Pipeline magnetic flux leakage data acquisition method and device, detector and storage medium

Abstract

The application discloses a kind of pipeline magnetic flux leakage internal detection data acquisition methods, including real-time monitoring acceleration data and angular velocity data collected in inertial sensor of magnetic flux leakage detector;According to acceleration data and angular velocity data, whether magnetic flux leakage detector is in stationary state is judged;If magnetic flux leakage detector is in stationary state, then the probe in magnetic flux leakage detector is controlled to be in hibernation state of not collecting magnetic flux leakage data, until magnetic flux leakage detector changes from stationary state to moving state;If magnetic flux leakage detector is in moving state, then the probe continues to carry out the collection of the magnetic flux leakage data of pipeline.This application avoids the repeated collection of useless data and the occupation of useless data to storage space, is favorable for reducing energy and the meaningless loss of storage space, for improving the frequency of magnetic flux leakage detector to collect magnetic flux leakage data provides support.In this application, a kind of pipeline magnetic flux leakage internal detection data acquisition device, equipment and computer readable storage medium are also provided, with the beneficial effects described above.

Description

Technical Field

[0001] This invention relates to the field of magnetic flux leakage detection technology, and in particular to a data acquisition method, device, magnetic flux leakage detector, and computer-readable storage medium for internal detection of magnetic flux leakage in pipelines. Background Technology

[0002] Pipeline magnetic flux leakage (MFL) testing is a technique that uses a MFL detector to detect flaws in metal pipelines transporting fluids such as oil and gas. In actual testing, the MFL detector is placed inside the pipeline and moves with the fluid flow, sequentially traversing various locations along the pipeline to collect MFL data at each point. Finally, the collected MFL data is analyzed by a host computer to determine whether cracks or other damage exist at different locations in the pipeline.

[0003] During the process of collecting magnetic flux leakage data in a pipeline using a magnetic flux leakage detector, the higher the frequency of data collection, the more accurate the subsequent pipeline damage analysis based on the data. However, the magnetic flux leakage data collected by the detector needs to be temporarily stored in its built-in memory. The higher the sampling frequency of the data, the larger the amount of data collected, and the greater the energy consumed in data collection, storage, and maintenance. If the magnetic flux leakage data of the pipeline has not been completely detected, that is, if the detector has not traversed the entire pipeline and the detector's energy is exhausted or its storage space is insufficient, the magnetic flux leakage detection of the pipeline will fail. Summary of the Invention

[0004] The purpose of this invention is to provide a data acquisition method, device, magnetic flux leakage detector, and computer-readable storage medium for pipeline magnetic flux leakage detection, which reduces the energy consumption of magnetic flux leakage data acquisition and reduces unnecessary waste of storage space to a certain extent.

[0005] To solve the above-mentioned technical problems, the present invention provides a data acquisition method for pipeline magnetic flux leakage detection, comprising:

[0006] Real-time monitoring of acceleration and angular velocity data collected by the inertial sensor in the magnetic flux leakage detector;

[0007] Based on the acceleration data and the angular velocity data, determine whether the magnetic flux leakage detector is in a stationary state;

[0008] If the magnetic flux leakage detector is in a stationary state, the probe in the magnetic flux leakage detector is controlled to be in a dormant state that does not collect magnetic flux leakage data until the magnetic flux leakage detector changes from a stationary state to a moving state;

[0009] If the magnetic flux leakage detector is in a moving state, the probe will continue to collect magnetic flux leakage data of the pipeline.

[0010] Optionally, determining whether the magnetic flux leakage detector is stationary based on the acceleration data and the angular velocity data includes:

[0011] Determine whether there are any instances where the acceleration data is not greater than an acceleration threshold and / or the angular velocity data is not greater than an angular velocity threshold within a preset time period;

[0012] If present, the magnetic flux leakage detector is in a static state.

[0013] Optionally, determining whether the magnetic flux leakage detector is stationary based on the acceleration data and the angular velocity data includes:

[0014] Determine whether there are any instances where the acceleration data is not greater than an acceleration threshold and / or the angular velocity data is not greater than an angular velocity threshold within a preset time period;

[0015] If it exists, then determine whether the magnetic flux leakage data collected by the probe within the preset time period is all within the preset threshold range. If so, the magnetic flux leakage detector is in a static state.

[0016] Optionally, determining whether the magnetic flux leakage detector is stationary based on the acceleration data and the angular velocity data includes:

[0017] Determine whether there exists a situation where the change in acceleration data collected in two consecutive time periods is not greater than a first preset change and / or the change in angular velocity data collected in two consecutive time periods is not greater than a second preset change.

[0018] If not, then determine whether the magnetic flux leakage data collected by each sampling channel of each probe within the preset time period are all within the preset threshold range.

[0019] If the number of sampling channels for the corresponding magnetic leakage data within the preset threshold range is not greater than a preset number, the magnetic leakage detector is in a moving state, and the sampling channel for the corresponding magnetic leakage data within the preset threshold range is a fault sampling channel, and the fault sampling channel is closed.

[0020] Optionally, it also includes:

[0021] The magnetic flux leakage data acquired when the magnetic flux leakage detector is in a moving state is filtered and denoised using a wavelet filtering algorithm to obtain filtered magnetic flux leakage data.

[0022] Optionally, it also includes:

[0023] The magnetic flux leakage data is compressed using the miniLZO algorithm or the Quicklz algorithm, and the compressed magnetic flux leakage data is then stored.

[0024] A data acquisition device for pipeline magnetic flux leakage detection includes:

[0025] The data monitoring module is used to monitor the acceleration and angular velocity data collected by the inertial sensor in the magnetic flux leakage detector in real time.

[0026] The state determination module is used to determine whether the magnetic flux leakage detector is in a stationary state based on the acceleration data and the angular velocity data.

[0027] The first processing module is used to control the probe in the magnetic flux leakage detector to be in a dormant state where it does not collect magnetic flux leakage data if the magnetic flux leakage detector is in a stationary state, until the magnetic flux leakage detector changes from a stationary state to a moving state.

[0028] The second processing module is used to keep the probe continuously collecting magnetic flux leakage data of the pipeline if the magnetic flux leakage detector is in a moving state.

[0029] A magnetic flux leakage detector includes: multiple probes for acquiring magnetic flux leakage data of a pipeline, an inertial sensor for acquiring acceleration data and angular velocity data, a microprocessor, and a memory for storing the magnetic flux leakage data;

[0030] The microprocessor is used to execute the steps of the data acquisition method for internal detection of magnetic flux leakage in pipelines as described in any one of the claims, based on the acceleration data and the angular velocity data.

[0031] Optionally, each probe includes a microprocessor and a memory, and each microprocessor implements the data acquisition steps of the pipeline magnetic flux leakage detection method according to the acceleration data and angular velocity data detected by the inertial sensor in its respective probe.

[0032] A computer-readable storage medium storing a computer program that is executed by a processor to implement the steps of the data acquisition method for internal detection of magnetic flux leakage in pipelines as described in any of the preceding claims.

[0033] The present invention provides a data acquisition method for pipeline magnetic flux leakage detection, comprising real-time monitoring of acceleration and angular velocity data collected by an inertial sensor in a magnetic flux leakage detector; determining whether the magnetic flux leakage detector is stationary based on the acceleration and angular velocity data; if the magnetic flux leakage detector is stationary, controlling the probe in the magnetic flux leakage detector to be in a dormant state without collecting magnetic flux leakage data until the magnetic flux leakage detector changes from a stationary state to a moving state; if the magnetic flux leakage detector is moving, keeping the probe continuously collecting magnetic flux leakage data of the pipeline.

[0034] This application considers that during the movement of fluid within a pipeline, the magnetic flux leakage detector (MFL detector) may occasionally experience short-term blockages. During this process, the MFL detector probe does not stop collecting MFL data, resulting in repetitive MFL data collected at the same location, which is not of substantial use for subsequent pipeline damage detection. Therefore, this application fully utilizes the acceleration and angular velocity data detected by the inertial sensor inherent in the MFL detector during pipeline MFL detection. Based on this acceleration and angular velocity data, the motion state of the MFL detector is monitored and judged. Once the MFL detector is determined to be stationary, the probe is controlled to enter a sleep state, during which the probe does not collect MFL data. Data collection resumes only when the MFL detector returns to motion. This avoids, to some extent, the repeated collection of useless data and the occupation of storage space by useless data, reducing the meaningless energy and storage space loss during actual MFL data collection and supporting the increase of the frequency of MFL data collection.

[0035] This application also provides a data acquisition device, equipment, and computer-readable storage medium for pipeline magnetic flux leakage detection, which have the aforementioned beneficial effects. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A schematic flowchart illustrating the data acquisition method for pipeline magnetic flux leakage detection provided in this application embodiment;

[0038] Figure 2 This is a structural block diagram of a data acquisition device for pipeline magnetic flux leakage detection provided in an embodiment of the present invention. Detailed Implementation

[0039] Magnetic flux leakage (MFL) detectors typically consist of multiple probes arranged in a ring. When placed in a pipe, each probe collects MFL data from different directions around the perimeter of the pipe. To better detect MFL at various locations within the pipe, the pipe's dimensions are usually designed to be slightly smaller than the pipe's inner diameter. This inevitably leads to MFL detectors getting stuck at bends in the pipe as the fluid flows through it. The impact of the fluid on the stuck detector allows it to flow back in. However, during this brief period of sticking, the MFL detector repeatedly detects and stores MFL data at the same location in the pipe. Clearly, this repeated MFL data does not contribute to subsequent pipe damage analysis and instead consumes energy from the probes and storage space.

[0040] Therefore, this application provides a technical solution that can reduce the unnecessary power consumption and storage space occupation of the magnetic flux leakage detector to a certain extent, thereby supporting the improvement of the sampling frequency of the magnetic flux leakage detector.

[0041] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, Figure 1 This is a flowchart illustrating the data acquisition method for pipeline magnetic flux leakage detection provided in this application embodiment. The data acquisition method for pipeline magnetic flux leakage detection may include:

[0043] S11: Real-time monitoring of acceleration and angular velocity data collected by the inertial sensor in the magnetic flux leakage detector.

[0044] It should be noted that the inertial sensor is a built-in sensor in the magnetic flux leakage detector, and the acceleration and angular velocity data it collects are important information for subsequent analysis of the location of storage damage in the pipeline.

[0045] Obviously, as the fluid moves within the pipe, the acceleration and angular velocity data in the magnetic flux leakage detector also change accordingly.

[0046] S12: Based on the acceleration and angular velocity data, determine whether the magnetic flux leakage detector is in a stationary state. If yes, proceed to S13; otherwise, proceed to S14.

[0047] As mentioned earlier, as the magnetic flux leakage detector moves inside the pipe, its acceleration and angular velocity data also change accordingly. Therefore, the acceleration and angular velocity data can be used as the basis for determining whether the magnetic flux leakage detector is in a moving state or a stationary state.

[0048] Under normal circumstances, when a magnetic flux leakage detector is in motion, not only does its position change with the movement of the pipe, but it also exhibits a spinning or rolling motion during the movement. Obviously, if the magnetic flux leakage detector is stuck, it cannot spin or roll, which leads to a significant difference in the magnitude of the acceleration and angular velocity data collected by the inertial sensor in stationary and moving states.

[0049] Therefore, determining whether the magnetic flux leakage detector is in a stationary state can include:

[0050] Determine whether there are cases where acceleration data is not greater than an acceleration threshold and / or angular velocity data is not greater than an angular velocity threshold within a preset time period;

[0051] If present, the magnetic flux leakage detector is in a static state.

[0052] Obviously, the aforementioned preset time period refers to the time period between the current moment and a certain duration before the current moment. The duration of this preset time period can be set based on the moving speed of the magnetic flux leakage detector. The greater the moving speed, the shorter the preset time period, and vice versa. The moving speed of the magnetic flux leakage detector is obviously positively correlated with the fluid flow rate, or even equal to the fluid flow rate. Therefore, the preset time period can be appropriately set based on the fluid flow rate.

[0053] The preset acceleration threshold and preset angular velocity threshold mentioned above are both close to 0. Therefore, if either the acceleration data or the angular velocity data fluctuates within a range close to 0 within a preset time period, it can be determined that the magnetic flux leakage detector is in a stationary state.

[0054] S13: If the magnetic flux leakage detector is in a stationary state, the probe in the magnetic flux leakage detector is controlled to be in a dormant state that does not collect magnetic flux leakage data until the magnetic flux leakage detector changes from a stationary state to a moving state.

[0055] S14: If the magnetic flux leakage detector is in a moving state, the probe will continue to collect magnetic flux leakage data of the pipeline.

[0056] When the magnetic flux leakage detector is in a static state, it can be determined that the detector may be stuck or blocked. In this case, it is meaningless to repeatedly collect the magnetic flux leakage data. Therefore, the probe of the magnetic flux leakage detector can be set to a sleep state, that is, the probe will no longer collect magnetic flux leakage data. This can reduce the energy loss caused by the probe collecting meaningless magnetic flux leakage data to a certain extent, and avoid the problem of meaningless magnetic flux leakage data occupying the memory space in the magnetic flux leakage detector.

[0057] Understandably, when each probe is in a dormant state, the inertial sensor still needs to continuously detect acceleration and angular velocity data to determine whether the magnetic flux leakage detector has changed from a stationary state to a moving state, thus avoiding the problem of missed detection of magnetic flux leakage data.

[0058] In summary, this application, when using a magnetic flux leakage detector (MFL detector) to detect MFL data in pipelines, fully considers the potential for jamming and pressure buildup during MFL detector movement within the pipeline. This can lead to the MFL detector repeatedly collecting meaningless MFL data from the same location within a short period. The application fully utilizes the acceleration and angular velocity data obtained from the inertial sensors within the MFL detector to detect and judge its motion state. When the MFL detector is stationary, the probe enters a dormant state; MFL data detection is only permitted when the MFL detector is in motion. This approach avoids energy consumption caused by monitoring useless MFL data and prevents useless MFL data from occupying storage space. With reduced energy consumption and storage space usage, the MFL detector can obviously have more energy and storage space to support higher-frequency MFL data acquisition by the probe, thereby improving the accuracy of subsequent pipeline damage analysis based on MFL data.

[0059] Based on any of the above embodiments, the reason why the collected magnetic flux leakage data is meaningless when the magnetic flux leakage detector is in a blocked or pressure-stressed state is obviously because the collected magnetic flux leakage data is repeated and unchanging at the same location in the pipeline. That is to say, when the magnetic flux leakage detector is in a blocked or pressure-stressed state, the magnetic flux leakage data will still show specific characteristics to a certain extent. Therefore, in another optional embodiment of this application, in order to further improve the accuracy of the movement state of the magnetic flux leakage detector, it may also include:

[0060] Determine whether there are cases where acceleration data is not greater than an acceleration threshold and / or angular velocity data is not greater than an angular velocity threshold within a preset time period;

[0061] If it exists, it is determined that the magnetic flux leakage data collected by the probe within the preset time period are all within the preset threshold range. If so, the magnetic flux leakage detector is in a static state.

[0062] Theoretically, if a magnetic flux leakage detector is stuck or pressurized, its corresponding acceleration, angular velocity, and magnetic flux leakage data should all exhibit specific characteristics within the same time period. Therefore, to further improve the accuracy of judging the motion state of the magnetic flux leakage detector, the acceleration, angular velocity, and magnetic flux leakage data can be combined to jointly determine whether the detector is stationary.

[0063] The acceleration and angular velocity data collected by the inertial sensor built into the magnetic flux leakage detector can reflect the motion state of the magnetic flux leakage detector to a certain extent. In this embodiment, based on the fact that either the acceleration data or the angular velocity data is close to 0 within a preset time period, the variation range of the magnetic flux leakage data collected by the magnetic flux leakage detector within the preset time period can be further judged. If the magnitude of the magnetic flux leakage data within the preset threshold range is within the preset threshold range, it can be clearly determined that the magnetic flux leakage detector is in a moving state.

[0064] If, within a preset time period, the acceleration data of the magnetic flux leakage detector is greater than the acceleration threshold, the angular velocity data is greater than the angular velocity threshold, and the fluctuation range of the magnetic flux leakage data also exceeds the preset threshold range, it can be clearly determined that the magnetic flux leakage detector should be in a moving state.

[0065] However, in practical applications, there may be situations where the acceleration and angular velocity data are not greater than the corresponding thresholds, but the fluctuation range of the magnetic flux leakage data exceeds the preset threshold; or the acceleration or angular velocity data are greater than the corresponding thresholds, but the fluctuation range of the magnetic flux leakage data exceeds the preset threshold. In order to avoid the problem of missing data acquisition, the probe can be left in sleep mode.

[0066] In this embodiment, the motion state of the magnetic flux leakage detector is monitored by combining acceleration data, angular velocity data, and magnetic flux leakage data, thereby improving the accuracy of the detector's state detection to a certain extent. It is understood that this detection method should be used when the probe is not in sleep mode to determine the motion state of the magnetic flux leakage detector. If the probe is in sleep mode, the motion state of the magnetic flux leakage detector should still be determined based on data collected by the inertial sensor.

[0067] Of course, a magnetic flux leakage detector can generally contain multiple probes, and each probe contains multiple sampling channels (i.e., multiple magnetic flux leakage sensors).

[0068] Therefore, optionally, when it is determined that the magnetic flux leakage detector is in a stationary state, one sampling channel of one of the probes can be selected to continuously collect magnetic flux leakage data, while all other sampling channels are in a dormant state. Thus, even when most probes of the magnetic flux leakage detector are in a dormant state, the motion state of the magnetic flux leakage detector can still be continuously judged by combining magnetic flux leakage data, acceleration data, and angular velocity data. Of course, the magnetic flux leakage data collected by the non-dormant sampling channels during this process does not need to be stored in the memory, but only temporarily cached. When each probe resumes normal magnetic flux leakage data collection in the non-dormant state, the magnetic flux leakage data of this stage can be deleted.

[0069] Furthermore, since the magnetic flux leakage detector contains multiple probes, each with multiple sampling channels, it is clear that multiple sets of magnetic flux leakage data can be collected within the same preset time period. When judging the motion state of the magnetic flux leakage detector by combining the acceleration and angular velocity data collected by the inertial sensor with the magnetic flux leakage data collected by the probes, the magnetic flux leakage data collected by one or more sampling channels of the probes can be randomly selected to judge the motion state of the magnetic flux leakage detector.

[0070] For example, based on comparing acceleration data and angular velocity data with corresponding preset thresholds, the magnetic flux leakage data collected by selecting one sampling channel in each probe can also be compared with the corresponding preset thresholds. If the magnetic flux leakage data collected by each sampling channel is not greater than the preset threshold, or if the proportion of sampling channels whose collected magnetic flux leakage data is not greater than the preset threshold is combined, the motion state of the magnetic flux leakage detector can be judged.

[0071] For example, if the acceleration data is less than the acceleration threshold and / or the angular velocity data is less than the angular velocity threshold, it is determined whether the proportion of the magnetic flux leakage data collected by each sampling channel within the preset threshold range reaches a first preset proportion. If so, it can be determined that the magnetic flux leakage detector is in a stationary state. If the acceleration data is greater than the acceleration threshold and / or the angular velocity data is greater than the angular velocity threshold, it is determined whether the proportion of the magnetic flux leakage data collected by each sampling channel within the preset threshold range reaches a preset proportion. If so, it can be determined that the magnetic flux leakage detector is in a moving state.

[0072] Of course, in practical applications, when judging the motion state of a magnetic flux leakage detector based on acceleration data, angular velocity data, and magnetic flux leakage data, reasonable judgment logic can also be set according to the principles of big data statistics. This will not be elaborated upon in detail here.

[0073] Optionally, this embodiment further considers that when multiple sampling channels of multiple probes are performing magnetic flux leakage data detection, there may be a situation where a sampling channel of one of the probes malfunctions. Therefore, in another optional embodiment of this invention, it may further include:

[0074] Determine whether there exists a situation where the change in acceleration data collected in two consecutive time periods is not greater than a first preset change and / or the change in angular velocity data collected in two consecutive time periods is not greater than a second preset change.

[0075] If not, determine whether the leakage magnetic data collected by each sampling channel of each probe within the preset time period are all within the preset threshold range.

[0076] If the number of sampling channels for the corresponding magnetic flux leakage data within the preset threshold range is not greater than the preset number, the magnetic flux leakage detector is in a moving state, and the sampling channel for the corresponding magnetic flux leakage data within the preset threshold range is a fault sampling channel, and the fault sampling channel is closed.

[0077] In this embodiment, the leakage magnetic data collected from each sampling channel is further combined to determine the sampling channel fault. For example, if the change in leakage magnetic data collected by one sampling channel of the same probe is not greater than a preset threshold within a preset time period, while the change in leakage magnetic data collected by the other sampling channels is greater than the preset threshold within the same preset time period, it can be determined that the sampling channel whose change in leakage magnetic data is not greater than the preset threshold within the preset time period is faulty. In this case, the faulty sampling channel can be directly shut down to stop it from working. On the one hand, this avoids the leakage magnetic data collected by the sampling channel from interfering with the subsequent analysis of pipeline damage, and on the other hand, it also avoids the energy consumption and storage space occupation caused by the leakage magnetic data collected by the faulty sampling channel.

[0078] Based on the experience of the staff, it can also be determined that the leakage magnetic field data detected under certain other conditions is useless data, and can be used as the basis for setting the probe to sleep. These will not be listed one by one.

[0079] Based on any of the above embodiments, under normal operation of the magnetic flux leakage detector, the collected magnetic flux leakage data is stored in file format at a preset size. When generating the storage file, an embedded application compression algorithm can be further used to compress the magnetic flux leakage data to form the storage file, thereby reducing the file size and lowering the storage pressure on the memory. Specifically, this may include:

[0080] The magnetic flux leakage data is compressed using the miniLZO or Quicklz algorithm, and the compressed magnetic flux leakage data is then stored.

[0081] miniLZO is an open-source lossless compression C library that features fast compression and decompression with small memory footprint. During the compression process, the LZO compression algorithm uses a specific algorithm to determine whether the current string has appeared in the history strings (strings that have already been processed). If it has appeared (defined as a repeating character), the length of the repeating string and the pointer distance are calculated, and the (repeat length, pointer distance) is used to replace the current repeating string.

[0082] The Quicklz algorithm continuously reads 3 bytes during compression and obtains a hash value based on these 3 bytes. The offset can be found based on this hash value, which is the position where the hash value last appeared. The cache can be used to determine whether the 3 bytes of the current occurrence are the same as those of the most recent occurrence of the same hash value (the hash may be the same but the actual value may be different).

[0083] Optionally, in addition to compressing the magnetic flux leakage data, this application can also filter the acquired magnetic flux leakage data, specifically including:

[0084] The magnetic flux leakage data acquired when the magnetic flux leakage detector is in a moving state is filtered and denoised using a wavelet filtering algorithm to obtain filtered magnetic flux leakage data.

[0085] The high-frequency acquisition and large amount of magnetic flux leakage data storage of ultra-high-definition magnetic flux leakage detection probes will inevitably generate more noise interference. Therefore, the data can be used to filter out or repair a large amount of useless noise.

[0086] To meet the operational requirements of pipeline inspection for defect detection, wavelet filtering algorithm has the characteristics of excellently characterizing the non-stationary features of signals, such as edges, peaks, and breakpoints, as well as low-pass filtering function. Therefore, wavelet filtering was selected as the filtering algorithm for leakage magnetic field data.

[0087] The wavelet filtering algorithm is generally divided into three stages:

[0088] 1) Filter Construction Stage: Determine the DB coefficients and decomposition coefficients (number of times). The noise removal capability, i.e., the smoothness of the signal, mainly depends on a reasonable DB coefficient. The DB coefficient selection range is [2, 10]. If the coefficient is too large, effective signal data will be filtered out; if the coefficient is too small, noise removal will be incomplete. The decomposition coefficients mainly determine the amount of signal data compression. The selection of the number of decompositions is the same as that of the DB coefficients. The larger the number of decompositions, the smoother the signal will be, but details will be lost. The number of decompositions makes a larger adjustment to the signal. The number of decompositions makes a large-scale adjustment to the signal, while the DB coefficients make a fine adjustment. There are no requirements for the order of adjustment; the goal is to achieve a clear signal characteristic, smoothness, less noise, and less loss of details.

[0089] It should be noted that the process of filtering magnetic flux leakage data by the wavelet filtering algorithm can be completed by the processor in the magnetic flux leakage detector, or the processor can upload the unfiltered magnetic flux leakage data to the host computer for filtering.

[0090] When the host computer implements the wavelet filtering algorithm to filter the leakage magnetic data, the DB coefficient is selected by manually selecting a feature (circumferential weld or defect) after the data is visualized by the software. It is best to select a feature of small defects to prevent some defects from being filtered out after the DB coefficient is adjusted. After achieving a better signal effect, further upward adjustment will not change the effect much.

[0091] 2) Wavelet decomposition stage: This stage uses a filter to split the signal data into two parts, selecting high-frequency signals and low-frequency signals. The high-frequency signals are noise signals + effective signals, and the low-frequency signals are effective signals. Then, the high-frequency signals are decomposed again. The total number of decompositions is the decomposition coefficient in the previous step. After this stage, the original data volume will be 2n (n is the number of decompositions). For example, if the original data volume is 1600 and the number of decompositions is 4, the final low-frequency and high-frequency signal data volume will be 1600 / 24 ​​= 100. This is lossy compression.

[0092] ③ Wavelet reconstruction stage:

[0093] Based on the high and low frequency signal data generated in each decomposition stage, the data is reconstructed, that is, the low frequency signal is used to fit and repair the high frequency signal, and finally the signal data is reconstructed back to the original data volume.

[0094] The signal effects produced by wavelet decomposition and reconstruction stages are not significantly different. If only wavelet decomposition is performed, it will result in lossy compression. After reconstruction, the signal will be repaired while the amount of data remains unchanged.

[0095] The following describes the data acquisition device for pipeline magnetic flux leakage detection provided in the embodiments of the present invention. The data acquisition device for pipeline magnetic flux leakage detection described below can be referred to in correspondence with the data acquisition method for pipeline magnetic flux leakage detection described above.

[0096] Figure 2 This is a structural block diagram of the data acquisition device for pipeline magnetic flux leakage detection provided in an embodiment of the present invention, with reference to... Figure 2 The data acquisition device for pipeline magnetic flux leakage detection may include:

[0097] The data monitoring module 100 is used to monitor the acceleration and angular velocity data collected by the inertial sensor in the magnetic flux leakage detector in real time.

[0098] The state determination module 200 is used to determine whether the magnetic flux leakage detector is in a stationary state based on the acceleration data and the angular velocity data.

[0099] The first processing module 300 is used to control the probe in the magnetic flux leakage detector to be in a dormant state where it does not collect magnetic flux leakage data if the magnetic flux leakage detector is in a stationary state, until the magnetic flux leakage detector changes from a stationary state to a moving state.

[0100] The second processing module 400 is used to keep the probe continuously collecting magnetic flux leakage data of the pipeline if the magnetic flux leakage detector is in a moving state.

[0101] In an optional embodiment of this application, the state judgment module 200 is specifically used to determine whether there are any cases where the acceleration data is not greater than the acceleration threshold and / or the angular velocity data is not greater than the angular velocity threshold within a preset time period; if so, the magnetic flux leakage detector is in a stationary state.

[0102] In an optional embodiment of this application, the state judgment module 200 is specifically used to determine whether there are acceleration data that are all not greater than an acceleration threshold and / or angular velocity data that are all not greater than an angular velocity threshold within a preset time period; if so, it is determined whether the magnetic flux leakage data collected by the probe within the preset time period are all within the preset threshold range; if so, the magnetic flux leakage detector is in a stationary state.

[0103] In an optional embodiment of this application, the state judgment module 200 is specifically used to determine whether there exists a situation where the change in acceleration data collected in two consecutive time periods is not greater than a first preset change and / or the change in angular velocity data collected in two consecutive time periods is not greater than a second preset change; if not, it is determined whether the magnetic flux leakage data collected by each sampling channel of each probe in the preset time period is within a preset threshold range; if the number of sampling channels for the corresponding magnetic flux leakage data within the preset threshold range is not greater than a preset number, then the magnetic flux leakage detector is in a moving state, and the sampling channel for the corresponding magnetic flux leakage data within the preset threshold range is a faulty sampling channel, and the faulty sampling channel is closed.

[0104] In an optional embodiment of this application, a filtering module is further included, which is used to perform filtering and noise reduction processing on the magnetic flux leakage data acquired when the magnetic flux leakage detector is in a moving state using a wavelet filtering algorithm to obtain filtered magnetic flux leakage data.

[0105] In an optional embodiment of this application, a compression module is further included, which is used to compress the magnetic flux leakage data using the miniLZO algorithm or the Quicklz algorithm, and to store the compressed magnetic flux leakage data.

[0106] The data acquisition device for pipeline magnetic flux leakage detection in this embodiment is used to implement the aforementioned data acquisition method for pipeline magnetic flux leakage detection. Therefore, the specific implementation method of the data acquisition device for pipeline magnetic flux leakage detection can be found in the embodiment section of the data acquisition method for pipeline magnetic flux leakage detection above, and will not be repeated here.

[0107] This application also provides an embodiment of a magnetic flux leakage detector, which may include: a plurality of probes for collecting magnetic flux leakage data of a pipeline, an inertial sensor for collecting acceleration data and angular velocity data, a microprocessor, and a memory for storing the magnetic flux leakage data;

[0108] The microprocessor is used to execute the steps of the data acquisition method for internal detection of magnetic flux leakage in pipelines as described above, based on the acceleration data and the angular velocity data.

[0109] Optionally, each probe includes a microprocessor and a memory. Each microprocessor implements the data acquisition steps of the pipeline leakage magnetic flux detection method based on the acceleration and angular velocity data detected by the inertial sensor in its respective probe.

[0110] In this embodiment, to avoid the situation where the magnetic flux leakage data collected by each probe in the magnetic flux leakage sensor is centrally managed by a single processor, which would place a heavy computational burden on the processor, affect the data storage speed of the magnetic flux leakage data, and thus limit the frequency at which each probe collects magnetic flux leakage data, and also to some extent avoid the problem of the entire magnetic flux leakage detector becoming unusable if the processor fails, each probe is equipped with an independent microprocessor. Each microprocessor runs independently and implements the program for the data acquisition method of the above-mentioned pipeline magnetic flux leakage detection. This helps to ensure the accuracy of subsequent pipeline damage analysis based on the acquired magnetic flux leakage data.

[0111] This application also provides an embodiment of a computer-readable storage medium storing a computer program that is executed by a processor to implement the steps of the data acquisition method for internal detection of pipeline magnetic flux leakage as described in any of the above claims.

[0112] The computer-readable storage medium may include random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0113] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0114] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A data acquisition method for internal detection of magnetic flux leakage in pipelines, characterized in that, include: Real-time monitoring of acceleration and angular velocity data collected by the inertial sensor in the magnetic flux leakage detector; Based on the acceleration data and the angular velocity data, determine whether the magnetic flux leakage detector is in a stationary state; If the magnetic flux leakage detector is in a stationary state, the probe in the magnetic flux leakage detector is controlled to be in a dormant state that does not collect magnetic flux leakage data until the magnetic flux leakage detector changes from a stationary state to a moving state; If the magnetic flux leakage detector is in a moving state, the probe will continue to collect magnetic flux leakage data of the pipeline. Determining whether the magnetic flux leakage detector is stationary based on the acceleration data and the angular velocity data includes: Determine whether there are any instances where the acceleration data is not greater than an acceleration threshold and / or the angular velocity data is not greater than an angular velocity threshold within a preset time period; If it exists, then determine whether the magnetic flux leakage data collected by the probe within the preset time period is all within the preset threshold range. If the proportion of magnetic flux leakage data collected by each sampling channel of the probe in the magnetic flux leakage detector within the preset threshold range reaches the first preset proportion, then the magnetic flux leakage detector is in a static state. Determine whether there exists a situation where the change in acceleration data collected in two consecutive time periods is not greater than a first preset change and / or the change in angular velocity data collected in two consecutive time periods is not greater than a second preset change. If not, then determine whether the leakage magnetic data collected by each sampling channel of each probe within the preset time period are all within the preset threshold range. If the number of sampling channels for the corresponding magnetic leakage data within the preset threshold range is not greater than a preset number, the magnetic leakage detector is in a moving state, and the sampling channel for the corresponding magnetic leakage data within the preset threshold range is a fault sampling channel, and the fault sampling channel is closed.

2. The data acquisition method for pipeline magnetic flux leakage detection as described in claim 1, characterized in that, Also includes: The magnetic flux leakage data acquired when the magnetic flux leakage detector is in a moving state is filtered and denoised using a wavelet filtering algorithm to obtain filtered magnetic flux leakage data.

3. The data acquisition method for pipeline magnetic flux leakage detection as described in claim 1, characterized in that, Also includes: The magnetic flux leakage data is compressed using the miniLZO algorithm or the Quicklz algorithm, and the compressed magnetic flux leakage data is then stored.

4. A data acquisition device for internal detection of magnetic flux leakage in pipelines, characterized in that, include: The data monitoring module is used to monitor the acceleration and angular velocity data collected by the inertial sensor in the magnetic flux leakage detector in real time. The state determination module is used to determine whether the magnetic flux leakage detector is in a stationary state based on the acceleration data and the angular velocity data. The first processing module is used to control the probe in the magnetic flux leakage detector to be in a dormant state where it does not collect magnetic flux leakage data if the magnetic flux leakage detector is in a stationary state, until the magnetic flux leakage detector changes from a stationary state to a moving state. The second processing module is used to keep the probe continuously collecting magnetic flux leakage data of the pipeline if the magnetic flux leakage detector is in a moving state. The state judgment module is specifically used to determine whether there are acceleration data that are all not greater than an acceleration threshold and / or angular velocity data that are all not greater than an angular velocity threshold within a preset time period; if there are, it is determined whether the magnetic flux leakage data collected by the probe within the preset time period are all within a preset threshold range; if the proportion of magnetic flux leakage data collected by each sampling channel of the probe in the magnetic flux leakage detector within the preset threshold range reaches a first preset proportion, then the magnetic flux leakage detector is in a stationary state. The state judgment module is specifically used to determine whether there exists a situation where the change in acceleration data collected in two consecutive time periods is not greater than a first preset change and / or the change in angular velocity data collected in two consecutive time periods is not greater than a second preset change. If not, it determines whether the magnetic flux leakage data collected by each sampling channel of each probe in the preset time period is within a preset threshold range. If the number of sampling channels for the corresponding magnetic flux leakage data within the preset threshold range is not greater than a preset number, the magnetic flux leakage detector is in a moving state, and the sampling channel for the corresponding magnetic flux leakage data within the preset threshold range is a faulty sampling channel, and the faulty sampling channel is closed.

5. A magnetic flux leakage detector, characterized in that, include: Multiple probes for collecting magnetic flux leakage data from the pipeline, an inertial sensor for collecting acceleration and angular velocity data, a microprocessor, and a memory for storing the magnetic flux leakage data; The microprocessor is used to execute the steps of the data acquisition method for internal detection of magnetic flux leakage in pipelines as described in any one of claims 1 to 3, based on the acceleration data and the angular velocity data.

6. The magnetic flux leakage detector as described in claim 5, characterized in that, Each probe includes a microprocessor and a memory. Each microprocessor implements the data acquisition steps of the pipeline magnetic flux leakage detection method based on the acceleration and angular velocity data detected by the inertial sensor in its respective probe.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the steps of the data acquisition method for internal detection of magnetic flux leakage in pipelines as described in any one of claims 1 to 3.

Images