. Also includes: Return to S1 when each signal segment is a falling phase signal; Also includes: When the number of times S3 is executed reaches the preset number, an early warning message is generated.

2. A pipeline leakage monitoring pressure signal trend elimination system based on signal segmentation, characterized in that, include: The fitting processing module is used to: perform polynomial fitting processing on the pressure signal of the pipe segment to be processed within a fixed period to obtain the first pressure signal; The determination module is used to: determine the maximum and minimum values ​​of the first pressure signal within the fixed period, and segment the first pressure signal within the fixed period according to the maximum and minimum values, generate multiple signal segments, and mark each signal segment according to the magnitude of the pressure value; The elimination module is used to: divide the first pressure signal into rising and falling phases; when the first pressure signal contains a signal in the rising phase, perform trend elimination on the signal in the rising phase using the Butterworth high-pass filtering method; the signal in the rising phase is a signal in which the pressure value continuously increases, and the signal in the falling phase is a signal in which the pressure value continuously decreases. Also includes: The repeat module is used to: repeat the elimination module and calculate whether the peak value of the cross-correlation coefficient between the pressure signal after the (k-1)th trend elimination and the pressure signal after the kth trend elimination meets the preset condition. If the preset condition is met, the elimination module is not repeated. Also includes: The replacement module is used to: take the pressure signal obtained after trend elimination as the signal of the rising phase in the original first pressure signal to generate a second pressure signal; Also includes: The return module is used to return to the fitting processing module when each signal segment is a signal in the falling phase. Also includes: The early warning module is used to generate early warning information when the number of times the repeat module is executed reaches a preset number.

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