Pipeline internal detector and leakage point mileage calculation method

Abstract

The invention discloses a pipeline internal detector and a leakage point mileage calculation method, which utilize the physical characteristic that the rolling period and the movement speed of a spherical internal detector have a strict corresponding relation when the spherical internal detector moves in a fixed-axis rolling mode in a pipeline, and analyze the signal period characteristics acquired by a built-in three-axis acceleration sensor to calculate the mileage of a leakage point. Precise estimation of the motion distance of the spherical inner detector is realized; meanwhile, sound signals are collected and analyzed, interference is removed in combination with a collision sound elimination algorithm based on morphology and wavelet analysis, leakage signals are recognized through a three-frequency-band energy combined threshold judgment method, and finally accurate positioning of pipeline leakage points is achieved. According to the method, the accumulated mileage of the spherical inner detector in the pipeline can be accurately calculated, the time slice of the leakage sound can be accurately identified, and the mileage of the leakage point can be accurately positioned.

Description

Technical Field

[0001] This invention relates to the field of internal detectors for oil and natural gas pipelines, and more particularly to an internal detector for pipelines and a method for calculating the mileage of leak points. Background Technology

[0002] In-pipe detectors for oil and gas pipelines need to be able to detect and locate pipeline leaks. Spherical in-pipe detectors, with a diameter smaller than the pipe diameter, can roll freely under the propulsion of the fluid inside the pipeline, offering advantages such as convenient deployment and retrieval, low risk of jamming, and the ability to be deployed intensively. However, because all signals measured by spherical in-pipe detectors are in a rotating coordinate system, they can only provide information such as rotational acceleration, magnetic field, and sound. Therefore, how to utilize this data to locate the sphere itself and the leak in a global coordinate system is of great significance for spherical in-pipe detectors.

[0003] Chinese patent CN201610478552.2 uses Fourier transform to extract the center frequency of the accelerometer as the rolling frequency of the spherical internal detector, which is then used to calculate the rolling speed of the spherical internal detector. The distance traveled by the internal detector within the pipe is obtained by integrating the speed. This method is very effective for spherical internal detectors that roll stably in liquid pipes with stable flow rates. However, it is not suitable for situations with unstable flow rates or where bends cause uneven rolling of the sphere. Furthermore, it requires a long data segment to extract an accurate center frequency and cannot detect instantaneous frequency and rolling speed. There will be a large cumulative error when integrating the speed to obtain the distance. In addition, the sound signal detected by the spherical internal detector includes sound signals generated by pipe leakage and the sound of the internal detector colliding with the pipe wall, which can interfere with the identification of leakage sound and the location of the distance.

[0004] Therefore, in order to accurately measure the mileage of the spherical internal detector in the pipeline and accurately identify and locate the leakage sound, a method for calculating the mileage of the spherical internal detector and the leakage point in the pipeline is proposed. Summary of the Invention

[0005] This invention provides a pipeline detector and a method for calculating the mileage of a leak point. This invention can accurately calculate the cumulative mileage of a spherical internal detector within a pipeline, accurately identify the time segment of the leak sound, and accurately locate the mileage of the leak point. See the description below for details: An internal detector for pipelines, wherein the internal detector is a spherical internal detector, comprising: an upper spherical shell, a lower spherical shell, a counterweight ring, a vibration damping rubber ring (such as an O-ring), an accelerometer, and an electronic module; the internal detector is made of metal for high-pressure conditions and plastic for low-pressure conditions; the spherical structure layout enables the sphere to roll along a fixed axis and record at least accelerometer data.

[0006] The design includes an acoustic cavity partition and a microphone group inside the sphere. The microphone group is located inside the cavity to record acoustic data. The first three resonant frequencies of the acoustic cavity are three discrete frequencies less than 5000Hz, covering the low-frequency band of the leakage sound signal.

[0007] The metal spherical internal detector allows for: opening the housing for function control and status display; copying files via a built-in memory card; and wired charging via a battery. The plastic spherical internal detector employs a wireless communication and charging scheme, enabling remote control and data transmission via a wireless communication module, and integrates wireless charging functionality.

[0008] Secondly, a method for calculating the mileage of a leak point, the method being based on the aforementioned pipeline detector, the method comprising: The raw acceleration signal acquired by the spherical internal detector is preprocessed, and a median filter is used to remove sudden glitch interference. Then, a second-order Butterworth high-pass filter is used to eliminate DC bias. By setting positive and negative thresholds, the complete cycle of the X-axis acceleration signal is divided into four state transition processes. The time interval between adjacent cycles is recorded to calculate the instantaneous frequency, thereby obtaining the instantaneous motion velocity of the spherical detector. The motion distance is obtained by summing the circumferences corresponding to each complete cycle; thus eliminating the cumulative error and removing the interference caused by the collision between the spherical inner detector and the tube wall in the acoustic signal. Leakage identification is performed using a three-band energy joint threshold decision method. Background sound under normal rolling conditions is selected as a reference to establish a statistical benchmark. The energy threshold for each frequency range is set based on the 6-σ criterion. The final leakage range is determined by taking the intersection of the potential leakage areas of the three frequency bands. The midpoint of the identified leak area is taken as the leak time point, and the mileage of the leak point is determined by combining the mileage-time curve.

[0009] The complete cycle is as follows: Once the detected signal completes four state transitions in sequence—crossing the lower threshold from bottom to top, crossing the upper threshold from bottom to top, crossing the upper threshold from top to bottom, and crossing the lower threshold from top to bottom—one complete rolling cycle of recognition is achieved.

[0010] The instantaneous period is calculated by recording the time interval between adjacent periods. Thus, the instantaneous frequency is obtained. frequency and circumference of the sphere C Multiplying these values ​​yields the instantaneous velocity of the spherical internal detector. .

[0011] Among them, the characteristic signal generated by the spherical internal detector when passing through the bend is used as a spatial marker point for mileage correction; The location of the bend is determined by GNSS measurement. When the spherical detector passes through the bend, these characteristic signals are recorded and the corresponding time points are mapped to the known physical location of the bend. The time-distance mapping is optimized to obtain the positioning result.

[0012] The mileage correction is as follows: piecewise linear stretching. When the detector moves from bend i to bend i+1, the mileage wheel travels Δs_i, and the distance between bend i and bend i+1 is ΔL_i. If Δs_i < ΔL_i, the mileage wheel reading is too small. The position of all data points in this segment is enlarged proportionally by ΔL_i / Δs_i, stretching the record of Δs_i to ΔL_i.

[0013] The removal of interference generated by the collision between the spherical inner detector and the tube wall in the acoustic signal includes: wavelet denoising and baseline estimation; gradient calculation and edge detection; collision event detection and connection; amplitude deviation analysis of the collision region; and local trend preservation replacement and smoothing.

[0014] The method calculates the cumulative rolling distance of the spherical internal detector under three operating conditions and constructs a time-to-distance mapping interpolation function; using the mapping interpolation function, the identified leakage event time points are converted into spatial mileage points.

[0015] The beneficial effects of the technical solution provided by this invention are: First, the algorithm principle is simple and the computational efficiency is high: Based on the physical characteristics of the fixed-axis rolling of the spherical internal detector, the present invention can estimate the motion distance by only using the periodic characteristics of the recorded three-axis rotation acceleration signal. There is no need for complex inertial navigation calculation or external positioning signal. The algorithm is simple to implement, has a small amount of computation, and is easy to run in real time in embedded systems. Second, the positioning accuracy is high and the error is controllable: Experimental results show that the mileage calculation method based on acceleration period detection has a cumulative distance error of only 1.33% in a 657.46-meter-long experimental pipeline; after mileage correction by combining the characteristic signal of the bend, the mileage positioning error under different pressure and flow velocity conditions meets the actual engineering accuracy requirements for pipeline leakage detection and positioning. Third, it has strong adaptability and good compatibility with different working conditions: This method is independent of flow velocity and can show good mileage positioning performance under different flow velocity conditions; Fourth, it has self-correction capability: This invention uses the collision feature signal generated by the spherical internal detector when passing through the bend as a spatial marker point, and combines it with the known bend position to correct the mileage, which can effectively eliminate the cumulative error in the long-distance movement process and ensure the stability of positioning accuracy. Fifth, no additional positioning equipment is required: This invention can complete the positioning function solely by the accelerometer built into the spherical internal detector, eliminating the need for external positioning equipment such as GPS and odometer wheels, thus reducing system complexity and cost and improving reliability in complex pipeline environments. Sixth, it can effectively eliminate interference from collision sounds, accurately identify leakage sounds, and accurately locate leak points. Attached Figure Description

[0016] Figure 1 Design diagram of the spherical internal detector structure; (a) is the overall structure diagram; (b) is the component sectional view. Figure 2 A design schematic of the electronic system for a spherical internal detector; Figure 3 This is a schematic diagram of the raw acceleration signal collected by the spherical internal detector as it rolls forward inside the pipe. (a) is a schematic diagram of the overall curve; (b) is a schematic diagram of the curve during stable rolling; and (c) is a schematic diagram of the curve when a collision occurs.

[0017] Figure 4 This is a schematic diagram of the acceleration curve after filtering; (a) is a schematic diagram of the overall curve; (b) is a schematic diagram of the curve during stable rolling; and (c) is a schematic diagram of the curve when a collision occurs.

[0018] Figure 5 A schematic diagram of an algorithm for period extraction of acceleration signals; Figure 6 This is a schematic diagram of the velocity curve of the spherical internal detector. Figure 7 This is a schematic diagram of the distance-time curve of the spherical internal detector; Figure 8 This is a flowchart illustrating the collision sound detection and elimination algorithm for a spherical internal detector based on morphology and wavelet analysis. Figure 9 This is a schematic diagram of the collision noise cancellation algorithm for an internal spherical detector. Among them, (a) is a schematic diagram of the original signal; (b) is a schematic diagram of wavelet denoising and baseline estimation; (c) is a schematic diagram of gradient calculation and edge detection; (d) is a schematic diagram of event detection; and (e) is a schematic diagram of collision signal removal.

[0019] Figure 10 A schematic diagram showing the time range of the identified leak area; Among them, (a) is the curve of the amplitude of the first three characteristic frequencies of the sound signal measured by the metal spherical internal detector at a pressure of 1 MPa and a flow rate of 1 m / s as a function of time; (b) is the curve of the amplitude of the first three characteristic frequencies of the sound signal measured by the metal spherical internal detector at a pressure of 0.5 MPa and a flow rate of 1 m / s as a function of time; and (c) is the curve of the amplitude of the first three characteristic frequencies of the sound signal measured by the plastic spherical internal detector at a pressure of 0.5 MPa and a flow rate of 1 m / s as a function of time.

[0020] Figure 11 This is a schematic diagram showing the location of the leak area identified by two spherical internal detectors under different operating conditions.

[0021] Among them, (a) is the curve of the amplitude of the first three characteristic frequencies of the sound signal measured by the metal spherical internal detector at a pressure of 1 MPa and a flow rate of 1 m / s as a function of time; (b) is the curve of the amplitude of the first three characteristic frequencies of the sound signal measured by the metal spherical internal detector at a pressure of 0.5 MPa and a flow rate of 1 m / s as a function of time; and (c) is the curve of the amplitude of the first three characteristic frequencies of the sound signal measured by the plastic spherical internal detector at a pressure of 0.5 MPa and a flow rate of 1 m / s as a function of time.

[0022] Figure 12 This is a flowchart of a method for calculating the mileage of pipeline leak points. Detailed Implementation

[0023] Example 1 The design of the spherical internal detector prioritizes acoustic detection performance while also considering requirements for pressure resistance, stability, and vibration reduction. For example... Figure 1 As shown, the spherical internal detector includes: an upper spherical shell, a lower spherical shell, a counterweight ring, an acoustic cavity partition, a vibration damping rubber ring (such as an O-ring), a microphone assembly (including: a microphone circuit board, an edge microphone, and a center microphone), and an electronic module (including: a data acquisition circuit board, a battery, and a magnetic coil).

[0024] The energy of the pipe leakage sound is mainly concentrated within 5 kHz and is relatively stable. Based on this characteristic, the dimensions of the spherical acoustic cavity were initially determined through simulation analysis, so that the first three resonant frequencies of the cavity are approximately 2 kHz, 3 kHz, and 4 kHz, which can effectively cover the main low-frequency band of the leakage sound signal. In the fixed-axis rolling state, the spherical internal detector only contacts the pipe wall in the equatorial region. For this reason, several optimization schemes were designed: in terms of vibration reduction design, only damping rubber rings (such as O-rings) are set on both sides of the equatorial plane as local damping components, avoiding the influence of the full-enclosed damping layer on the sound transmission of the spherical shell; in terms of the distribution of electronic components, cylindrical spaces are designed in the two pole regions for electronic system components; in order to maintain the integrity of the acoustic cavity, only a slender circuit board for fixing the microphone and signal transmission is placed inside the cavity.

[0025] To ensure successful launch and arrival at the receiving tube for the spherical internal detector, a magnetic emission coil module was designed and installed inside the detector. This module emits extremely low-frequency electromagnetic signals capable of penetrating metal pipes. The detector automatically activates after a preset time has elapsed within the pipe, emitting an alternating magnetic field. Personnel outside the pipe can use portable magnetic field receiving equipment to inspect the pipe and determine the detector's position by detecting changes in signal strength at specific frequencies, thus confirming successful launch and readiness for retrieval. Real-world pipelines present leakage detection needs in both high-pressure and low-pressure scenarios. The leakage signal strength and structural pressure requirements differ significantly under different pressure conditions, necessitating the targeted selection of the detector's shell material. In high-pressure scenarios, the acoustic signal generated by the leak is strong, allowing for a slight sacrifice in acoustic permeability to meet structural strength requirements. In low-pressure scenarios, the leakage acoustic signal is relatively weak, making detection sensitivity the primary consideration. This invention develops two spherical internal detector solutions: a metal spherical internal detector for high-pressure conditions and a plastic spherical internal detector for low-pressure conditions.

[0026] As a highly integrated mobile detection platform, the spherical internal detector for pipeline leak detection requires its electronic system design to meet multiple requirements, including high-precision data acquisition, stable and reliable operation, and low power consumption. Both types of spherical internal detectors made of different materials maintain the same core data acquisition function and employ the same sensor selection scheme to ensure data acquisition accuracy. However, due to the different assembly methods of the two types of spherical internal detectors—the metal spherical internal detector is detachable but shields against high-frequency electromagnetic signals, while the plastic spherical internal detector is permanently sealed but does not shield against high-frequency electromagnetic signals—different designs have been implemented in terms of the main controller, power supply method, and data transmission.

[0027] like Figure 2As shown, the electronic system of the spherical internal detector adopts a modular design, mainly composed of six functional modules. The system is centered on a microcontroller unit (MCU), connecting a data acquisition module, a data storage module, a positioning module, a human-machine interface module, and a power management module. The data acquisition module acquires acoustic signals via the I2S (Inter-IC Sound) bus and acceleration and magnetic signals via the I2C (Inter-Integrated Circuit) bus or the SPI (Serial Peripheral Interface) bus. These data are uniformly scheduled and processed by the main control MCU. The main control MCU transmits the processed data to a TF card via SDIO (Secure Digital Input Output) or SPI interface for storage as a file. The positioning module is used to emit low-frequency magnetic signals to the outside of the pipe, determining whether to activate based on the movement state of the spherical internal detector. The power management module provides the necessary power to each functional module through an LDO (Low Dropout Regulator) conversion circuit. Based on the aforementioned unified hardware architecture, the human-machine interaction module and power management module of the two spherical internal detectors adopt different implementation schemes: the metal spherical internal detector requires opening the shell and uses a button and indicator light scheme to control functions and display status through direct operation, and data download is done by directly removing the TF card to copy files, and charging is done by removing the battery for wired charging; the plastic spherical internal detector adopts a wireless communication and charging scheme, realizing remote control and data transmission through a wireless communication module, and integrating wireless charging function to avoid opening the sphere for charging.

[0028] Example 2 This invention provides a method for locating a pipe spherical internal detector based on rotational acceleration data recorded by the spherical internal detector. This method utilizes the strict correspondence between the rolling period and the movement speed of the spherical internal detector as it rolls along a fixed axis in the pipe. By analyzing the periodic characteristics of the triaxial acceleration signals, it achieves accurate estimation of the movement distance and determines the leak point mileage by identifying the time point of the leakage sound.

[0029] 101: The spherical internal detector rolls forward along the pipe driven by the fluid, and collects data from the built-in triaxial accelerometer and sound sensor in real time during the motion process; 102: The raw acceleration signal was preprocessed by using median filtering to remove sudden glitches and then using a second-order Butterworth high-pass filter to eliminate DC bias. 103: Design a state machine-based periodic detection algorithm. By setting positive and negative thresholds, the complete period of the X-axis acceleration signal is divided into four state transition processes. The time interval between adjacent periods is recorded to calculate the instantaneous frequency, thereby obtaining the instantaneous motion velocity of the detector inside the sphere. The detection cycle is completed when the detected signal sequentially completes four state transitions: crossing the lower threshold from bottom to top, crossing the upper threshold from bottom to top, crossing the upper threshold from top to bottom, and crossing the lower threshold from top to bottom.

[0030] 104: The distance traveled is obtained by summing the circumferences corresponding to each complete cycle; Specifically, this step involves: calculating the instantaneous period and instantaneous frequency by recording the time interval between adjacent periods; multiplying the frequency by the circumference of the sphere's equator to obtain the instantaneous velocity; and accumulating the circumference corresponding to each complete period to obtain the distance traveled.

[0031] 105: The collision characteristic signal generated when the spherical internal detector collides with the pipe wall when passing through the pipe bend is used as a spatial marker point. Combined with the actual position of the bend determined in advance by high-precision GNSS measurement, the mileage calculation result is corrected to eliminate cumulative error. 106: In terms of leakage detection, a collision detection and elimination algorithm based on morphology and wavelet analysis is adopted. Through wavelet denoising, baseline estimation, gradient calculation and edge detection, collision event detection and connection, collision region amplitude deviation analysis, and local trend preservation replacement and smoothing, the interference generated by the collision between the spherical inner detector and the pipe wall in the acoustic signal is removed. 107: A three-band energy joint threshold decision method is used for leakage identification. The background sound under normal rolling conditions is selected as a reference to establish a statistical benchmark. The energy threshold of each frequency range is set based on the 6-σ criterion. The final leakage range is determined by taking the intersection of the potential leakage areas of the three frequency bands. 108: The midpoint of the identified leak area is taken as the leak time point, and the mileage-time curve is used to determine the leak point mileage.

[0032] In summary, the method of this invention is simple and reliable, requires no additional positioning equipment, and the positioning error meets the practical application requirements of the pipe spherical internal detector for detection and positioning.

[0033] Example 3 The structural composition and functional principle of the embodiments of the present invention will be further explained with reference to the accompanying drawings. When the spherical internal detector moves in a fixed-axis rolling manner in the pipe, its rolling period has a strict correspondence with its movement speed. The embodiments of the present invention utilize this characteristic to estimate the movement distance of the spherical internal detector by analyzing the periodic characteristics of the acceleration signal.

[0034] Taking the experimental data of the spherical internal detector under a flow velocity of 1 m / s as an example, Figure 3 Figure (a) shows the triaxial acceleration signals of the spherical internal detector throughout the experiment. Overall, the acceleration signal maintains a stable periodic change for most of the time. When the spherical internal detector collides with the tube wall as it passes through the bend, the acceleration waveform fluctuates significantly. When the spherical internal detector rolls steadily, as shown... Figure 3 As shown in Figure (b), the spherical internal detector rolls around the Z-axis in a fixed-axis posture. The X-axis and Y-axis acceleration components exhibit stable sinusoidal wave characteristics with a 90° phase difference; the Z-axis acceleration component remains essentially near zero. When the spherical internal detector collides with the pipe wall at locations such as bends, as... Figure 3 As shown in Figure (c), the motion stability of the sphere is disrupted. At this time, the Z-axis acceleration signal increases, and all three axes of acceleration produce obvious abnormal peaks.

[0035] To extract the periodic characteristics from the acceleration signal, preprocessing of the original signal is necessary. Median filtering is used to remove sudden spikes and interference, followed by a second-order Butterworth high-pass filter to eliminate the DC bias. The filter's cutoff frequency is set based on the motion characteristics of the spherical detector. Since the equatorial circumference of the spherical detector is 427.04 mm, the roll-off frequency can be estimated to be approximately 2.34 Hz ​​when the detector moves at a speed of 1 m / s. To ensure effective removal of the DC component without affecting the extraction of the roll-off frequency, the cutoff frequency of the high-pass filter is set to 0.4 Hz. The filtered signal is as follows: Figure 4 As shown, where Figure 4 Figures (b) and (c) in the text are Figure 4 The waveform curves in Figure (a) are the curves during stable rolling and the curves during a collision, respectively.

[0036] The filtered acceleration signal has two significant characteristics: first, the signal waveform is smooth and has obvious periodicity, making it suitable for periodic feature extraction; second, under stable rolling conditions, the amplitude of the triaxial acceleration signal is stably distributed within the range of ±1g, where g is the gravitational acceleration.

[0037] Based on the characteristics of the filtered signal, a state machine-based periodic detection algorithm was designed, the principle of which is as follows: Figure 5 As shown. This algorithm divides a complete cycle of the X-axis acceleration signal into four state transition processes by setting two thresholds (±0.7g): (1) State 1→2: The signal crosses the lower threshold from bottom to top; (2) State 2→3: The signal crosses the upper threshold from bottom to top; (3) State 3→4: The signal crosses the upper threshold from top to bottom; (4) State 4→1: The signal crosses the lower threshold from top to bottom.

[0038] Once the detected signal completes these four state transitions, a full cycle of identification is complete. The instantaneous cycle can be calculated by recording the time intervals between adjacent cycles. Thus, the instantaneous frequency is obtained. Relating frequency to the circumference of the sphere C Multiplying these two values ​​yields the instantaneous velocity of the spherical internal detector. .

[0039] The velocity curve of the spherical internal detector after smoothing is as follows: Figure 6 As shown in the figure, the spherical internal detector moves stably at a speed of approximately 1 m / s for most of the time, which is consistent with the experimentally set pipe flow velocity, indicating that the structural design of the spherical internal detector can ensure that it maintains a stable fixed-axis rolling state in the pipe. During the movement, the spherical internal detector collides with the pipe wall, causing instability in its movement, and its speed fluctuates and decreases significantly, but it can then quickly recover to a stable state. This shows that the state machine-based periodic detection algorithm can effectively track the changes in the movement speed and motion state of the spherical internal detector.

[0040] By summing the circumferences corresponding to each complete cycle, a curve showing the change in the movement distance of the spherical inner detector over time is obtained, as shown below. Figure 7 As shown. The experimental pipeline is 657.46 m long. From Figure 7 As can be seen, the total movement distance calculated by the algorithm through perimeter accumulation is 648.7 m, with an error of 1.33% compared to the actual pipe length. This indicates that the mileage calculation method based on acceleration period detection can be effectively used for the positioning of spherical internal detectors.

[0041] It is worth noting that in the application of spherical internal detector positioning, since the pipeline route and location are known, the collision signal generated when the spherical internal detector passes through the bend can be used as a location marker to correct the cumulative error of the algorithm and further improve the positioning accuracy.

[0042] To further reduce the impact of positioning errors and improve positioning accuracy, this embodiment of the invention utilizes the characteristic signals generated by the spherical internal detector when passing through bends as spatial marker points for mileage correction. The bend positions in the experimental pipeline have been determined by high-precision GNSS measurements. When the spherical internal detector passes through these bends, both acceleration and acoustic signals show significant changes. By recording these characteristic signals and mapping the corresponding time points to the known physical locations of the bends, the time-distance mapping can be optimized, thereby improving the positioning accuracy of the spherical internal detector. The correction method is piecewise linear stretching. Assuming the detector travels from bend i to bend i+1, the mileage wheel shows a distance of Δs_i, but the actual distance between bends i and i+1 is ΔL_i. If Δs_i < ΔL_i, it indicates that the mileage wheel reading for this segment is too low. Therefore, the positions of all data points within this segment are magnified proportionally by ΔL_i / Δs_i, stretching the record of Δs_i to ΔL_i.

[0043] The cumulative error of the spherical detector at known bends needs to be calculated, and then this cumulative error is applied to the distance to the spherical detector calculated by the algorithm. By analyzing the significant decrease points in the velocity data of the spherical detector, the time points when the spherical detector passes through each bend are determined, and the cumulative travel distance value at that moment is read, thus obtaining the position of each bend. The error correction process is shown in Table 1.

[0044] Table 1. Odometer error correction for spherical internal detector under a flow rate of 1 m / s.

[0045] As can be seen from the data in Table 1, the cumulative error in mileage calculation gradually increases as the spherical internal detector moves within the pipeline. The corrected mileage position of the spherical internal detector is 413.35 m, which, compared to the actual mileage position of 416.89 m, represents an absolute error of 3.54 m, meeting the accuracy requirements for actual field applications of spherical internal detector detection and positioning.

[0046] Example 4 The leak identification and leak location mileage scheme includes three key steps: interference elimination, leak identification, and leak location mileage. The interference elimination step focuses on solving the interference problem caused by the collision between the spherical internal detector and the pipe wall. The collision signal is manifested as a short-duration high-energy pulse, while the leak signal shows a continuous energy rise. Based on this difference, an algorithm can be designed (specifically as described in the interference elimination section below (1)) to identify and remove the spike signal caused by the collision, while retaining the overall trend of the energy curve. In the leak determination step, a statistical benchmark is established by selecting the background sound under normal rolling conditions as a reference. The threshold decision method is used to evaluate the energy curves of the three frequencies to determine the possible leak intervals, and the leak time point is determined by calculating the midpoint of the interval. Finally, the leak location is determined by comparing it with the mileage-time curve of the spherical internal detector.

[0047] (1) Interference cancellation During leak localization, the leak area is determined and located by the amplitude change of acoustic energy. However, the collision sound from the detector inside the sphere interferes with the overall trend analysis and amplitude change judgment of the energy curve. To address this issue, a collision detection and elimination algorithm based on a combination of morphology and wavelet analysis was developed. This algorithm aims to automatically identify and effectively eliminate collision spikes in the energy curve while preserving the overall trend information of the curve. The core idea of ​​the algorithm is to differentiate and process collision sound and leakage sound based on their different characteristics. Collision sound is characterized by sudden high-energy spikes in the time domain, with a short duration, typically 0.1-0.5 s, and drastic amplitude changes; while leakage sound is characterized by a slow rise in energy and its persistence, typically lasting for tens of seconds, with relatively smooth amplitude changes. Under normal rolling conditions, the energy curve remains at a low level with minimal fluctuations. Based on these differences, the algorithm uses wavelet transform to capture the time-frequency characteristics of the signal, analyzes the structural characteristics of the signal through mathematical morphology, identifies abrupt change regions using gradient analysis, and finally reconstructs the signal using a local trend preservation strategy to achieve accurate removal of collision interference. The algorithm flow is as follows: Figure 8 As shown.

[0048] The algorithm mainly consists of the following steps: 1) Wavelet denoising and baseline estimation: First, the original energy curve... Denoising is performed using wavelet transform. The sym8 wavelet basis function is selected, and the signal is decomposed into multiple scales. （1） in, Represents wavelet transform, Indicates inverse wavelet transform. This indicates a soft thresholding operation. Soft thresholding is performed by adjusting the wavelet coefficients. This is achieved using the following functions: （2） in, The threshold is adaptively determined based on the signal-to-noise level. wavelet coefficients The symbol.

[0049] Subsequently, the baseline component of the signal is estimated using morphological opening operations: （3） in, This indicates a morphological opening operation. For length is Linear structural elements. Baseline This reflects the long-term trend of the signal and provides a stable reference for subsequent collision identification.

[0050] 2) Gradient calculation and edge detection: First, calculate the original signal. The first derivative is used to obtain the rate of change of the signal: （4） Then set the slope threshold. Identify rapidly changing regions in a signal: （5） （6） in, Represents the set of ascending edges. This represents the set of descent edges. These edge points will be used later to identify collision events.

[0051] 3) Collision event detection and connection: Based on the edge detection results, potential collision events that meet the duration requirement are identified: （7） in, Indicates the first A potential collision signal area and These represent the start and end times of the region, respectively. and These represent the minimum and maximum allowed event durations, respectively.

[0052] To connect multiple scattered edges belonging to the same collision event, apply the morphological closing operation: （8） in, It is the tagging function for the initial potential collision events, when hour ,otherwise ; This indicates a morphological closing operation. It is a length of The linear structuring element. The result after the closing operation. It provides more continuous and complete potential collision zone markings.

[0053] 4) Collision area amplitude deviation analysis: For each potential collision region, calculate the average deviation of its signal value from the baseline: （9） in, Indicates the area The time span. When the deviation The absolute value exceeds the preset threshold At that time, the region is marked as a collision peak: （10） in, Indicates the area It was confirmed as a collision. This indicates the normal signal area.

[0054] 5) Maintain replacement and smoothing of local trends: For identified collision regions, a local trend preservation strategy is used for signal reconstruction. First, the average difference between the signal before and after the collision region and the baseline is calculated: （11） （12） in, and Representing regions The preceding and following time windows.

[0055] Next, linear interpolation is performed within the collision region to generate a smooth transition signal from the left trend to the right trend: （13） Where t is time, and These are the start times of the collision zone. and end time The signal.

[0056] The trend is overlaid onto the baseline to obtain the reconstructed signal: （14） Finally, Gaussian smoothing is applied to ensure the overall continuity of the reconstructed signal: （15） in, This represents the convolution operation. The standard deviation is The Gaussian kernel function. The processed signal. It retains the overall trend and leakage characteristics of the original signal while effectively removing collision interference.

[0057] Taking the first-order frequency energy curve from a leakage experiment of a metal spherical internal detector under flow conditions of 1 MPa and 1 m / s as an example, the algorithm's processing procedure is as follows: Figure 9 As shown, the algorithm removes multiple collision spikes from the original signal and smooths the extracted signal according to its original trend, laying the foundation for accurate location of the leak segment.

[0058] (2) Leakage determination To determine the leakage area and its precise location, this invention proposes a leakage detection method based on a three-band energy joint threshold decision, using the processing results of a collision elimination algorithm. First, a known leak-free time period during the rolling process of the spherical detector is artificially selected as the baseline interval. The mean and standard deviation of the energy at the first, second, and third resonant frequencies are calculated. Then, based on the statistical normal distribution theory, the energy threshold for each frequency range is set using the 6-σ criterion. The energy signal during normal rolling is used as a sample, and the sample mean plus six times the standard deviation is set as the threshold. Theoretically, there is a 99.99% confidence level that signals exceeding this threshold are outliers, i.e., leakage signals. Finally, the final leakage range is determined by taking the intersection of the potential leakage areas in the three frequency bands. To eliminate the influence of other instantaneous interference, multiple adjacent detected leakage areas are merged. Areas with a time interval of less than 5 seconds are considered as the same leakage event. The area with the longest duration among all merged areas is selected as the final identification result, obtaining the start and end points of the leakage time range. Then, the midpoint of the identified leakage area's time is taken as the leakage point location.

[0059] To verify the effectiveness of the leakage detection method based on the joint threshold decision of three frequency bands, experimental data from a metal spherical internal detector under 1 MPa and 0.5 MPa conditions, and a plastic spherical internal detector under 0.5 MPa conditions, were processed. The results are as follows: Figure 10 As shown. From Figure 10As can be seen, this method can effectively distinguish between leakage signals and background noise, and accurately identify the leakage area.

[0060] (3) Leakage point location To pinpoint the exact location of a pipeline leak, an acceleration-mileage calculation program was developed to determine the leak location, enabling the conversion from time-domain detection to spatial localization. The cumulative rolling distance of the spherical internal detector was calculated under three operating conditions, and a time-to-distance mapping interpolation function was constructed. Using this mapping function, the identified leak event time point can be converted into a spatial mileage point.

[0061] Figure 11 The results of the leakage region transformation from the time domain to the distance domain under three experimental conditions are presented. Specifically, for the metal spherical internal detector, the leakage point was located at 404.44 m under the 1 MPa pressure and 1 m / s flow rate condition, and at 403.49 m under the 0.5 MPa pressure condition. For the plastic spherical internal detector, the leakage point was located at 413.61 m under the 0.5 MPa pressure and 1 m / s flow rate condition.

[0062] The actual leak location measured by high-precision GNSS was 416.89 m. Therefore, the absolute values ​​of the leak location errors in the three experiments were 12.45 m, 13.40 m and 3.28 m, respectively.

[0063] Unless otherwise specified, the model numbers of the various devices in this embodiment of the invention are not limited, and any device that can perform the above functions is acceptable.

[0064] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0065] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A pipe internal detector, characterized in that, The internal detector is a spherical internal detector, which includes: an upper spherical shell, a lower spherical shell, a counterweight ring, a vibration damping rubber ring, an accelerometer, and an electronic module; the internal detector is made of metal for high-pressure conditions and plastic for low-pressure conditions; the spherical structure layout enables the sphere to roll along a fixed axis and record at least the accelerometer data.

2. The pipeline detector according to claim 1, characterized in that, The acoustic cavity partition and microphone group inside the sphere are designed. The microphone group is located inside the cavity to record acoustic data, so that the first three resonant frequencies of the acoustic cavity are three discrete frequencies less than 5000Hz, covering the low frequency band of the leakage sound signal.

3. A pipe internal detector according to claim 1, characterized in that, The metal spherical internal detector is used for: opening the housing for function control and status display, copying files via a data cable, and wired charging via a battery cable; The plastic spherical internal detector employs a wireless communication and charging scheme, enabling remote control and data transmission via a wireless communication module, and integrates wireless charging functionality.

4. A method for calculating the mileage of a leak point, characterized in that, The method is based on the pipe-in-the-pipe detector according to any one of claims 1-3, and the method includes: The raw acceleration signal acquired by the spherical internal detector is preprocessed, and a median filter is used to remove sudden glitch interference. Then, a second-order Butterworth high-pass filter is used to eliminate DC bias. By setting positive and negative thresholds, the complete cycle of the X-axis acceleration signal is divided into four state transition processes. The time interval between adjacent cycles is recorded to calculate the instantaneous frequency, thereby obtaining the instantaneous motion velocity of the spherical detector. The motion distance is obtained by summing the circumferences corresponding to each complete cycle; thus eliminating the cumulative error and removing the interference caused by the collision between the spherical inner detector and the tube wall in the acoustic signal. Leakage identification is performed using a three-band energy joint threshold decision method. Background sound under normal rolling conditions is selected as a reference to establish a statistical benchmark. The energy threshold for each frequency range is set based on the 6-σ criterion. The final leakage range is determined by taking the intersection of the potential leakage areas of the three frequency bands. The midpoint of the identified leak area is taken as the leak time point, and the mileage of the leak point is determined by combining the mileage-time curve.

5. The method for calculating the mileage of a leak point according to claim 4, characterized in that, The complete cycle is: Once the detected signal completes four state transitions in sequence—crossing the lower threshold from bottom to top, crossing the upper threshold from bottom to top, crossing the upper threshold from top to bottom, and crossing the lower threshold from top to bottom—one complete rolling cycle of recognition is achieved.

6. The method for calculating the mileage of a leak point according to claim 5, characterized in that, The instantaneous period is calculated by recording the time interval between adjacent periods. Thus, the instantaneous frequency is obtained. Frequency and circumference of the sphere C Multiplying these values ​​yields the instantaneous velocity of the spherical internal detector. .

7. The method for calculating the mileage of a leak point according to claim 5, characterized in that, The characteristic signal generated by the spherical internal detector when passing through a bend is used as a spatial marker point for mileage correction. The location of the bend is determined by GNSS measurement. When the spherical detector passes through the bend, these characteristic signals are recorded and the corresponding time points are mapped to the known physical location of the bend. The time-distance mapping is optimized to obtain the positioning result.

8. The method for calculating the mileage of a leak point according to claim 7, characterized in that, The mileage correction is as follows: segmented linear stretching. When the detector moves from bend i to bend i+1, the mileage wheel travels Δs_i, and the distance between bend i and bend i+1 is ΔL_i. If Δs_i < ΔL_i, the mileage wheel reading is too small. The position of all data points in this segment is enlarged proportionally by ΔL_i / Δs_i, stretching the record of Δs_i to ΔL_i.

9. The method for calculating the mileage of a leak point according to claim 4, characterized in that, The removal of interference from the acoustic signal caused by the collision between the spherical internal detector and the tube wall includes: wavelet denoising and baseline estimation; gradient calculation and edge detection; collision event detection and connection; amplitude deviation analysis of the collision region; and local trend preservation replacement and smoothing.

10. The method for calculating the mileage of a leak point according to claim 4, characterized in that, The method calculates the cumulative rolling distance of the spherical internal detector under three operating conditions and constructs a time-to-distance mapping interpolation function; using the mapping interpolation function, the time points of the identified leakage events are converted into spatial mileage points.

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