Method and system for measuring the running speed and mileage of an in-pipe detector

Abstract

The application relates to the technical field of pipeline detection, in particular to a method and system for measuring the running speed and mileage of an inner detector in a pipeline. The method comprises the following steps: acquiring vibration signal data collected by an inertial measurement unit during the running of the inner detector in the pipeline; identifying and extracting a plurality of vibration signal pairs generated when the inner detector passes through a weld seam of the pipeline from the vibration signal data; calculating the time difference between the first vibration signal and the second vibration signal in each vibration signal pair; calculating the instantaneous speed of the inner detector when passing through the current weld seam according to the preset distance between the front part and the rear part of the inner detector and the time difference; and calculating the real-time running speed and mileage of the inner detector based on the plurality of instantaneous speeds, the attitude data and the acceleration data collected by the inertial measurement unit, and through a data fusion algorithm, so as to solve the problem of inaccurate trajectory calculation and defect positioning of the inner detector.

Description

Technical Field

[0001] This application relates to the field of pipeline inspection technology, and in particular to a method and system for measuring the operating speed and mileage of an in-pipe detector. Background Technology

[0002] In pipeline inspection technology, internal detectors need to move along with the medium (such as oil or gas) inside the pipeline to detect defects in the pipe wall. During this process, real-time and accurate measurement of the detector's own speed and cumulative mileage is a crucial prerequisite for subsequently calculating its precise trajectory within the pipeline and accurately locating the detected defects at their actual positions. Therefore, there is an urgent need for a technology that can stably and reliably measure the speed and mileage of internal detectors in the complex internal environment of pipelines.

[0003] To measure the speed and mileage of an internal detector, a mechanical odometer wheel scheme is currently commonly used. This scheme typically involves mounting three odometer wheels on the detector, with the wheels pressed against the inner wall of the pipe by a support structure and springs. As the detector moves within the pipe, the odometer wheels rotate under the influence of friction. An integrated displacement encoder generates displacement pulses corresponding to the wheel's rotation; the mileage of the internal detector is measured by counting these pulses.

[0004] However, the measurement methods described above, which rely on mechanical odometer wheels, are severely limited in accuracy and reliability by the physical environment inside the pipeline. Impurities such as sludge and iron filings may exist on the pipeline's inner wall, or the inner wall itself may have varying degrees of smoothness, easily leading to slippage, wear, or jamming of the odometer wheel. Slippage results in lower speed and mileage measurements; wear alters the effective diameter of the odometer wheel, causing systematic errors in mileage calculation; and jamming causes complete loss of mileage data. These problems, stemming from the inherent limitations of the mechanical contact measurement principle, directly affect the accuracy of internal detector trajectory calculation and defect location, constituting a reliability bottleneck that urgently needs to be overcome in internal detection technology. Summary of the Invention

[0005] This application provides a method and system for measuring the operating speed and mileage of an internal pipeline detector, in order to solve the problem of inaccurate trajectory calculation and defect location of the internal detector.

[0006] The first aspect of this application provides a method for measuring the operating speed and mileage of an in-pipe detector, the method comprising: Acquire vibration signal data collected by the inertial measurement unit during the operation of the internal detector inside the pipeline; Multiple vibration signal pairs generated by the internal detector when passing through the pipe weld are identified and extracted from the vibration signal data, wherein each vibration signal pair includes a first vibration signal generated by the front part of the internal detector passing through the current weld and a second vibration signal generated by the rear part of the internal detector passing through the current weld; For each pair of vibration signals, calculate the time difference between the first vibration signal and the second vibration signal; The instantaneous velocity of the internal detector when it passes through the current weld is calculated based on the preset distance between the front and rear components of the internal detector and the time difference. Based on multiple instantaneous velocities, and combined with attitude and acceleration data collected by the inertial measurement unit, the real-time operating speed and mileage of the internal detector are calculated through a data fusion algorithm.

[0007] This application utilizes an inertial measurement unit to collect vibration signals from the internal detector as it passes through a weld seam, and extracts a pair of vibration signals generated by the preceding and following components passing through the same weld seam. The time difference between these signals is calculated, and combined with the preset distance between the preceding and following components, the instantaneous velocity of the detector as it passes through the weld seam can be accurately obtained. Further integration of attitude and acceleration data for calculation can effectively improve the accuracy of real-time speed and mileage measurement of the internal detector. This method does not rely on a mechanical odometer wheel, which helps alleviate inaccurate speed measurements and mileage loss caused by odometer wheel wear, slippage, or jamming, providing more reliable data support for trajectory calculation and defect localization of the internal detector.

[0008] Optionally, the front component is the front cup of the internal detector, the rear component is the rear cup of the internal detector, and the preset distance is the axial distance between the front cup and the rear cup.

[0009] This application clarifies the structural basis for distance and velocity measurement using vibration signals across weld seams by specifically defining the front and rear components as the front and rear cups of the internal detector, and using the axial distance between them as the preset distance. Based on the inherent physical process of vibration signals generated when the front and rear cups pass through the weld seam, this scheme can reliably acquire the time difference and calculate the instantaneous velocity, thereby improving the measurement accuracy of the internal detector's operating speed and mileage, and providing more stable data support for subsequent trajectory calculation and defect location.

[0010] Optionally, identifying and extracting multiple vibration signal pairs generated when the internal detector passes through the pipe weld includes: detecting signal peaks that conform to preset vibration characteristics in the vibration signal data, and identifying two signal peaks that appear consecutively in time as one vibration signal pair.

[0011] This application detects signal peaks that conform to preset vibration characteristics in vibration signal data and identifies two consecutively occurring peaks as a vibration signal pair. This allows for the accurate extraction of paired vibration signals generated when the front and rear components of the internal detector pass through the same weld seam from IMU data. This identification method fully utilizes the inherent characteristics of weld seam signals, helping to improve the accuracy and efficiency of vibration signal pair extraction. It provides a reliable data foundation for subsequent instantaneous velocity calculation based on time difference, thereby improving the measurement accuracy of the internal detector's operating speed and mileage.

[0012] Optionally, calculating the time difference between the first vibration signal and the second vibration signal includes: calculating the interval between the peak point of the first vibration signal and the peak point of the second vibration signal on the time axis, as the time difference.

[0013] This application calculates the time difference between the peak points of the first and second vibration signals on the time axis, enabling precise quantification of the time interval between the passage of the inner detector's components through the same weld seam. This calculation method, based on signal peak values, helps reduce the impact of noise interference on time difference measurement, improving the accuracy and stability of time difference extraction. Based on this time difference and a preset distance between the front and rear components, the instantaneous velocity of the inner detector as it passes through the weld seam can be calculated more reliably, providing more accurate input parameters for subsequent calculations of operating speed and mileage, thus improving the accuracy of the measurement results.

[0014] Optionally, the data fusion algorithm is Kalman filtering.

[0015] This application employs Kalman filtering as a data fusion algorithm, which effectively integrates instantaneous velocity calculated based on weld vibration signals with attitude and acceleration data collected by the inertial measurement unit. This algorithm comprehensively utilizes the characteristics of multi-source data to optimize the estimation results of the real-time operating speed and mileage of the internal detector, helping to improve the continuity and stability of measurement data and enhance the accuracy of the speed and mileage information relied upon for trajectory calculation and defect location.

[0016] Optionally, the method further includes: comparing and cross-verifying the calculated real-time running speed and mileage with the speed and mileage measured by the mechanical mileage wheel set on the internal detector, so as to obtain the verified running speed and mileage.

[0017] This application effectively integrates measurement results from two different principles by comparing and cross-verifying the real-time operating speed and mileage calculated based on IMU vibration signals with the speed and mileage measured by a mechanical odometer wheel on the internal detector. This cross-verification mechanism helps identify abnormal fluctuations that may exist in the measurement data from a single source. Through mutual verification of the data, the overall reliability and accuracy of the operating speed and mileage output can be improved, providing more robust data support for trajectory calculation and defect location of the internal detector.

[0018] Optionally, the inertial measurement unit is integrated into the internal detector and is used to collect the angular velocity and acceleration information of the internal detector in three-dimensional space in real time to generate the vibration signal data, attitude data and acceleration data.

[0019] This application integrates an inertial measurement unit (IMU) into an internal detector, enabling it to acquire angular velocity and acceleration information of the internal detector in three-dimensional space in real time, and generate vibration signal data, attitude data, and acceleration data. This integration method achieves unified acquisition of multiple key data, providing a unified source of raw data for subsequent calculations of instantaneous velocity based on vibration signals and for solving operating speed and mileage by fusing attitude and acceleration data. This helps improve the synchronization and coordination of data acquisition and enhances the data foundation for monitoring and calculating the operating status of the internal detector.

[0020] Optionally, the real-time operating speed and mileage of the internal detector are calculated using a data fusion algorithm based on multiple instantaneous velocities and combined with attitude and acceleration data collected by the inertial measurement unit, including: Using multiple instantaneous velocities as observations, the velocity obtained by integrating the acceleration data from the inertial measurement unit is corrected; The attitude data and acceleration data are integrated using the corrected velocity to calculate the real-time motion trajectory of the internal detector, and the mileage is obtained by summing the motion trajectory.

[0021] This application corrects the velocity obtained by integrating acceleration data from an inertial measurement unit (IMU) using multiple instantaneous velocities as observations. The corrected velocity is then used to integrate attitude and acceleration data to calculate the real-time motion trajectory and accumulate the distance traveled. This method effectively suppresses the error accumulation caused by relying solely on acceleration integration. By introducing instantaneous velocities based on weld vibration signals as external observations and periodically correcting the velocity, the accuracy of trajectory and distance travel calculations is improved, providing more reliable measurement results for the internal detector's routing and positioning.

[0022] Optionally, the detector is an in-pipe detector equipped with a single row of probes or a pipeline pig.

[0023] This application expands the applicability of the speed and mileage measurement scheme by applying the method to pipeline detectors or pipeline pigs equipped with single-row probes. For detection devices equipped with only a single-row probe, speed and distance measurement based on IMU vibration signals can be achieved without adding additional hardware, helping to reduce equipment complexity. Simultaneously, this method can also be applied to pipeline pigs, enabling them to acquire operating speed and mileage information without the need for mileage wheels, thus improving the condition monitoring capabilities of such devices during pipeline operation.

[0024] The second aspect of this application provides a system for measuring the operating speed and mileage of a pipeline detector, applicable to the method for measuring the operating speed and mileage of a pipeline detector described in the first aspect, comprising: The data processing module is used to acquire vibration signal data collected by the inertial measurement unit during the operation of the internal detector inside the pipeline; The signal recognition module identifies and extracts multiple vibration signal pairs generated by the internal detector when it passes through the pipe weld from the vibration signal data. Each vibration signal pair includes a first vibration signal generated by the front part of the internal detector passing through the current weld and a second vibration signal generated by the rear part of the internal detector passing through the current weld. The time calculation module is used to calculate the time difference between the first vibration signal and the second vibration signal for each vibration signal pair; The weld seam instantaneous velocity calculation module is used to calculate the instantaneous velocity of the inner detector when it passes through the current weld seam based on the preset distance between the front part and the rear part of the inner detector and the time difference. The fusion calculation module is used to calculate the real-time operating speed and mileage of the internal detector based on multiple instantaneous velocities and in combination with attitude data and acceleration data collected by the inertial measurement unit through a data fusion algorithm.

[0025] This application provides a complete hardware logic architecture for measuring the operating speed and mileage of pipeline detectors by constructing a measurement system that includes a data processing module, a signal recognition module, a time calculation module, a weld instantaneous velocity calculation module, and a fusion calculation module. This system can automatically complete the entire process from IMU vibration signal acquisition, weld signal recognition, time difference calculation, instantaneous velocity solution to multi-source data fusion, which helps improve the integration and processing efficiency of the measurement process, and enhances the accuracy and stability of the internal detector's operating speed and mileage measurement.

[0026] As can be seen from the above technical solutions, this application provides a method and system for measuring the operating speed and mileage of an in-pipe detector. The method includes: acquiring vibration signal data collected by an inertial measurement unit during the operation of the in-pipe detector; identifying and extracting multiple vibration signal pairs generated by the in-pipe detector when passing through a pipe weld from the vibration signal data, wherein each vibration signal pair includes a first vibration signal generated by the front part of the in-pipe detector passing through the current weld and a second vibration signal generated by the rear part of the in-pipe detector passing through the current weld; calculating the time difference between the first vibration signal and the second vibration signal for each vibration signal pair; calculating the instantaneous speed of the in-pipe detector when passing through the current weld based on a preset distance between the front part and the rear part of the in-pipe detector and the time difference; and calculating the real-time operating speed and mileage of the in-pipe detector based on multiple instantaneous speeds and in combination with attitude data and acceleration data collected by the inertial measurement unit through a data fusion algorithm, so as to solve the problem of inaccurate trajectory calculation and defect location of the in-pipe detector. Attached Figure Description

[0027] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A schematic flowchart illustrating the method for measuring the operating speed and mileage of an in-pipe detector provided in an embodiment of this application; Figure 2 A schematic diagram of the detector passing through the weld seam in the method for measuring the operating speed and mileage of the detector in the pipeline provided in the embodiments of this application; Figure 3 This is a schematic diagram showing that the detector IMU data contains a large number of vibration signals passing through the weld seam in a single detection in the method for measuring the operating speed and mileage of the pipeline detector provided in the embodiments of this application. Figure 4 A schematic diagram illustrating that each weld seam of the detector passing through the pipeline provides a method for measuring the operating speed and mileage of the detector in an embodiment of this application contains two vibration signals; Figure 5 A schematic diagram of the time difference of vibration signals of components before and after the detector passes through the weld seam in the method for measuring the running speed and mileage of the detector in the pipeline provided in the embodiments of this application; Figure 6 This is a schematic diagram comparing the detector speed calculated from the vibration signal of each weld with the speed of the mileage wheel in the method for measuring the running speed and mileage of the detector in the pipeline provided in the embodiments of this application. Detailed Implementation

[0029] The embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described below do not represent all embodiments consistent with this application. They are merely examples of systems and methods consistent with some aspects of this application.

[0030] To address the issue of inaccurate trajectory calculation and defect localization for internal detectors, see [reference needed]. Figures 1-6 This application provides a method for measuring the operating speed and mileage of an in-pipe detector in certain embodiments. The method includes:

[0031] S100: Acquire vibration signal data collected by the inertial measurement unit during the operation of the internal detector inside the pipeline.

[0032] The inertial measurement unit (IMU) is typically integrated into the core detection module of the internal detector, containing a three-axis accelerometer and a three-axis gyroscope. The three-axis accelerometer collects vibration acceleration signals generated by the detector in the axial, radial, and circumferential directions of the pipe due to factors such as unevenness of the pipe's inner wall, welds, bends, or friction between the detector's own diaphragm and the pipe's inner wall. The three-axis gyroscope collects information on the detector's angular velocity changes during operation, assisting in capturing attitude changes during vibration. The collected vibration signal data is a continuous time-domain signal with a sampling frequency of no less than 600Hz to ensure accurate capture of transient vibration characteristics generated when feature points such as welds pass through. This vibration signal data is stored in the internal detector's local data storage unit and can be exported to external analysis equipment via a subsequent data transmission interface.

[0033] S200: Identify and extract multiple vibration signal pairs generated by the internal detector when it passes through the pipe weld from the vibration signal data.

[0034] Each vibration signal pair includes a first vibration signal generated by the front component of the inner detector passing through the current weld and a second vibration signal generated by the rear component of the inner detector passing through the current weld.

[0035] It should be understood that the "front component" and "rear component" here specifically refer to two key structures on the inner detector that have a fixed, known distance (denoted as L, usually in meters) in the axial direction. These two components are usually two independent vibration-sensitive units arranged one in front of the other on the main body of the inner detector along its direction of travel, or they refer to two specific structures on the inner detector that contact the inner wall of the pipe and can generate identifiable vibration characteristics, such as two sets of front and rear cups, two front and rear detection wheels, or two independent IMU modules integrated at different positions at the front and rear of the detector (although S100 mentions that the IMU is usually integrated into the core detection module, in some designs, to obtain more accurate relative distance information, a small IMU or vibration sensor may be arranged at each of the front and rear ends of the detector). When the inner detector travels in the pipe, the front component first passes through the weld, triggering the first vibration signal; as the detector continues to move forward, after traveling a distance L, the rear component arrives at and passes through the same weld, triggering the second vibration signal. These two vibration signals generated successively and caused by the same weld constitute a "vibration signal pair". By identifying such a signal pair, the operating speed of the detector can be calculated based on the time difference between their generation.

[0036] In some embodiments, identifying and extracting multiple vibration signal pairs generated when the internal detector passes through the pipe weld includes: detecting signal peaks that conform to preset vibration characteristics in the vibration signal data, and identifying two signal peaks that appear consecutively in time as a vibration signal pair.

[0037] It should be understood that "preset vibration characteristics" are typically set based on the physical characteristics of the pipe weld and the mechanism of interaction between the internal detector and the weld. Specifically, this can include the following structural and parametric characteristics: First, the amplitude characteristics of the signal. Because the weld forms a physical protrusion or discontinuity relative to the smooth surface of the pipe's inner wall, a relatively large vibration impact is generated when the sensitive components of the detector (such as a cup, detection wheel, or vibration sensor) pass over it. Therefore, the preset vibration characteristics include an amplitude threshold significantly higher than the background noise level. Only when the peak value of the vibration signal exceeds this threshold is it considered a potential weld signal. Second, the time-domain characteristics of the signal. The vibration signal caused by the weld typically exhibits a pulse waveform that rises rapidly and then decays quickly, with a short duration. The preset characteristics define a reasonable signal width range, for example, within a few hundred milliseconds, to distinguish it from other possible continuous vibration interference within the pipe. Secondly, there's the frequency characteristic of the signal. Different types of detector components, when interacting with the weld, will excite vibration energy within a specific frequency range. Preset vibration characteristics may include screening for key frequency components, such as using a bandpass filter to extract signal energy in a specific frequency band to enhance the recognizable weld signal. Furthermore, regarding "two consecutive signal peaks in time," "consecutive" does not mean the two peaks are tightly linked in time, but rather that, after excluding other interference signals, two consecutive signal peaks within a reasonable time interval that conform to the aforementioned amplitude, time domain, and frequency characteristics. This time interval range is related to the design length of the internal detector (i.e., the distance L between the two sensitive components) and its possible operating speed range within the pipeline. Typically, a maximum and minimum time interval threshold are set. Only when the time difference between the two peaks falls within this threshold range will they be identified as a vibration signal pair caused by the same weld. For example, if the distance L between the components is 2 meters and the detector's operating speed is between 0.5 m / s and 5 m / s, then the time difference between the two peaks should be between 0.4 seconds and 4 seconds. Peak combinations exceeding this range will be excluded. By combining such multi-dimensional preset vibration characteristic parameters, effective vibration signal pairs can be detected and extracted more accurately from complex vibration signal data.

[0038] This application detects signal peaks that conform to preset vibration characteristics in vibration signal data and identifies two consecutively occurring peaks as a vibration signal pair. This allows for the accurate extraction of paired vibration signals generated when the front and rear components of the internal detector pass through the same weld seam from IMU data. This identification method fully utilizes the inherent characteristics of weld seam signals, helping to improve the accuracy and efficiency of vibration signal pair extraction. It provides a reliable data foundation for subsequent instantaneous velocity calculation based on time difference, thereby improving the measurement accuracy of the internal detector's operating speed and mileage.

[0039] S300: For each vibration signal pair, calculate the time difference between the first vibration signal and the second vibration signal.

[0040] In some embodiments, calculating the time difference between the first vibration signal and the second vibration signal includes: calculating the interval between the peak point of the first vibration signal and the peak point of the second vibration signal on the time axis as the time difference.

[0041] Specifically, for each identified vibration signal pair—the first vibration signal generated by the front component of the internal detector passing through the weld and the second vibration signal generated by the rear component passing through the same weld—the peak points of each signal must first be accurately located on the time-domain waveform diagram. Here, a "peak point" refers to the specific data sampling point in the vibration signal waveform where the vibration amplitude reaches a local maximum. Once these two peak points are determined, their corresponding time values ​​on the time axis can be directly read or calculated. For example, if the time corresponding to the peak point of the first vibration signal is t1, and the time corresponding to the peak point of the second vibration signal is t2, then the time difference Δt between these two vibration signals is defined as t2 minus t1, i.e., Δt = t2 - t1. This time difference Δt intuitively reflects the time interval experienced by the internal detector from the arrival of its front component at the weld position to the arrival of its rear component at the same weld position, and is a key parameter for subsequent calculation of the instantaneous operating speed of the internal detector. In practice, this is usually achieved by using data processing algorithms to traverse the stored vibration signal data and identify feature points, ensuring that the time difference for each valid vibration signal pair can be accurately calculated.

[0042] This application calculates the time difference between the peak points of the first and second vibration signals on the time axis, enabling precise quantification of the time interval between the passage of the inner detector's components through the same weld seam. This calculation method, based on signal peak values, helps reduce the impact of noise interference on time difference measurement, improving the accuracy and stability of time difference extraction. Based on this time difference and a preset distance between the front and rear components, the instantaneous velocity of the inner detector as it passes through the weld seam can be calculated more reliably, providing more accurate input parameters for subsequent calculations of operating speed and mileage, thus improving the accuracy of the measurement results.

[0043] S400: Calculate the instantaneous speed of the inner detector as it passes through the current weld seam based on the preset distance and time difference between the front and rear components of the inner detector.

[0044] Specifically, the preset distance between the front and rear components can be measured directly or based on the distance the mileage wheel travels through the weld seam.

[0045] In some embodiments, the front component is the front cup of the internal detector, the rear component is the rear cup of the internal detector, and the preset distance is the axial distance between the front cup and the rear cup.

[0046] It should be understood that the cups of the internal detector are an important component, typically made of elastic and wear-resistant materials (such as polyurethane) and have a bowl-shaped structure. The front cup is located at the front end of the internal detector, while the rear cup is located relatively rearward. They maintain a fixed mounting distance along the detector's axis. This preset axial distance is a known parameter determined during the detector's design and assembly phases, and can be obtained, for example, by accurately measuring the distance between the two cup mounting brackets. When the internal detector operates within the pipe, the front cup first contacts and seals against the pipe's inner wall, followed by the rear cup in the same manner. Since the front and rear cups are part of the overall structure of the internal detector, they move together within the pipe. Therefore, when the internal detector passes a weld in the pipe, the front cup will pass through the weld before the rear cup. The time difference between their passing through the same weld, combined with the fixed preset axial distance between them, allows for the accurate deduction of the internal detector's operating speed at the instant it passes the weld. The preset distance is a fixed value determined during the design of the internal detector based on factors such as its overall size, the installation position of the diaphragm cup, and the expected pipeline operating environment. For example, it can be set to 0.5 meters, 1 meter, or other specific lengths. Once the detector is assembled and calibrated before leaving the factory, this distance remains constant throughout the entire detection operation and serves as one of the key basic parameters for subsequent velocity calculations.

[0047] This application clarifies the structural basis for distance and velocity measurement using vibration signals across weld seams by specifically defining the front and rear components as the front and rear cups of the internal detector, and using the axial distance between them as the preset distance. Based on the inherent physical process of vibration signals generated when the front and rear cups pass through the weld seam, this scheme can reliably acquire the time difference and calculate the instantaneous velocity, thereby improving the measurement accuracy of the internal detector's operating speed and mileage, and providing more stable data support for subsequent trajectory calculation and defect location.

[0048] S500: Based on multiple instantaneous velocities and combined with attitude and acceleration data collected by the inertial measurement unit, the real-time operating speed and mileage of the internal detector are calculated through a data fusion algorithm.

[0049] In some embodiments, based on multiple instantaneous velocities and combined with attitude and acceleration data collected by the inertial measurement unit, the real-time operating speed and mileage of the internal detector are calculated by a data fusion algorithm, including: S510: Corrects the velocity obtained by integrating the acceleration data from the inertial measurement unit using multiple instantaneous velocities as observations.

[0050] It should be understood that the core of the above steps lies in constructing a state estimator based on Kalman filtering (or other suitable data fusion algorithms). This state estimator integrates the triaxial acceleration data output from the inertial measurement unit (IMU) twice (once to obtain velocity, and twice to obtain position) as the output of the system's predictive model, while using multiple instantaneous velocities calculated from the vibration signals of the front and rear accelerometer cups over the weld as observation inputs with a certain amount of noise. Structurally, this typically involves establishing the system's state equation and observation equation. The state equation describes the evolution of the internal detector's motion state (such as position, velocity, acceleration, and possibly even error terms like IMU zero bias) over time, primarily driven by the IMU's acceleration measurements. The observation equation establishes the relationship between the velocity component in the state vector and the instantaneous velocity observations obtained by the accelerometer cup vibration method. Through the Kalman filtering prediction-update iterative process, using the relatively accurate instantaneous velocity observations provided by the accelerometer cup vibration method, the velocity estimation bias caused by accumulated errors (such as accelerometer zero drift, noise integration, etc.) during the IMU integration process is continuously corrected, thereby obtaining a more accurate and robust velocity estimation result.

[0051] S520: The corrected velocity is used to integrate the attitude data and acceleration data to calculate the real-time motion trajectory of the internal detector, and the mileage is obtained by accumulating the motion trajectory.

[0052] It should be understood that the "corrected velocity" here refers to the velocity optimized by the aforementioned data fusion algorithm. Attitude data is primarily provided by the gyroscopes in the IMU. By integrating the angular velocity output by the gyroscopes, the attitude angles (such as roll, pitch, and yaw) of the internal detector in space can be obtained. These attitude angles are used to transform the acceleration measured by the IMU in the body coordinate system to the geodetic coordinate system (or inertial coordinate system). Specifically, firstly, using the corrected velocity and the angular velocity data output by the IMU, the real-time attitude of the internal detector is calculated more accurately using attitude update algorithms (such as quaternion methods, Euler angle methods, etc.). Then, the three-axis acceleration data collected by the IMU in the body coordinate system is subtracted from the component of gravitational acceleration in the body coordinate system (this requires precise attitude angle information) to obtain the motion acceleration of the internal detector. Combining this with the corrected velocity as an initial condition, the motion acceleration is integrated in the geodetic coordinate system to obtain a more accurate velocity in the geodetic coordinate system. Furthermore, by integrating the corrected velocity in the geodetic coordinate system, the real-time position coordinates of the internal detector in the geodetic coordinate system can be calculated, thus forming the real-time motion trajectory. Finally, the mileage is calculated by summing the magnitudes of the displacement vectors within each small time interval of this motion trajectory, thereby obtaining the total mileage traveled by the internal detector along the pipeline. In this process, the corrected velocity not only directly improves the accuracy of the velocity itself, but also indirectly improves the accuracy of position and mileage calculations by affecting attitude calculation and acceleration integration.

[0053] Specifically, the relationship between the instantaneous velocity difference between two adjacent welds and the IMU acceleration integral is as follows: ; in, The instantaneous velocity is calculated when passing through the two welds, and is a known value. Specific force measured by IMU Let g be the direction cosine matrix from the navigation frame to the vehicle coordinate system (internal detector body coordinate system), and g be the gravity vector. This is the constant zero bias of the IMU accelerometer.

[0054] Using the above formula, terms unrelated to motion acceleration (such as gravitational components and zero bias) can be calculated or corrected using known velocity differences, thereby obtaining a more accurate axial motion acceleration.

[0055] Secondly, a Kalman filter system is constructed to fuse the corrected axial velocity with the attitude, angular velocity and other data of the IMU in order to calculate the real-time three-dimensional motion velocity, mileage and trajectory of the internal detector.

[0056] This Kalman filter system operates under non-integrity constraints (NHC) on the pipeline. In the internal detector body coordinate system (b-frame), its velocity can be expressed as: ; in, The real-time axial velocity is obtained from weld information and IMU integration.

[0057] The system's state vector Defined as a 15-dimensional vector containing IMU errors, as shown in the following formula: ; in, , , , , The errors are, in order, position error, velocity error, attitude error, gyroscope bias, and accelerometer bias in the navigation system (n-system).

[0058] The equation for the evolution of the state over time is: ; in, The state transition matrix, which is related to the time interval Δt, can be expressed as: =I+FΔt. F is the dynamic matrix of the system, which has the following form: ; in, It is a 3×3 identity matrix; For comparison under navigation systems, Let be the projection of the angular velocity of the navigation frame n relative to the inertial frame (i) onto the navigation frame. and Represent the corresponding antisymmetric matrix; It is a 3×3 zero matrix.

[0059] The system's observation equations are: ; in, The observable is the projection of the velocity calculated by the IMU onto the b-frame. Compared with axial velocity observations difference; To observe noise.

[0060] The equation can be written in matrix form: z = Hx + The observation matrix H is: .

[0061] Finally, the Kalman filter system constructed based on the state equation and the observation equation is as follows: ; And it is calculated according to the standard prediction-update iteration formula. Wherein, This is the state vector for the next time step; This is the state transition matrix; This is the current state vector; This is IMU noise; For observation vectors; The observation matrix; To observe noise.

[0062] Kalman system iterative formula: ; in, Let be the prior state covariance matrix, representing the state at time t. k+1 An estimate of the uncertainty of the system state before observation; Let be the state transition matrix, describing the system from time t. k arrive k+1 The physical evolution model; Let be the posterior state covariance matrix, representing the state at time t. k The uncertainty of the system state after measurement and update; for The transpose of the matrix; The process noise covariance matrix; The Kalman gain matrix; The observation matrix; For measuring noise; Let be the posterior state covariance matrix, representing the state at time t. k+1 The final uncertainty estimate of the system state after combining the measurement data; It is the identity matrix; The state estimation vector is the output of the Kalman filter, i.e., at time t. k+1 The optimal estimate of the true state of the system.

[0063] Through iterative Kalman filtering, the system calculates the axial velocity based on the observed values. Continuously adjust the state error estimation of the IMU This allows for more accurate estimations of attitude, velocity, and position (mileage) errors. The corrected state error is then used to correct the original IMU integration results (including the velocity obtained from acceleration integration and the position obtained from quadratic integration), ultimately calculating the high-precision real-time three-dimensional motion velocity, cumulative mileage, and motion trajectory of the internal detector within the pipe.

[0064] This application utilizes an inertial measurement unit to collect vibration signals from the internal detector as it passes through a weld seam, and extracts a pair of vibration signals generated by the preceding and following components passing through the same weld seam. The time difference between these signals is calculated, and combined with the preset distance between the preceding and following components, the instantaneous velocity of the detector as it passes through the weld seam can be accurately obtained. Further integration of attitude and acceleration data for calculation can effectively improve the accuracy of real-time speed and mileage measurement of the internal detector. This method does not rely on a mechanical odometer wheel, which helps alleviate inaccurate speed measurements and mileage loss caused by odometer wheel wear, slippage, or jamming, providing more reliable data support for trajectory calculation and defect localization of the internal detector.

[0065] In some embodiments, the data fusion algorithm is Kalman filtering.

[0066] It should be understood that Kalman filtering, as a recursive state estimation algorithm, in the data fusion process of this application, focuses on constructing the system's state equation and observation equation, and achieving optimal estimation of detector velocity and mileage through two main steps: prediction and update. Specifically, the system state vector typically includes key parameters such as the detector's real-time velocity, position (mileage), and possible acceleration deviations. The state equation is used to predict the system state at the next moment based on the current state. It combines the acceleration information output by the inertial measurement unit and considers the influence of system process noise (such as inherent random errors of the sensor, disturbances caused by unevenness of the pipe inner wall, etc.). The observation equation is used to correlate the instantaneous velocity (as an observation) calculated from the weld vibration signal with the predicted state. This observation is also subject to measurement noise (such as errors in the vibration signal extraction process, small deviations in the preset value of the weld spacing, etc.). In the prediction step, the algorithm predicts a prior estimate of the current state based on the state transition matrix and control input (primarily acceleration). In the update step, the algorithm calculates the Kalman gain, weights and fuses the prior estimate with the actual observations to obtain a posterior estimate of the state, and updates the error covariance matrix to prepare for the next iteration. Through this continuous prediction and update cycle, Kalman filtering can effectively fuse continuous attitude and acceleration data from the inertial measurement unit and discrete instantaneous velocity data obtained based on weld vibration, dynamically correcting accumulated errors, thereby obtaining smoother, more accurate, and robust velocity and odometry estimates.

[0067] This application employs Kalman filtering as a data fusion algorithm, which effectively integrates instantaneous velocity calculated based on weld vibration signals with attitude and acceleration data collected by the inertial measurement unit. This algorithm comprehensively utilizes the characteristics of multi-source data to optimize the estimation results of the real-time operating speed and mileage of the internal detector, helping to improve the continuity and stability of measurement data and enhance the accuracy of the speed and mileage information relied upon for trajectory calculation and defect location.

[0068] In some embodiments, the method further includes: comparing and cross-verifying the calculated real-time running speed and mileage with the speed and mileage measured by the mechanical mileage wheel set on the internal detector, so as to obtain the verified running speed and mileage.

[0069] This application effectively integrates measurement results from two different principles by comparing and cross-verifying the real-time operating speed and mileage calculated based on IMU vibration signals with the speed and mileage measured by a mechanical odometer wheel on the internal detector. This cross-verification mechanism helps identify abnormal fluctuations that may exist in the measurement data from a single source. Through mutual verification of the data, the overall reliability and accuracy of the operating speed and mileage output can be improved, providing more robust data support for trajectory calculation and defect location of the internal detector.

[0070] In some embodiments, the inertial measurement unit is integrated into the internal detector to acquire the angular velocity and acceleration information of the internal detector in three-dimensional space in real time, so as to generate vibration signal data, attitude data and acceleration data.

[0071] It should be understood that "vibration signal data" mainly refers to the acceleration change signals generated by the internal detector during its operation within the pipeline, due to factors such as friction with the pipeline wall, collision, medium disturbance, and its own mechanical vibration, collected by the accelerometer in the inertial measurement unit. These signals can reflect, to a certain extent, the motion state of the internal detector and the roughness of the pipeline wall. "Attitude data" refers to the real-time attitude angles of the internal detector in three-dimensional space, calculated by the inertial measurement unit through processing the collected angular velocity and acceleration data (e.g., based on strapdown inertial navigation algorithms). These angles typically include roll, pitch, and yaw, describing the rotational attitude of the internal detector relative to the reference coordinate system. "Acceleration data," in addition to containing the components mentioned above used for extracting vibration features, focuses more on the overall change of linear acceleration along the three axes of the internal detector during its motion. It is one of the original physical quantities for calculating velocity and position (mileage). By performing integration and other processing, the motion velocity and displacement information of the internal detector can be obtained.

[0072] This application integrates an inertial measurement unit (IMU) into an internal detector, enabling it to acquire angular velocity and acceleration information of the internal detector in three-dimensional space in real time, and generate vibration signal data, attitude data, and acceleration data. This integration method achieves unified acquisition of multiple key data, providing a unified source of raw data for subsequent calculations of instantaneous velocity based on vibration signals and for solving operating speed and mileage by fusing attitude and acceleration data. This helps improve the synchronization and coordination of data acquisition and enhances the data foundation for monitoring and calculating the operating status of the internal detector.

[0073] In some embodiments, the detector is an in-pipe detector or a pipe cleaner equipped with a single row of probes.

[0074] It should be understood that "single-row probes" specifically refers to a row of probes arranged at uniform or specific intervals along the circumferential direction of the detector (e.g., magnetic flux leakage probes, ultrasonic probes, etc., used to detect pipeline defects). This arrangement is typically suitable for spiral scanning inspection of the inner wall of a pipeline. As the detector moves inside the pipeline and rotates simultaneously, the single-row probes gradually cover the entire circumferential area of ​​the inner wall of the pipeline, thus achieving full circumferential inspection. Compared to multi-row probe arrangements, single-row probes can simplify the structural design of the detector to some extent, reduce hardware costs and assembly complexity, while still meeting the basic requirement for comprehensive inspection of the inner wall of the pipeline. This is particularly valuable in scenarios with specific requirements for detection accuracy and coverage, and where cost control is stringent.

[0075] This application expands the applicability of the speed and mileage measurement scheme by applying the method to pipeline detectors or pipeline pigs equipped with single-row probes. For detection devices equipped with only a single-row probe, speed and distance measurement based on IMU vibration signals can be achieved without adding additional hardware, helping to reduce equipment complexity. Simultaneously, this method can also be applied to pipeline pigs, enabling them to acquire operating speed and mileage information without the need for mileage wheels, thus improving the condition monitoring capabilities of such devices during pipeline operation.

[0076] This application also provides a measurement system for the operating speed and mileage of a pipeline detector in certain embodiments, applicable to the measurement method for the operating speed and mileage of a pipeline detector provided in the above embodiments, including: The data processing module is used to acquire vibration signal data collected by the inertial measurement unit during the operation of the internal detector inside the pipeline; The signal recognition module identifies and extracts multiple vibration signal pairs generated by the internal detector when it passes through the pipe weld from the vibration signal data. Each vibration signal pair includes a first vibration signal generated by the front part of the internal detector passing through the current weld and a second vibration signal generated by the rear part of the internal detector passing through the current weld. The time calculation module is used to calculate the time difference between the first vibration signal and the second vibration signal for each vibration signal pair; The weld seam instantaneous velocity calculation module is used to calculate the instantaneous velocity of the internal detector when it passes through the current weld seam based on the preset distance and time difference between the front and rear parts of the internal detector. The fusion computing module is used to calculate the real-time operating speed and mileage of the internal detector based on multiple instantaneous velocities and combined with attitude and acceleration data collected by the inertial measurement unit through a data fusion algorithm.

[0077] This application provides a complete hardware logic architecture for measuring the operating speed and mileage of pipeline detectors by constructing a measurement system that includes a data processing module, a signal recognition module, a time calculation module, a weld instantaneous velocity calculation module, and a fusion calculation module. This system can automatically complete the entire process from IMU vibration signal acquisition, weld signal recognition, time difference calculation, instantaneous velocity solution to multi-source data fusion, which helps improve the integration and processing efficiency of the measurement process, and enhances the accuracy and stability of the internal detector's operating speed and mileage measurement.

[0078] As can be seen from the above technical solutions, the embodiments of this application provide a method and system for measuring the operating speed and mileage of an in-pipe detector. The method includes: acquiring vibration signal data collected by an inertial measurement unit during the operation of the in-pipe detector; identifying and extracting multiple vibration signal pairs generated by the in-pipe detector when passing through the pipe weld from the vibration signal data, wherein each vibration signal pair includes a first vibration signal generated by the front part of the in-pipe detector passing through the current weld and a second vibration signal generated by the rear part of the in-pipe detector passing through the current weld; calculating the time difference between the first vibration signal and the second vibration signal for each vibration signal pair; calculating the instantaneous speed of the in-pipe detector when passing through the current weld based on the preset distance and time difference between the front and rear parts of the in-pipe detector; and calculating the real-time operating speed and mileage of the in-pipe detector through a data fusion algorithm based on multiple instantaneous speeds and combined with attitude data and acceleration data collected by the inertial measurement unit, so as to solve the problem of inaccurate trajectory calculation and defect location of the in-pipe detector.

Claims

1. A method for measuring the operating speed and mileage of an in-pipe detector, characterized in that, The method includes: Acquire vibration signal data collected by the inertial measurement unit during the operation of the internal detector inside the pipeline; Multiple vibration signal pairs generated by the internal detector when passing through the pipe weld are identified and extracted from the vibration signal data, wherein each vibration signal pair includes a first vibration signal generated by the front part of the internal detector passing through the current weld and a second vibration signal generated by the rear part of the internal detector passing through the current weld; For each pair of vibration signals, calculate the time difference between the first vibration signal and the second vibration signal; The instantaneous velocity of the internal detector when it passes through the current weld is calculated based on the preset distance between the front and rear components of the internal detector and the time difference. Based on multiple instantaneous velocities, and combined with attitude and acceleration data collected by the inertial measurement unit, the real-time operating speed and mileage of the internal detector are calculated through a data fusion algorithm.

2. The method for measuring the operating speed and mileage of the pipeline detector according to claim 1, characterized in that, The front component is the front diaphragm of the internal detector, the rear component is the rear diaphragm of the internal detector, and the preset distance is the axial distance between the front diaphragm and the rear diaphragm.

3. The method for measuring the operating speed and mileage of the pipeline detector according to claim 1, characterized in that, The step of identifying and extracting multiple vibration signal pairs generated when the internal detector passes through the pipe weld includes: detecting signal peaks that conform to preset vibration characteristics in the vibration signal data, and identifying two signal peaks that appear consecutively in time as one vibration signal pair.

4. The method for measuring the operating speed and mileage of the pipeline detector according to claim 1, characterized in that, The calculation of the time difference between the first vibration signal and the second vibration signal includes: calculating the interval between the peak point of the first vibration signal and the peak point of the second vibration signal on the time axis, as the time difference.

5. The method for measuring the operating speed and mileage of the pipeline detector according to claim 1, characterized in that, The data fusion algorithm is Kalman filtering.

6. The method for measuring the operating speed and mileage of the pipeline detector according to claim 1, characterized in that, The method further includes: comparing and verifying the calculated real-time running speed and mileage with the speed and mileage measured by the mechanical mileage wheel set on the internal detector, so as to obtain the verified running speed and mileage.

7. The method for measuring the operating speed and mileage of the pipeline detector according to claim 1, characterized in that, The inertial measurement unit is integrated into the internal detector and is used to collect the angular velocity and acceleration information of the internal detector in three-dimensional space in real time to generate the vibration signal data, attitude data and acceleration data.

8. The method for measuring the operating speed and mileage of the pipeline detector according to claim 1, characterized in that, The real-time operating speed and mileage of the internal detector are calculated using a data fusion algorithm based on multiple instantaneous velocities and the attitude and acceleration data collected by the inertial measurement unit, including: Using multiple instantaneous velocities as observations, the velocity obtained by integrating the acceleration data from the inertial measurement unit is corrected; The attitude data and acceleration data are integrated using the corrected velocity to calculate the real-time motion trajectory of the internal detector, and the mileage is obtained by summing the motion trajectory.

9. The method for measuring the operating speed and mileage of the pipeline detector according to claim 1, characterized in that, The detector is a pipeline detector or pipeline pig equipped with a single row of probes.

10. A system for measuring the operating speed and mileage of an in-pipe detector, characterized in that, The method for measuring the operating speed and mileage of the in-pipe detector according to any one of claims 1-9 includes: The data processing module is used to acquire vibration signal data collected by the inertial measurement unit during the operation of the internal detector inside the pipeline; The signal recognition module identifies and extracts multiple vibration signal pairs generated by the internal detector when it passes through the pipe weld from the vibration signal data. Each vibration signal pair includes a first vibration signal generated by the front part of the internal detector passing through the current weld and a second vibration signal generated by the rear part of the internal detector passing through the current weld. The time calculation module is used to calculate the time difference between the first vibration signal and the second vibration signal for each vibration signal pair; The weld seam instantaneous velocity calculation module is used to calculate the instantaneous velocity of the inner detector when it passes through the current weld seam based on the preset distance between the front part and the rear part of the inner detector and the time difference. The fusion calculation module is used to calculate the real-time operating speed and mileage of the internal detector based on multiple instantaneous velocities and in combination with attitude data and acceleration data collected by the inertial measurement unit through a data fusion algorithm.

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