A long liquid pipeline online nondestructive testing and state evaluation system and method

Abstract

This invention provides an online non-destructive testing and condition assessment system and method for long-distance liquid pipelines, relating to the field of non-destructive testing and condition assessment technology for long-distance pipelines. The system includes a testing device and a data processing and assessment device. The testing device comprises a housing, a central connecting shaft, and a group of functional modules coaxially fixed thereto. The module group includes a front bumper, a battery module, an electronics and data processing module, and a testing module. The testing module contains a sensor carrier assembly with multiple ultrasonic transducer mounting holes. The data processing and assessment device is used for signal processing, defect identification, and condition assessment. The integrated coaxial module design ensures stable testing posture, the multi-directional transducer arrangement achieves omnidirectional defect coverage, and the spatial coordinate indexing and axial continuity discrimination mechanism convert the signal into precisely located defect information and quantified condition parameters, significantly improving testing reliability, positioning accuracy, and maintenance decision-making efficiency.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing and condition assessment technology for long-distance pipelines, and more specifically, to an online nondestructive testing and condition assessment system and method for long-distance liquid pipelines. Background Technology

[0002] Long-distance liquid pipelines are critical infrastructure for transporting media such as crude oil and refined oil, characterized by long transport distances, complex operating environments, and long service lives. During long-term operation, pipelines are subject to various factors, including internal media corrosion, external environmental influences, foundation settlement, and construction defects. These factors can lead to structural defects in the pipe walls, such as corrosion thinning, cracks, and depressions. These defects threaten the safe operation of the pipelines and may cause serious accidents such as leaks and explosions. Therefore, regular and effective non-destructive testing and condition assessment of in-service long-distance pipelines are crucial for ensuring their safe and economical operation.

[0003] Currently, in-service inspection of long-distance liquid pipelines mainly relies on a combination of external manual inspections and localized non-destructive testing. Manual inspections primarily involve visually examining the pipeline's external anti-corrosion coating, ancillary facilities, and surrounding environment using simple instruments, making it difficult to detect defects inside the pipeline. Localized non-destructive testing typically involves sampling inspections of stress concentration or high-risk areas such as welds and elbows during planned shutdowns or maintenance periods, using methods such as ultrasonic testing (UT) and magnetic particle testing (MT). The disadvantages of this approach are obvious: the inspection range is extremely limited, failing to cover the majority of the pipeline's length; the inspection results are random and unpredictable, heavily reliant on the experience of the inspectors and the selection of sampling locations, making it impossible to systematically grasp the overall health condition of the pipeline and easily overlooking potential hazards.

[0004] To expand the scope of inspection, the industry has developed pipeline in-situ inspection technology, which utilizes intelligent detectors (PIGs) that move along with the medium inside the pipeline to achieve full-line scanning. Existing pipeline in-situ detectors are mostly based on magnetic flux leakage (MFL) or ultrasonic testing (UT) principles. However, existing technologies still have several shortcomings: First, in terms of hardware structure, the layout of various functional modules (such as power supply, data acquisition, and sensor arrays) within the detector is often dispersed or stacked. When operating in long-distance pipelines with many bends and complex flow patterns, instability and relative displacement between modules can easily lead to deviations in the direction of the detection signal and data discontinuity, affecting detection accuracy and repeatability. Second, in terms of data processing and evaluation, existing technologies often focus on the acquisition of defect signals and simple alarms, lacking a complete data processing and evaluation system with the pipeline's spatial location as the core index. Inspection data is mostly stored in time-series format, making it difficult to accurately correspond to the actual physical location of the pipeline. This results in the inability to effectively align and compare inspection data from different periods and batches under a unified spatial coordinate system, limiting the ability to predict pipeline condition trends and assess lifespan based on historical data. In addition, defect identification algorithms are mostly based on threshold judgment of single-point signals, which have poor anti-interference ability against occasional abnormal signals caused by pipeline operation noise, medium fluctuations, etc., and have a high false alarm rate, causing trouble for operation and maintenance decisions.

[0005] Existing technologies struggle to simultaneously achieve highly stable full-line data acquisition, highly reliable defect identification, and systematic pipeline condition assessment and trend analysis under the condition of uninterrupted operation of long-distance liquid pipelines. Summary of the Invention

[0006] The technical problem to be solved by the present invention is that the existing technology is difficult to achieve high stability of full-line data acquisition, high reliability of defect identification, and systematic pipeline condition assessment and trend analysis under the condition of uninterrupted operation of long-distance liquid pipelines. In order to overcome the above-mentioned defects of the existing technology, the present invention provides an online non-destructive testing and condition assessment system and method for long-distance liquid pipelines.

[0007] This invention provides an online non-destructive testing and condition assessment system for long-distance liquid pipelines, comprising: The detection device is used to continuously perform non-destructive testing on the inner wall of a pipeline as it moves with the medium inside the pipeline, and outputs a detection signal. A data processing and evaluation device is used to receive the detection signal and process the detection data, identify defects, and evaluate the pipeline condition. The detection device includes a housing, a central connecting shaft disposed within the housing, and a group of functional modules arranged coaxially along the central connecting shaft and fixedly connected to form an integrated structure. The functional module group includes a front bumper for buffer protection, a battery module for power supply, an electronic and data processing module for data acquisition and processing, and a detection module; the detection module is equipped with a sensor carrier assembly, which has multiple ultrasonic transducer mounting holes, and each ultrasonic transducer mounting hole is equipped with an ultrasonic transducer.

[0008] Compared with existing technologies, the online non-destructive testing and condition assessment system for long-distance liquid pipelines disclosed in this application has the following advantages: The testing device adopts a coaxial integrated module design with a central connecting shaft running through it, which effectively resists complex fluid and geometric disturbances inside the pipeline and ensures the stability of sensor posture and detection signals; the modular sensor carrier component allows for flexible arrangement of multi-directional ultrasonic transducers, achieving comprehensive coverage of pipeline cross-sectional defects and significantly improving the detection rate; while the dedicated data processing and assessment device, by introducing spatial coordinate indexing and axial continuity discrimination mechanisms, transforms the original signal into precisely located defect information and pipeline section status parameters, which not only greatly reduces noise false alarms but also enables quantitative assessment and trend analysis of pipeline health status, thereby improving the overall reliability, accuracy, and operation and maintenance decision-making efficiency of online testing for long-distance pipelines.

[0009] In one possible implementation, the data processing and evaluation apparatus includes: The data receiving and positioning module is used to convert the detection signal sequence arranged in time sequence into the detection data sequence arranged in the axial position of the pipeline according to the operating speed and sampling time interval of the detection device; The preprocessing and feature extraction module, connected to the data receiving and positioning module, is used to perform noise suppression, baseline correction and amplitude normalization on the detection data, and extract feature parameters including amplitude features, axial variation gradient features and abnormal energy distribution features. The defect identification module, connected to the preprocessing and feature extraction module, is used to perform segmented scanning along the pipe axis based on feature parameters, and introduces axial spatial continuity as a necessary condition for defect judgment. The status assessment module, connected to the defect identification module, is used to convert the identified defect features into status parameters at the pipeline section level, thereby enabling a quantitative assessment of the pipeline's operating status.

[0010] Compared with existing technologies, this method accurately maps time-stream signals to spatial positioning data, fundamentally ensuring the axial position accuracy of defects. Through multi-level preprocessing and multi-dimensional feature extraction, the signal is effectively purified and defect characteristics are highlighted. More importantly, the introduction of "axial spatial continuity" as a hard criterion in the defect identification process can strictly distinguish between real structural anomalies and transient noise interference, thereby significantly reducing the false alarm rate of the system. Finally, the system integrates discrete defect information into intuitive section-level state parameters, enabling quantitative characterization and trend analysis of pipeline health status, significantly improving the objectivity of detection results, the efficiency of decision support, and the level of intelligence in long-term operation and maintenance management.

[0011] Another technical solution of the present invention is to provide an evaluation method for an online non-destructive testing and condition assessment system for long-distance liquid pipelines, characterized by comprising the following steps: S1. The pipeline detection device collects detection signal sequences in a time sequence. Based on the operating speed of the detection device and the sampling time interval, it calculates the axial position of the pipeline corresponding to each detection signal, establishes the correspondence between the detection signals and the spatial position of the pipeline, and forms a detection data sequence indexed by axial position. S2. After preprocessing the detection data sequence, feature parameters are extracted, and the feature parameters are bound to the corresponding axial position of the pipe to construct a feature parameter set; S3. Perform segmented scanning of characteristic parameters along the pipeline axis, and identify the segments that meet the preset abnormal conditions and are continuously distributed in the axial direction to reach the minimum length as defect characteristic segments; S4. Based on the number of defect features, spatial distribution density, and variation range of characteristic parameters in each pipeline section, calculate the state parameters of each section and complete the pipeline condition assessment. S5. Output evaluation results including pipeline section location identifiers, status parameters, and defect characteristic information.

[0012] Compared with existing technologies, this method strictly converts time signals into spatial positioning data to ensure accurate axial location of defects; introduces "axial continuity" constraints in the identification process to effectively distinguish between real defects and random noise, greatly reducing the false alarm rate; and generates segment-level state parameters by comprehensively considering features such as defect density, distribution, and intensity, making the evaluation results objective, quantitative, and able to directly support maintenance decisions, significantly improving the reliability, interpretability, and engineering applicability of the detection results.

[0013] In one possible implementation, step S1 specifically includes the following steps: S11. The detection device operating inside the pipeline continuously detects the inner wall of the pipeline according to the preset sampling frequency and outputs a sequence of detection signals arranged in chronological order. S12. Receive the detection signal sequence, and calculate the axial position of the pipeline corresponding to each detection signal based on the operating speed of the detection device in the pipeline and the sampling time interval; for the i-th detection signal, its corresponding axial position of the pipeline... Determined according to the following relationship: in, To detect the operating speed of the device, The sampling time interval between adjacent detected signals; S13. Convert the original time-ordered detection signal sequence into a detection data sequence arranged according to the axial position of the pipeline, so that the detection data can be stored and retrieved using the spatial position of the pipeline as an index.

[0014] Compared with existing technologies, by accurately converting the raw detection signal sequence acquired over time into a spatialized data sequence indexed by the axial position of the pipeline based on a clear relationship between operating speed and sampling interval, a unified and accurate spatial coordinate benchmark is established for the entire evaluation process. This ensures that all subsequent processing (such as feature extraction, defect identification, and condition assessment) can be based on accurate pipeline location information, fundamentally solving problems such as positioning ambiguity, data alignment difficulties, and failure of multi-cycle result comparison caused by the disconnect between signal time tags and pipeline physical location. This greatly improves the accuracy of defect location, the spatial consistency of evaluation results, and the comparability of long-term monitoring data.

[0015] In one possible implementation, in step S13, if there are spatially discontinuous detection data caused by fluctuations in operating speed, changes in sampling interval, or missing local data, the detection data sequence is subjected to continuity correction processing. The missing data is compensated by interpolation or resampling to ensure that the detection data is continuously distributed in the axial direction of the pipeline.

[0016] Compared with existing technologies, the continuity correction step introduced in the preprocessing of detection data ensures the continuity and integrity of the detection data along the pipeline axis by interpolating or resampling to compensate for spatial discontinuities caused by fluctuations in operating speed, changes in sampling intervals, or missing data. This process significantly enhances the reliability of the data sequence and the stability of subsequent analysis, enabling the defect identification mechanism based on axial continuity constraints to be reliably applied under real and non-ideal engineering data conditions. This improves the adaptability of the entire evaluation system to actual complex working conditions and the credibility of the results.

[0017] In one possible implementation, step S2 specifically includes the following steps: S21. Detect the detection signal in the detection data sequence. Preprocessing is performed, including amplitude normalization, and its expression is: in, and These represent the mean and standard deviation of the detection signal within the corresponding pipeline section, respectively. S22. Extract the feature parameters from the detection data. The feature parameters include the axial change gradient feature of the detection signal, and its expression is: in, The axial position interval between adjacent pipes; S23. Bind each feature parameter to the corresponding axial position of the pipe to construct a unified set of feature parameters.

[0018] Compared with existing technologies, introducing amplitude standardization processing based on statistics (mean and standard deviation) before feature extraction can effectively eliminate signal baseline drift and amplitude inconsistency caused by differences in media, environment, or sensors in different pipeline sections, ensuring the benchmark consistency and comparability of subsequent feature analysis. At the same time, the axial gradient of the detection signal is specifically calculated as a feature parameter, which can sensitively capture the signal abrupt changes caused by the edges of defects such as corrosion and cracks, enhancing the ability to identify small or smooth defects. By binding the processed features of each dimension with precise spatial location, a high-fidelity and traceable feature parameter set is constructed, providing a high-quality and standardized data foundation for subsequent defect identification and quantitative assessment based on spatial continuity, thereby improving the overall sensitivity of defect detection and the reliability of condition assessment.

[0019] In one possible implementation, step S3 specifically includes the following steps: S31. Perform segmented scanning of characteristic parameters along the pipeline axis, divide the pipeline into several continuous analysis sections, and perform statistical analysis on the characteristic parameters in each section to identify effective defect characteristic sections. S32. Bind the confirmed defect feature section to its corresponding axial position of the pipeline, and record it using the pipeline spatial position as an index.

[0020] Compared with existing technologies, by segmenting and statistically analyzing characteristic parameters along the pipeline axis, objective and systematic identification and confirmation of defect feature sections can be achieved. Furthermore, the identified defect features are bound to the precise axial position of the pipeline and recorded using spatial position as an index. This not only ensures that each defect has clear spatial coordinates, facilitating accurate positioning and subsequent maintenance, but also constructs a structured defect database centered on the physical location of the pipeline, significantly improving the operability, traceability, and efficiency of long-term monitoring data management and analysis of the detection results.

[0021] In one possible implementation, step S31 involves statistical analysis of the characteristic parameters within each segment to identify valid defect characteristic segments, specifically including the following steps: S312, When the characteristic parameters of a certain segment Relative to the characteristic parameters of the reference section A segment is marked as an anomalous candidate segment when the following relationship is met: ; in, The preset discrimination threshold is used; S321. Verify the continuity of candidate abnormal sections along the pipe axis. Only if the spatial continuity length of the abnormal section is... A section is considered a valid defect feature section when the following relationship is met: .

[0022] Compared with existing technologies, In one possible implementation, step S4 specifically includes the following steps: S41. Using the axial section of the pipeline as the basic evaluation unit, the number of defect features within each section is... Spatial distribution density and the magnitude of change of characteristic parameters A comprehensive analysis was conducted, and state parameters were constructed to characterize the pipeline condition of this section. Its expression is: in, , , Preset weighting coefficients; S42. Under multiple testing conditions, the state parameters obtained from different testing cycles are aligned in a unified pipeline coordinate system. By comparing the changes in state parameters of the same pipeline section in different time dimensions, the evolution trend of pipeline state is analyzed.

[0023] Compared with existing technologies, the two-level defect determination mechanism of "threshold initial screening + continuity verification" is adopted. By introducing a minimum continuity length constraint on the basis of statistical anomaly detection, the rigor and reliability of defect identification are ensured: The first level is a quantitative comparison based on a preset threshold, which can objectively and efficiently screen out potential abnormal segments and avoid subjective bias; The second level applies strict axial continuity verification to the candidate segments, effectively distinguishing between signal anomalies caused by real structural defects (usually with spatial continuity) and occasional fluctuations caused by random noise and transient interference (usually in the form of isolated points), thereby significantly reducing the false alarm rate of the system at the root, so that the finally confirmed defect feature segments have higher confidence and engineering guidance value.

[0024] In one possible implementation, step S5 specifically includes the following steps: S51. Organize the assessment results in a unified manner: Sort the pipeline condition assessment results according to the pipeline axial position and store them in the form of structured data, so that the assessment results can be quickly retrieved and called according to the pipeline spatial position, and retain the assessment result data corresponding to each inspection cycle. S52. Output the assessment results in a unified manner: Based on the pipeline operation and management requirements, output the assessment result data, which includes pipeline section location identifiers, corresponding status parameters and defect characteristic information, for use in subsequent pipeline maintenance decisions, operation records or data archiving.

[0025] Compared with existing technologies, by sorting and storing data by axial position, a seamless mapping between assessment results and pipeline physical location is achieved, facilitating maintenance personnel to quickly locate and verify problem sections. The structured data format supports efficient retrieval, multi-period data comparison, and long-term trend analysis, providing a continuous and reliable data foundation for pipeline health management. The final standardized report integrates location, status, and defect details, directly meeting the engineering needs of maintenance decision-making, operation records, and data archiving, significantly improving the efficiency and scientific nature of closed-loop management from detection to maintenance. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the structure of an online non-destructive testing and condition assessment system for long-distance liquid pipelines according to the present invention; Figure 2 This is a flowchart of an online non-destructive testing and condition assessment method for long-distance liquid pipelines according to the present invention.

[0027] Explanation of reference numerals in the attached figures: 1-Front bumper; 2-Battery module; 3-Electronics and data processing module; 4-Rubber connecting ring; 5-Detection module; 6-Module connecting rod; 7-Central connecting shaft; 8-Universal connecting assembly; 9-Connecting flange; 10-Sensor bearing assembly; 11-Ultrasonic transducer mounting hole; 12-Signal transmission interface. Detailed Implementation

[0028] First, those skilled in the art should understand that these embodiments are merely used to explain the technical principles of the embodiments of this application and are not intended to limit the scope of protection of the embodiments of this application. Those skilled in the art can make adjustments as needed to adapt to specific application scenarios.

[0029] In the description of the embodiments of this application, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application based on the specific circumstances.

[0030] In the embodiments of this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0031] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0032] See Figure 1 As shown in the figure, this application discloses an online non-destructive testing and condition assessment system for long-distance liquid pipelines, including: The detection device is used to continuously perform non-destructive testing on the inner wall of a pipeline as it moves with the medium inside the pipeline, and outputs a detection signal. The data processing and evaluation device is used to receive the detection signal and process the detection data, identify defects, and evaluate the pipeline condition.

[0033] The detection device is a modular detection device arranged coaxially, including a front bumper 1, a battery module 2, an electronics and data processing module 3, a rubber connecting ring 4, a detection module 5, a module connecting rod 6, a central connecting shaft 7, a universal joint assembly 8, a connecting flange 9, a sensor bearing assembly 10, an ultrasonic transducer mounting hole 11, and a signal transmission interface 12. All functional modules are arranged coaxially along the central connecting shaft 7 to form an integrated structure.

[0034] The front bumper 1 is located at the outermost front end of the device. The front bumper 1 is a rubber buffer component used to provide cushioning protection for the head structure during device operation. The battery module 2 is located in the battery compartment inside the front section of the device. It is used to power the entire device and is fixed by the limiting structure on the inner wall of the cylinder to limit its axial displacement and radial sway.

[0035] The electronic and data processing module 3 is located inside the middle section of the cylinder of the device and is fixedly connected to the cylinder. It is used to realize the acquisition, storage and processing of detection data.

[0036] A detection module 5 is installed at the end of the device. The detection module 5 is connected to the preceding module via a module connecting rod 6. The module connecting rod 6 is arranged along the axial direction of the device to achieve axial positioning and structural support for the detection module 5. A rubber connecting ring 4 is provided on the outer periphery of the docking position between the detection module 5 and the electronic and data processing module 3 to achieve flexible connection and vibration damping between the modules.

[0037] The central connecting shaft 7 runs through each functional module along the axis of the device to ensure the coaxial connection of each module and the overall structural stability.

[0038] Universal joint components 8 are installed at the module connection points (between battery module 2 and electronics and data processing module 3, and between electronics and data processing module 3 and detection module 5) to compensate for attitude deviations generated during device operation while maintaining axial connection continuity.

[0039] A connecting flange 9 is provided on the outer periphery between the battery module 2 and the data processing module 3 to realize the assembly and fixation between the modules and facilitate disassembly and maintenance.

[0040] The detection module 5 internally houses a sensor support assembly 12, which serves as the support and positioning base for the detection module 5, used for the subsequent installation, positioning, and support of the ultrasonic transducer. The sensor support assembly 12 has multiple ultrasonic transducer mounting holes 11, which define the installation position and orientation of the ultrasonic transducer.

[0041] The ultrasonic transducer is installed in the ultrasonic transducer mounting hole 11. Its arrangement includes a straight arrangement in which the direction of the sound beam is basically consistent with the axis of the device, and an oblique arrangement in which the direction of the sound beam forms a set angle with the axis of the device, thereby realizing multi-directional detection coverage under the same detection section.

[0042] A signal transmission interface 12 is provided between the detection module 5 and the electronic and data processing module 3 to realize the transmission connection of detection signals and data.

[0043] In this embodiment, the data processing and evaluation apparatus includes: The data receiving and positioning module is used to convert the detection signal sequence arranged in time sequence into the detection data sequence arranged in the axial position of the pipeline according to the operating speed and sampling time interval of the detection device; The preprocessing and feature extraction module, connected to the data receiving and positioning module, is used to perform noise suppression, baseline correction and amplitude normalization on the detection data, and extract feature parameters including amplitude features, axial variation gradient features and abnormal energy distribution features. The defect identification module, connected to the preprocessing and feature extraction module, is used to perform segmented scanning along the pipe axis based on feature parameters, and introduces axial spatial continuity as a necessary condition for defect judgment. The status assessment module, connected to the defect identification module, is used to convert the identified defect features into status parameters at the pipeline section level, thereby enabling a quantitative assessment of the pipeline's operating status.

[0044] This embodiment has the following advantages: 1. High structural stability during testing This invention adopts a modular structure coaxially integrated along the central connecting axis, which enables each functional module to maintain a fixed axial consistency during operation. This effectively suppresses the influence of pipe bending, media disturbance and operating vibration on the detection posture, reduces the risk of detection direction deviation, and improves the stability and repeatability of the detection results, thereby reducing the dispersion of detection results in the same section under multiple detection conditions.

[0045] 2. Strong multi-directional detection capability and high defect detection rate. By uniformly positioning and installing multi-directional ultrasonic transducers using sensor-bearing components, different sound beam directions can form a coordinated coverage within the same detection cross section, enhancing the response capability to defects with different orientations, reducing missed detections caused by a single detection direction, improving the completeness of defect identification, and thus reducing missed detections caused by differences in defect orientation.

[0046] 3. Modular design, low maintenance and expansion costs. Each detection and processing module adopts a modular assembly method, which can be disassembled, replaced and upgraded as needed, reducing on-site maintenance procedures and shortening downtime, thereby reducing the overall maintenance cost caused by the failure of a single module, and is suitable for engineering applications of long-distance pipelines.

[0047] See Figure 2 The present embodiment relates to an evaluation method for an online non-destructive testing and condition assessment system for long-distance liquid pipelines, comprising the following steps: S1. The pipeline detection device collects detection signal sequences in a time sequence. Based on the operating speed of the detection device and the sampling time interval, it calculates the axial position of the pipeline corresponding to each detection signal, establishes the correspondence between the detection signals and the spatial position of the pipeline, and forms a detection data sequence indexed by axial position. S2. After preprocessing the detection data sequence, feature parameters are extracted, and the feature parameters are bound to the corresponding axial position of the pipe to construct a feature parameter set; S3. Perform segmented scanning of characteristic parameters along the pipeline axis, and identify the segments that meet the preset abnormal conditions and are continuously distributed in the axial direction to reach the minimum length as defect characteristic segments; S4. Based on the number of defect features, spatial distribution density, and variation range of characteristic parameters in each pipeline section, calculate the state parameters of each section and complete the pipeline condition assessment. S5. Output evaluation results including pipeline section location identifiers, status parameters, and defect characteristic information.

[0048] The time signal is strictly converted into spatial positioning data to ensure the accuracy of the defect axial position; the "axial continuity" constraint is introduced in the identification process to effectively distinguish between real defects and random noise, greatly reducing the false alarm rate; and by generating section-level state parameters by comprehensively considering the characteristics of defect density, distribution and intensity, the evaluation results are made objective, quantitative and can directly support maintenance decisions, significantly improving the reliability, interpretability and engineering applicability of the detection results.

[0049] In this embodiment, step S1 involves receiving, locating, and serializing the detection data. During the online non-destructive testing process, the pipeline inspection device continuously inspects the inner wall of the pipeline according to a preset sampling frequency and outputs a sequence of detection signals arranged in chronological order.

[0050] Step S1 specifically includes the following steps: S11. The detection device operating inside the pipeline continuously detects the inner wall of the pipeline according to the preset sampling frequency and outputs a sequence of detection signals arranged in chronological order. S12. Receive the detection signal sequence, and calculate the axial position of the pipeline corresponding to each detection signal based on the running speed of the detection device in the pipeline and the sampling time interval, thereby establishing the correspondence between the detection signal and the spatial position of the pipeline.

[0051] For the i-th detection signal, its corresponding pipe axial position Determined according to the following relationship: in, To detect the operating speed of the device, The sampling time interval between adjacent detected signals; S13. After completing the spatial positioning, the detection signal sequence originally arranged in chronological order is converted into a detection data sequence arranged in the axial position of the pipeline, so that the detection data is stored and retrieved with the spatial position of the pipeline as an index, thereby realizing the orderly expression of the detection data in the spatial dimension of the pipeline.

[0052] To address spatial discontinuities caused by fluctuations in operating speed, changes in sampling intervals, or missing local data, the software module performs continuity correction on the detection data sequence. It compensates for missing data by interpolation or resampling, ensuring that the detection data remains continuously distributed along the axial direction of the pipeline.

[0053] Through the above processing, the software module completes the reception, spatial positioning, and serialization of detection data, providing a unified spatial coordinate basis for subsequent data analysis.

[0054] In this embodiment, step S2 involves preprocessing and extracting feature parameters from the detection data. After spatial localization and serialization of the detection data, uniform preprocessing is performed on the detection data to eliminate the influence of changes in the operating status of the detection device, medium conditions, and sampling differences on the consistency of the detection signal.

[0055] Specifically, step S2 includes the following steps: S21. Detect the detection signal in the detection data sequence. Preprocessing is performed, including noise suppression, baseline correction, and amplitude normalization. The expression for amplitude normalization is: in, and These represent the mean and standard deviation of the detection signal within the corresponding pipeline section, respectively. S22. After preprocessing, extract the feature parameters from the detection data to perform structured characterization of the detection signal in the time domain, spatial domain, and energy distribution. The feature parameters include the amplitude characteristics, axial gradient characteristics, and anomalous energy distribution characteristics of the detection signal. The expression for the axial gradient characteristic is as follows: in, The axial position interval between adjacent pipes; S23. Bind each feature parameter to the corresponding axial position of the pipe to construct a unified set of feature parameters.

[0056] In this embodiment, step S3 is defect feature identification and spatial mapping. After obtaining feature data with spatial consistency and structured expression, the abnormal state of the pipeline inner wall is identified and located based on the feature parameter set.

[0057] Step S3 specifically includes the following steps: S31. Perform segmented scanning of characteristic parameters along the pipeline axis, divide the pipeline into several continuous analysis sections, and perform statistical analysis on the characteristic parameters in each section to identify effective defect characteristic sections. S32. Bind the confirmed defect feature section to its corresponding pipeline axial position, and record it using the pipeline spatial position as an index, so that the defect features form a clear and traceable mapping relationship in the pipeline spatial dimension.

[0058] In step S31, statistical analysis is performed on the characteristic parameters within each segment to identify valid defect characteristic segments. This includes the following steps: S312, When the characteristic parameters of a certain segment Relative to the characteristic parameters of the reference section A segment is marked as an anomalous candidate segment when the following relationship is met: ; in, The preset discrimination threshold is used; S321. Verify the continuity of candidate abnormal sections along the pipe axis. Only if the spatial continuity length of the abnormal section is... A section is considered a valid defect feature section when the following relationship is met: .

[0059] In this embodiment, step S4 is pipeline condition assessment. After completing defect feature identification and spatial mapping, the overall operating status of the pipeline and its sections is assessed based on the distribution of defect features along the pipeline axis.

[0060] Step S4 specifically includes the following steps: S41. Using the axial section of the pipeline as the basic evaluation unit, the number of defect features within each section is... Spatial distribution density and the magnitude of change of characteristic parameters A comprehensive analysis was conducted, and state parameters were constructed to characterize the pipeline condition of this section. Its expression is: in, , , Preset weighting coefficients; S42. Under multiple testing conditions, the state parameters obtained from different testing cycles are aligned in a unified pipeline coordinate system. By comparing the changes in state parameters of the same pipeline section in different time dimensions, the evolution trend of pipeline state is analyzed.

[0061] In this embodiment, step S5 involves organizing and outputting the evaluation results. After completing the pipeline condition evaluation, the software module performs unified organization and output processing of the evaluation results.

[0062] Step S5 specifically includes the following steps: S51. Organize the assessment results in a unified manner: Sort the pipeline condition assessment results according to the pipeline axial position and store them in the form of structured data, so that the assessment results can be quickly retrieved and called according to the pipeline spatial position, and retain the assessment result data corresponding to each inspection cycle. S52. Output the assessment results in a unified manner: Based on the pipeline operation and management requirements, output the assessment result data, which includes pipeline section location identifiers, corresponding status parameters and defect characteristic information, for use in subsequent pipeline maintenance decisions, operation records or data archiving.

[0063] This embodiment has the following advantages: 1. Spatial indexed data processing, high positioning accuracy The inspection data is organized and analyzed using the axial spatial coordinates of the pipeline as the core index. Data from different inspection cycles can be aligned under a unified spatial coordinate system, reducing section mismatch and position offset problems, improving the accuracy of defect location and the traceability of results, thereby reducing defect location calibration errors and improving the traceability of results.

[0064] 2. Continuity constraint discrimination mechanism with low false alarm rate. Introducing axial spatial continuity as a necessary criterion in the defect identification process effectively distinguishes between real structural anomalies and occasional signal fluctuations, reduces misjudgments caused by noise interference, lowers the cost of invalid re-inspection and misjudgment processing, and thus reduces the probability of invalid re-inspection triggered by isolated anomalies.

[0065] 3. Parameterized status output improves operational and maintenance decision-making efficiency. By transforming discrete defect characteristics into section-level state parameters, a quantitative description of pipeline operating status can be achieved, facilitating comparative analysis between different sections and different inspection cycles, thereby reducing reliance on manual interpretation and improving the consistency of operation and maintenance decisions.

[0066] In the description of the embodiments of this application, it should be noted that the terms "inner" and "outer" and other terms indicating direction or positional relationship are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or component must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this application.

[0067] In the description of this application, the references to terms such as "an embodiment," "some embodiments," "in this embodiment," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0068] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A long liquid pipeline on-line non-destructive testing and condition assessment system, characterized in that, include: The detection device is used to continuously perform non-destructive testing on the inner wall of a pipeline as it moves with the medium inside the pipeline, and outputs a detection signal. A data processing and evaluation device is used to receive the detection signal and process the detection data, identify defects, and evaluate the pipeline condition. The detection device includes a housing, a central connecting shaft (7) disposed within the housing, and a group of functional modules arranged coaxially and fixedly connected along the central connecting shaft (7) to form an integrated structure. The functional module group includes a front bumper (1) for buffer protection, a battery module (2) for power supply, an electronic and data processing module (3) for data acquisition and processing, and a detection module (5); The detection module (5) is provided with a sensor carrier assembly (10), and the sensor carrier assembly (10) is provided with multiple ultrasonic transducer mounting holes (11), and each ultrasonic transducer mounting hole (11) is provided with an ultrasonic transducer.

2. The long liquid pipeline online non-destructive testing and condition assessment system of claim 1, wherein, The data processing and evaluation device includes: The data receiving and positioning module is used to convert the detection signal sequence arranged in time sequence into the detection data sequence arranged in the axial position of the pipeline according to the operating speed and sampling time interval of the detection device; The preprocessing and feature extraction module, connected to the data receiving and positioning module, is used to perform noise suppression, baseline correction and amplitude normalization on the detection data, and extract feature parameters including amplitude features, axial variation gradient features and abnormal energy distribution features. The defect identification module, connected to the preprocessing and feature extraction module, is used to perform segmented scanning along the pipe axis based on feature parameters, and introduces axial spatial continuity as a necessary condition for defect judgment. The status assessment module, connected to the defect identification module, is used to convert the identified defect features into status parameters at the pipeline section level, thereby enabling a quantitative assessment of the pipeline's operating status.

3. An evaluation method of the on-line non-destructive testing and condition assessment system for long liquid pipelines according to claims 1-2, characterized by, Includes the following steps: S1. The pipeline detection device collects detection signal sequences in a time sequence. Based on the operating speed of the detection device and the sampling time interval, it calculates the axial position of the pipeline corresponding to each detection signal, establishes the correspondence between the detection signals and the spatial position of the pipeline, and forms a detection data sequence indexed by axial position. S2. After preprocessing the detection data sequence, feature parameters are extracted, and the feature parameters are bound to the corresponding axial position of the pipe to construct a feature parameter set; S3. Perform segmented scanning of characteristic parameters along the pipeline axis, and identify the segments that meet the preset abnormal conditions and are continuously distributed in the axial direction to reach the minimum length as defect characteristic segments; S4. Based on the number of defect features, spatial distribution density, and variation range of characteristic parameters in each pipeline section, calculate the state parameters of each section and complete the pipeline condition assessment. S5. Output evaluation results including pipeline section location identifiers, status parameters, and defect characteristic information.

4. The method for online non-destructive testing and condition assessment of long-distance liquid pipelines according to claim 3, characterized in that, Step S1 specifically includes the following steps: S12. The detection device running inside the pipeline continuously detects the inner wall of the pipeline according to the preset sampling frequency and outputs a sequence of detection signals arranged in chronological order. S11. Receive the detection signal sequence, and calculate the axial position of the pipeline corresponding to each detection signal based on the operating speed of the detection device in the pipeline and the sampling time interval; for the i-th detection signal, its corresponding axial position of the pipeline... Determined according to the following relationship: in, To detect the operating speed of the device, The sampling time interval between adjacent detected signals; S13. Convert the original time-ordered detection signal sequence into a detection data sequence arranged according to the axial position of the pipeline, so that the detection data can be stored and retrieved using the spatial position of the pipeline as an index.

5. The method for online non-destructive testing and condition assessment of long-distance liquid pipelines according to claim 3, characterized in that, In step S13, if there are spatial discontinuities in the detection data due to fluctuations in operating speed, changes in sampling interval, or missing local data, the detection data sequence is subjected to continuity correction processing. The missing data is compensated by interpolation or resampling to ensure that the detection data is continuously distributed in the axial direction of the pipeline.

6. The method for online non-destructive testing and condition assessment of long-distance liquid pipelines according to claim 3, characterized in that, Step S2 specifically includes the following steps: S21. Detect the detection signal in the detection data sequence. Preprocessing is performed, including amplitude normalization, and its expression is: in, and These represent the mean and standard deviation of the detection signal within the corresponding pipeline section, respectively. S22. Extract the feature parameters from the detection data. The feature parameters include the axial change gradient feature of the detection signal, and its expression is: in, The axial position interval between adjacent pipes; S23. Bind each feature parameter to the corresponding axial position of the pipe to construct a unified set of feature parameters.

7. The method for online non-destructive testing and condition assessment of long-distance liquid pipelines according to claim 3, characterized in that, Step S3 specifically includes the following steps: S31. Perform segmented scanning of characteristic parameters along the pipeline axis, divide the pipeline into several continuous analysis sections, and perform statistical analysis on the characteristic parameters in each section to identify effective defect characteristic sections. S32. Bind the confirmed defect feature section to its corresponding axial position of the pipeline, and record it using the pipeline spatial position as an index.

8. The method for online non-destructive testing and condition assessment of long-distance liquid pipelines according to claim 3, characterized in that, In step S31, statistical analysis is performed on the characteristic parameters within each segment to identify valid defect characteristic segments. This includes the following steps: S312, When the characteristic parameters of a certain segment Relative to the characteristic parameters of the reference section A segment is marked as an anomalous candidate segment when the following relationship is met: ; in, The preset discrimination threshold is used; S321. Verify the continuity of candidate abnormal sections along the pipe axis. Only if the spatial continuity length of the abnormal section is... A section is considered a valid defect feature section when the following relationship is met: .

9. The method for online non-destructive testing and condition assessment of long-distance liquid pipelines according to claim 3, characterized in that, Step S4 specifically includes the following steps: S41. Using the axial section of the pipeline as the basic evaluation unit, the number of defect features within each section is... Spatial distribution density and the magnitude of change of characteristic parameters A comprehensive analysis was conducted, and state parameters were constructed to characterize the pipeline condition of this section. Its expression is: in, , , Preset weighting coefficients; S42. Under multiple testing conditions, the state parameters obtained from different testing cycles are aligned in a unified pipeline coordinate system. By comparing the changes in state parameters of the same pipeline section in different time dimensions, the evolution trend of pipeline state is analyzed.

10. The method for online non-destructive testing and condition assessment of long-distance liquid pipelines according to claim 3, characterized in that, Step S5 specifically includes the following steps: S51. Organize the assessment results in a unified manner: Sort the pipeline condition assessment results according to the pipeline axial position and store them in the form of structured data, so that the assessment results can be quickly retrieved and called according to the pipeline spatial position, and retain the assessment result data corresponding to each inspection cycle. S52. Output the assessment results in a unified manner: Based on the pipeline operation and management requirements, output the assessment result data, which includes pipeline section location identifiers, corresponding status parameters and defect characteristic information, for use in subsequent pipeline maintenance decisions, operation records or data archiving.

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