X-ray flaw detection robot for linear continuous pipe of overhead transmission line

Abstract

The invention discloses an overhead transmission line linear continuous pipe X-ray flaw detection robot, and relates to the technical field of power equipment detection, the robot comprises a sensor unit, a motion and support assembly, a control unit, an intelligent early warning assembly, a data storage device, a communication module and a power supply module; according to the invention, the X-ray flaw detection sensor, the ultrasonic sensor, the electromagnetic sensor and the temperature sensor are integrated, and the data fusion and preprocessing technology is combined, so that the omnibearing and high-precision state monitoring of the linear continuous pipe of the overhead transmission line is realized, and the detection efficiency and accuracy are greatly improved; according to the method, the robot can rapidly identify potential defects in a complex and changeable detection environment, in addition, through the data fusion technology, data redundancy can be effectively reduced, the data processing speed is increased, and the detection efficiency is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of power equipment detection, in particular to an X-ray flaw detection robot for straight-line continuous pipes of overhead power transmission lines. Background Art

[0002] With the rapid development of the power industry, overhead transmission lines, as an important part of power transmission, are increasingly valued for their safety and reliability. As a key component connecting various transmission lines, the linear continuous pipes of overhead transmission lines are crucial for state monitoring and maintenance to ensure the continuity of power transmission. However, since the continuous pipes are usually installed in complex outdoor environments and are affected by natural environmental factors and mechanical stress for a long time, they are prone to various damages and defects, such as cracks and corrosion. If these defects are not discovered and handled in time, they may lead to line failures and even cause large-scale power outages, causing huge losses to society and the economy. Therefore, regular and efficient state monitoring of the linear continuous pipes of overhead transmission lines has become a technical problem that the power industry urgently needs to solve.

[0003] Traditional detection methods for linear continuous pipes of overhead transmission lines mainly rely on manual inspections and single sensor detection. Although manual inspections can find some obvious defects, they are often difficult to find small or hidden defects due to the limitations of personnel experience and attention, and the inspection efficiency is low. Although single sensor detection methods can detect specific types of defects, they are often unable to achieve full-scale monitoring of continuous pipes due to limitations on sensor types and quantities, and defects are easily missed. In addition, traditional detection methods also have the problem of insufficient data processing and analysis capabilities, making it difficult to conduct in-depth mining and intelligent judgment of detection data, resulting in low accuracy and efficiency in defect identification.

[0004] Therefore, the development of an X-ray flaw detection robot for linear continuous pipes of overhead transmission lines will greatly improve the state monitoring level of linear continuous pipes of overhead transmission lines, ensure the safety and reliability of power transmission, and has important social and economic value. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide an overhead power transmission line straight continuous pipe X-ray flaw detection robot, which integrates multiple parts such as sensor units, motion and support components, control units, and intelligent early warning components. Through multi-sensor data fusion technology, pipeline status data is collected in all directions, and intelligent algorithms are used to make intelligent judgments and early warnings on defect characteristics. The robot has a remote communication function and can send detection data and early warning information in real time, providing key information such as fault location, type, and predicted occurrence time for operation and maintenance personnel. The application of the present invention effectively improves the efficiency and accuracy of status monitoring of overhead power transmission line straight continuous pipes, ensures the safety and reliability of power transmission, and has broad application prospects and socio-economic value.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an overhead power transmission line linear continuous tube X-ray flaw detection robot, the robot comprising a sensor unit, a motion and support component, a control unit, an intelligent early warning component, a data storage device, a communication module and a power module;

[0007] The sensor unit integrates multiple sensors to collect pipeline status data in all directions when the robot runs on the straight continuous pipe of the overhead transmission line and transmits it to the control unit;

[0008] The motion and support assembly comprises a moving mechanism and a supporting structure, wherein the moving mechanism can move on the straight continuous pipe of the overhead power transmission line, and the supporting structure is used to carry the remaining components of the robot;

[0009] The control unit is composed of a data fusion and preprocessing module, a defect feature intelligent judgment module and a monitoring data early warning module;

[0010] The intelligent early warning component is equipped with an indicator light, a speaker and a display screen, the indicator light can emit a light signal, the speaker can emit a sound, and the display screen is used to display a user interaction interface;

[0011] The data storage device: has a storage chip and a hard disk device, and is used to store various data generated during the detection process;

[0012] The communication module: adopts wireless communication, has remote communication function, and can send and receive signals in real time;

[0013] The power module is composed of rechargeable batteries and supplies the power required for the operation of various components of the robot.

[0014] Furthermore, the sensors used in the sensor unit are: X-ray flaw detection sensors, ultrasonic sensors, electromagnetic sensors and temperature sensors.

[0015] Furthermore, the data fusion and preprocessing module in the control unit is connected to the multi-sensor collaborative detection sensor unit through a high-speed data bus, receives the transmitted data, and summarizes it, converts data in different formats into a unified format through a data conversion program, removes noise in the image through noise reduction, extracts useful information through filtering operations, and uses a multi-sensor data fusion formula to fuse the data collected by multiple sensors to form a comprehensive data set.

[0016] Furthermore, the data fusion and preprocessing module in the control unit uses a multi-sensor data fusion formula to fuse the multi-sensor collected data, and the formula is: Among them, D totalis the integrated data after fusion, D i They represent the data collected by the i-th sensor, i=1 is the data of the X-ray flaw detection sensor, i=2 is the data of the ultrasonic sensor, and so on. i is the weight of each sensor data, which is dynamically adjusted according to the reliability of the sensor and the current detection environment, i is the attenuation coefficient related to the sensor characteristics, and t is the detection time.

[0017] Furthermore, the defect feature intelligent judgment module in the control unit extracts features that can characterize the defects from the fused comprehensive data through a defect feature extraction formula, judges the type of defect through a defect type judgment formula in combination with the internally stored defect feature database, evaluates its severity, and gives preliminary processing suggestions.

[0018] Furthermore, the defect feature intelligent judgment module in the control unit extracts features that can characterize defects from the fused comprehensive data through a defect feature extraction formula. Let the fused data be D total , the formula is: Among them, F defect is the extracted defect feature value, x is the data dimension variable, φ is the phase parameter related to the defect spatial distribution, and θ is the frequency parameter used to match the periodic changes of the defect characteristics.

[0019] Furthermore, the defect feature intelligent judgment module in the control unit judges the defect type by a defect type judgment formula in combination with the extracted defect features, and the formula is: Among them, T defect Indicates the defect type identifier, c k is the coefficient related to different defect types, obtained through historical data training, T threshold is the defect judgment threshold, and sgn is the sign function.

[0020] Furthermore, the monitoring data warning module in the control unit collects historical detection and real-time monitoring data, and uses the fault prediction formula to predict the probability of future faults. When the fault prediction probability exceeds the corresponding warning threshold T failure The early warning is triggered in real time, and the operation and maintenance personnel are informed through sound and light alarms combined with the defect diagnosis results, and the information of fault location, type and predicted occurrence time is sent to the operation and maintenance personnel.

[0021] Furthermore, the monitoring data early warning module in the control unit predicts the probability of future failures based on the currently detected defect characteristics and historical data. The formula is: Among them, P failureis the probability of future failure, γ is the coefficient related to the defect development speed, t future is the future prediction time point, F defect t is the defect characteristic value changing with time.

[0022] Compared with the existing technology, the overhead transmission line straight continuous pipe X-ray flaw detection robot has the following beneficial effects:

[0023] 1. The present invention integrates multiple sensors such as X-ray flaw detection sensors, ultrasonic sensors, electromagnetic sensors and temperature sensors, and combines data fusion and preprocessing technology to achieve all-round and high-precision status monitoring of the linear continuous pipes of overhead power transmission lines, greatly improving the detection efficiency and accuracy, so that the robot can quickly identify potential defects in complex and changeable detection environments. Compared with the traditional single sensor detection method, the present invention can combine the advantages of multiple sensors to avoid possible misjudgments or missed judgments of a single sensor, thereby ensuring the comprehensiveness and reliability of the detection results. In addition, through data fusion technology, it can also effectively reduce data redundancy, improve data processing speed, and further improve detection efficiency.

[0024] 2. The present invention realizes intelligent identification of defect features and fault prediction by introducing a defect feature intelligent judgment module and a monitoring data early warning module in the control unit, which not only improves the accuracy and efficiency of defect diagnosis, but also can timely discover and warn of potential faults. Specifically, the defect feature intelligent judgment module can automatically extract the features characterizing the defects from the integrated data after fusion, and compare them with the internally stored defect feature database, so as to quickly and accurately judge the defect type and evaluate its severity. The monitoring data early warning module can collect historical detection and real-time monitoring data, and use the fault prediction formula to predict the probability of future faults. Once the predicted probability exceeds the early warning threshold, the early warning is immediately triggered, and the information of the fault location, type, and predicted occurrence time is sent to the operation and maintenance personnel to ensure that the operation and maintenance personnel can take timely measures to avoid faults. This not only improves the safety and stability of the power system, but also reduces the operation and maintenance costs and risks.

[0025] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 This is the architecture diagram of the X-ray flaw detection robot for straight-line continuous pipes of overhead transmission lines;

[0028] Figure 2 This is a schematic diagram of the detection process and control unit functions of the linear continuous tube X-ray flaw detection robot for overhead transmission lines. DETAILED DESCRIPTION

[0029] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0030] Embodiment 1:

[0031] Urban power transmission line detection

[0032] In the city's overhead power transmission lines, a certain section of straight continuous pipe needs to be regularly inspected to ensure safe and stable power transmission.

[0033] First, the overhead transmission line straight continuous pipe X-ray flaw detection robot is placed on the transmission line straight continuous pipe to be inspected. The robot's movement and support components play a role, and its moving mechanism moves stably on the pipeline. The support structure carries the sensor unit, control unit, intelligent early warning component, communication module and power module components.

[0034] During the movement, the X-ray flaw detection sensors, ultrasonic sensors, electromagnetic sensors and temperature sensors in the sensor unit begin to collect pipeline status data in all directions. For example, the X-ray flaw detection sensor penetrates the pipeline to detect whether there are cracks or holes inside; the ultrasonic sensor analyzes the thickness changes of the pipeline wall and the integrity of the internal structure by transmitting and receiving ultrasonic waves; the electromagnetic sensor monitors the changes in the electromagnetic field around the pipeline to determine whether there are electromagnetic anomalies caused by metal corrosion or other reasons; the temperature sensor measures the surface temperature of the pipeline in real time. If the local temperature is too high, it may indicate poor contact or excessive resistance. These data are transmitted to the control unit in real time.

[0035] After the data fusion and preprocessing module in the control unit receives data from different sensors, it summarizes them through a high-speed data bus. Then, the data conversion program converts data in different formats into a unified format, and then performs noise reduction and filtering operations. For example, it removes noise interference in X-ray images to make the images clearer so as to extract useful information. It also uses the multi-sensor data fusion formula to fuse the data collected by multiple sensors. The formula is: Among them, D total is the integrated data after fusion, D i They represent the data collected by the i-th sensor, i=1 is the data of the X-ray flaw detection sensor, i=2 is the data of the ultrasonic sensor, and so on. i is the weight of each sensor data, which will be dynamically adjusted according to the reliability of the sensor and the current detection environment, i is the attenuation coefficient related to the sensor characteristics, and t is the detection time to form a comprehensive data set.

[0036] The defect feature intelligent judgment module extracts features that can characterize defects from the comprehensive data set, and extracts features that can characterize defects from the fused comprehensive data through the defect feature extraction formula. Let the fused data be D total , the formula is: Among them, F defect is the extracted defect feature value, x is the data dimension variable, φ and θ are the phase and frequency parameters related to the defect feature, and the defect type is determined by the defect type judgment formula combined with the internally stored defect feature database. The formula is: Among them, T defect Indicates the defect type identifier, c k is the coefficient related to different defect types, obtained through training with a large amount of historical data, T threshold is the judgment threshold, sgn is the sign function. Assuming that an abnormal signal is detected in a certain part of the pipeline, the module will analyze and judge that this may be a minor corrosion defect caused by long-term erosion by wind and rain, and evaluate its severity as mild. At the same time, it will give preliminary treatment suggestions, such as anti-corrosion treatment of the part during the next maintenance.

[0037] The monitoring data early warning module collects historical detection and real-time monitoring data to predict the probability of future failures. The formula is: Among them, P failure is the probability of future failure, γ is the coefficient related to the defect development speed, t future is the future prediction time point, F defect t is the defect characteristic value that changes with time. If it is predicted that the corrosion site may further deteriorate and affect power transmission safety in the next three months, and the fault prediction probability exceeds the set warning threshold T failureThe indicator light of the intelligent early warning component will light up a red light signal, the speaker will sound an alarm, and the display screen will show the fault location, type (corrosion defect), and predicted occurrence information. At the same time, the communication module will remotely send this information to the operation and maintenance personnel so that they can take timely measures.

[0038] During the entire inspection process, the data storage device will continue to retain various types of data generated during the inspection process, including data collected by sensors, processed comprehensive data sets, and defect judgment results, providing a basis for subsequent analysis and maintenance. The rechargeable battery of the power module provides stable power support for various components of the robot to ensure that the inspection work can be completed smoothly.

[0039] To sum up, in the urban power transmission line detection scenario, the flaw detection robot of the present invention plays a key role. Through its integrated multiple sensors, such as X-ray, ultrasonic, electromagnetic and temperature sensors, it comprehensively collects pipeline data. The control unit effectively processes and analyzes the data, accurately determines the defect type and severity, and can predict the probability of failure. The intelligent early warning component promptly transmits key information to the operation and maintenance personnel, ensuring the safety of power transmission. The data storage device provides a basis for subsequent maintenance, and the power module ensures operation. This robot effectively solves the problem of urban power transmission line detection, improves detection efficiency and accuracy, reduces operation and maintenance risks, and is of great significance to the stable supply of urban power.

[0040] Embodiment 2:

[0041] Transmission line inspection in mountainous areas

[0042] In the overhead power transmission line environment in mountainous areas, due to the complex terrain and inconvenient transportation, it is difficult to maintain the power transmission lines, so it is particularly important to use the robot of the present invention to perform flaw detection.

[0043] After the robot is transported to the vicinity of the linear continuous pipe of the mountainous transmission line, it is started and placed on the pipeline. The motion and support components adapt to the complex terrain and climatic conditions in the mountainous area to ensure that the robot can move safely and stably on the pipeline. For example, the support structure is designed with better stability and adaptability, and can maintain the robot's balance on uneven pipeline surfaces or in strong winds.

[0044] The sensor unit starts working and each sensor actively collects data. In mountainous areas, due to complex environmental factors, there may be more potential risks, such as pipeline displacement or damage caused by landslide geological disasters, or pipeline corrosion accelerated by humid environment. X-ray flaw detection sensors can detect fine cracks inside the pipeline caused by external forces. Ultrasonic sensors can detect uneven thickness of pipeline walls caused by long-term vibration or corrosion. Electromagnetic sensors monitor electromagnetic field anomalies caused by changes in soil properties around the pipeline or metal corrosion. Temperature sensors can promptly detect temperature anomalies caused by increased local resistance or changes in ambient temperature.

[0045] After the control unit receives the data, the data fusion and preprocessing module processes the data. In mountainous areas, there may be more electromagnetic interference. To remove these interferences and ensure the accuracy of the data, the multi-sensor data fusion formula is used to fuse the multi-sensor collected data to form a comprehensive data set formula: The defect feature intelligent judgment module performs analysis and extracts features that can characterize defects from the fused comprehensive data through the defect feature extraction formula. The formula is: Combined with the internally stored defect feature database, the defect type is determined by the defect type judgment formula, which is: And evaluate its severity. Suppose a pipeline deformation defect suspected to be caused by a landslide is detected. The module determines the defect type as moderate deformation through comparison with the database and its own intelligent judgment, and gives corresponding treatment suggestions, such as arranging professional personnel to conduct on-site inspection and repair as soon as possible.

[0046] The monitoring data early warning module combines the special environmental factors and historical data of mountainous areas to predict the probability of future failures. The formula is: If it is predicted that the deformation part may cause power transmission failure within the next month and exceeds the warning threshold T failure The intelligent early warning component starts immediately, the indicator light flashes yellow light signal, the speaker emits an alarm sound of specific frequency, and the display screen shows detailed information about the fault location, type (pipeline deformation), and predicted time of occurrence (within one month). The communication module sends this information to the operation and maintenance center at the foot of the mountain via satellite communication (taking into account the possible poor network signal in mountainous areas). After receiving the information, the operation and maintenance personnel can promptly formulate a maintenance plan and prepare relevant equipment and materials so as to go to the mountainous area for maintenance as soon as possible.

[0047] During the entire detection process, the data storage device continuously records data, and the rechargeable battery of the power module maintains stable power output under the special environment of low temperature and high altitude mountainous areas, ensuring the normal operation of the robot and the safe and reliable operation of the power transmission lines in mountainous areas.

[0048] To sum up, in terms of power transmission line detection in mountainous areas, the flaw detection robot has obvious advantages. In view of the complex terrain and harsh environment in mountainous areas, the robot's movement and support components ensure stable movement, the sensors keenly capture various potential defect signals, and the control unit overcomes the problem of electromagnetic interference to process and judge data. Intelligent early warning uses satellite communication to promptly report the situation to the operation and maintenance center, making it convenient for operation and maintenance personnel to respond quickly. The data storage and power supply modules work stably under special environments. This invention greatly improves the feasibility and reliability of power transmission line detection in mountainous areas, reduces power outages caused by line failures, and strongly supports the stable operation of power infrastructure in mountainous areas.

[0049] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. Overhead power transmission line straight line continuous pipe X-ray flaw detection robot, characterized by: The robot includes a sensor unit, a motion and support component, a control unit, an intelligent early warning component, a data storage device, a communication module and a power module; The sensor unit integrates multiple sensors to collect pipeline status data in all directions when the robot runs on the straight continuous pipe of the overhead transmission line and transmits it to the control unit; The motion and support assembly comprises a moving mechanism and a supporting structure, wherein the moving mechanism can move on the straight continuous pipe of the overhead power transmission line, and the supporting structure is used to carry the remaining components of the robot; The control unit is composed of a data fusion and preprocessing module, a defect feature intelligent judgment module and a monitoring data early warning module; The intelligent early warning component is equipped with an indicator light, a speaker and a display screen, the indicator light can emit a light signal, the speaker can emit a sound, and the display screen is used to display a user interaction interface; The data storage device: has a storage chip and a hard disk device, and is used to store various data generated during the detection process; The communication module: adopts wireless communication, has remote communication function, and can send and receive signals in real time; The power module is composed of rechargeable batteries and supplies the power required for the operation of various components of the robot.

2. The X-ray flaw detection robot for linear coiled tubes of overhead power transmission lines according to claim 1, characterized in that: The sensors used in the sensor unit are: X-ray flaw detection sensor, ultrasonic sensor, electromagnetic sensor and temperature sensor.

3. The X-ray flaw detection robot for linear coiled tubes of overhead power transmission lines according to claim 1, characterized in that: The data fusion and preprocessing module in the control unit is connected to the multi-sensor collaborative detection sensor unit through a high-speed data bus, receives the transmitted data, summarizes it, converts data in different formats into a unified format through a data conversion program, removes noise in the image through noise reduction, extracts useful information through filtering operations, and uses a multi-sensor data fusion formula to fuse the data collected by multiple sensors to form a comprehensive data set.

4. The X-ray flaw detection robot for linear coiled tubes of overhead power transmission lines according to claim 3 is characterized in that: The data fusion and preprocessing module in the control unit uses a multi-sensor data fusion formula to fuse the multi-sensor collected data. The formula is: Among them, D total is the integrated data after fusion, D i They represent the data collected by the i-th sensor, i=1 is the X-ray flaw detection sensor data, i=2 is the ultrasonic sensor data, and so on, ω i is the weight of each sensor data, which is dynamically adjusted according to the reliability of the sensor and the current detection environment, i is the attenuation coefficient related to the sensor characteristics, and t is the detection time.

5. The X-ray flaw detection robot for linear coiled tubes of overhead power transmission lines according to claim 1, characterized in that: The defect feature intelligent judgment module in the control unit extracts features that can characterize defects from the fused comprehensive data through a defect feature extraction formula, judges the type of defect through a defect type judgment formula in combination with an internally stored defect feature database, evaluates its severity, and gives preliminary processing suggestions.

6. The X-ray flaw detection robot for linear coiled tubes of overhead power transmission lines according to claim 5, characterized in that: The defect feature intelligent judgment module in the control unit extracts the features that can characterize the defects from the fused comprehensive data through the defect feature extraction formula. Let the fused data be D total , the formula is: where F defect is the extracted defect feature value, x is the data dimension variable, φ is the phase parameter related to the defect spatial distribution, and θ is the frequency parameter used to match the periodic changes of the defect characteristics.

7. The X-ray flaw detection robot for linear coiled tubes of overhead power transmission lines according to claim 5, characterized in that: The defect feature intelligent judgment module in the control unit judges the defect type by combining the extracted defect features with the defect type judgment formula, and the formula is: Among them, T defect Indicates the defect type identifier, c k is the coefficient related to different defect types, obtained through historical data training, T threshold is the defect judgment threshold, and sgn is the sign function.

8. The X-ray flaw detection robot for linear coiled tubes of overhead power transmission lines according to claim 1, characterized in that: The monitoring data warning module in the control unit collects historical detection and real-time monitoring data, and uses the fault prediction formula to predict the probability of future faults. When the fault prediction probability exceeds the corresponding warning threshold T failure The early warning is triggered in real time, and the operation and maintenance personnel are informed through sound and light alarms combined with the defect diagnosis results, and the information of fault location, type and predicted occurrence time is sent to the operation and maintenance personnel.

9. The X-ray flaw detection robot for linear coiled tubes of overhead power transmission lines according to claim 8, characterized in that: The monitoring data early warning module in the control unit predicts the probability of future faults based on the currently detected defect characteristics and historical data. The formula is: Among them, P failure is the probability of future failure, γ is the coefficient related to the defect development speed, t future is the future prediction time point, F defect (t) is the defect characteristic value changing with time.

Images