Rapid non-contact sensing and detection method for bolt loosening defect in transmission line tower

Abstract

The present invention relates to a rapid non-contact sensing and detection method for a bolt loosening defect in a transmission line tower, which belongs to the technical field of power system maintenance and monitoring. The method comprises: constructing a dynamic database of bolt vibration characteristics, and using the principle of impact elastic waves to detect the bolt loosening condition; using acoustic imaging technology, infrared imaging technology and electromagnetic-field detection technology to identify the bolt loosening condition of a transmission line tower; constructing an early-warning model for bolt loosening in the transmission line tower, and using detection data from real-time acoustic imaging, infrared imaging and electromagnetic-field detection to obtain the bolt loosening condition of the transmission line tower; and finally, performing early warning for bolt loosening in the transmission line tower. By combining multiple detection methods, the present invention improves the efficiency and accuracy of detecting a bolt loosening defect in a transmission line tower, thereby reducing the maintenance costs, and improving the reliability and safety of a power transmission system.

Description

Method for fast non-contact sensing and detection of bolt loosening defects of power transmission line tower TECHNICAL FIELD

[0001] The present application belongs to the technical field of power system maintenance and monitoring, and particularly relates to a method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower. BACKGROUND

[0002] The power transmission line tower is a crucial element in the power system, and its stability is directly related to the safe and reliable operation of the entire power system. As a key part of tower assembly and connection, the fastening state of the bolt is crucial to the structural safety of the tower.

[0003] The tower structure of the power transmission tower is often connected by bolts or welds. Long-term wind and structural vibration may cause loosening or cracking at the connection site. If not timely discovered and treated, it may cause serious accidents and secondary disasters, causing a huge impact on social life and safety. At the same time, key components such as wire clamps and bolts in the power transmission line are also easily disturbed by environmental factors. In particular, in areas with variable weather, strong storms and severe temperature changes can exacerbate the wear and loosening of components, increasing the difficulty of inspection and maintenance. In some remote mountainous areas, due to complex terrain and poor transportation, the inspection work is more difficult, thereby increasing the risk of failure of the power transmission line. At present, single infrared and ultraviolet temperature measurement technology cannot accurately detect the defects of fittings, therefore, it is an important task to quickly and accurately detect bolt loosening defects and timely repair to ensure the safe operation of the power transmission line.

[0004] In the study of detecting bolt loosening, sensing technology and monitoring technology are constantly developing, and non-contact sensing technology is gradually applied to the health monitoring of bolt loosening due to its high efficiency, reliability, and strong real-time performance.

[0005] In the prior art, a method for detecting bolt loosening using computer vision technology first captures images of the bolt using a smartphone, then performs preprocessing operations on the images, including perspective transformation and converting the images to grayscale. Next, edge detection is performed using the Canny algorithm, and Hough transformation is used to extract straight line features in the images. Finally, by analyzing the angle changes of the identified straight lines, it is determined whether the bolt has loosened. This detection method can effectively identify bolt loosening, showing advantages such as economy, efficiency, and easy operation. However, it is strongly dependent on environmental light and background during detection, and in different lighting conditions or complex backgrounds, it may be difficult to obtain clear images, affecting the accuracy of the detection; at the same time, this method determines whether the bolt has loosened by identifying the angle change of the straight line, which requires the bolt edge in the image to be clearly visible. For some partially obscured or unevenly illuminated bolts, this method may not accurately measure the angle change; although edge detection and Hough transformation algorithms are widely used, they may not accurately distinguish between bolts and other objects with similar features in complex scenes, which may lead to false positives or false negatives.

[0006] In the prior art, there is also a bolt loosening detection method that combines deep learning target detection and digital image processing. First, a deep learning network model capable of identifying bolt regions in images is trained by collecting and learning a certain number of bolt image samples. Using this model, the system can accurately locate and crop the bolt-containing regions from the image. Subsequently, a series of digital image processing operations are performed on these cropped bolt regions, including filtering, edge detection, and Hough line transformation, to extract the bolt's marker line and the bolt's fixed surface marker line. By calculating the angle between these two marker lines, the bolt's loosening angle can be further calculated to determine whether the bolt has loosened and the degree of loosening. However, in this method, the performance of the deep learning model usually depends on the quality and quantity of the training data. If the training data set is not large enough or not diverse enough, the model may not accurately recognize new or rare poses, resulting in poor generalization ability; deep learning models are often considered "black boxes" and it is difficult to explain their internal decision-making process. This can lead to trust and transparency issues, especially in safety-critical applications.

[0007] In summary, although the existing technologies are non-contact detection methods, they still face challenges in detection accuracy, environmental adaptability, cost-effectiveness, and other aspects under certain conditions, and there are many uncertainties in practical applications that may lead to deviations. Therefore, how to overcome the shortcomings of existing technologies is a problem that needs to be solved in the field of power system maintenance and monitoring technology. SUMMARY

[0008] The purpose of the present application is to solve the problems of the prior art, and provide a transmission line tower bolt loosening defect fast non-contact sensing detection method, which combines multiple detection methods to improve the detection efficiency and accuracy of transmission line tower bolt loosening defects, thereby reducing maintenance costs and improving the reliability and safety of the power transmission system.

[0009] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0010] The transmission line tower bolt loosening defect fast non-contact sensing detection method comprises the following steps:

[0011] Step (1), a bolt vibration feature dynamic database is constructed, and the bolt loosening condition is detected by using the elastic wave principle;

[0012] Step (2), acoustic imaging technology, infrared imaging technology and electromagnetic field detection technology are used to identify the transmission line tower bolt loosening condition;

[0013] Step (3), using the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection and the corresponding transmission line tower bolt loosening condition, a transmission line tower bolt loosening early warning model is constructed;

[0014] Step (4), using the transmission line tower bolt loosening early warning model and the detection data of real-time acoustic imaging, infrared imaging and electromagnetic field detection, the transmission line tower bolt loosening condition is obtained;

[0015] Step (5), if any result in the detection results of step (1), step (2) and step (4) is bolt loosening, the transmission line tower bolt loosening early warning is carried out.

[0016] Further, preferably, the specific method of step (1) is:

[0017] An accurate model of the tower bolt connection is established in the finite element software, including the bolt, nut, contact surface and surrounding structure;

[0018] Define material properties for each component in the model;

[0019] Mesh the model;

[0020] Set boundary conditions to simulate the actual working environment, and apply external loads or impacts to the model to simulate the operating conditions that may cause bolt loosening;

[0021] Simulate the elastic wave propagation of the bolt after being impacted, and calculate the dynamic response at different time points;

[0022] The reflection and transmission phenomena of impact elastic waves at the bolt connection are focused on, the waveform changes under normal fixation and different loosening states of the bolt are recorded, and bolt vibration characteristic data is extracted to construct a dynamic database of bolt vibration characteristics.

[0023] The impact elastic wave waveform of the bolt connection to be detected is collected in real time, and bolt vibration characteristic data is obtained. The data is compared with the data in the dynamic database of bolt vibration characteristics, so as to obtain the loosening condition of the bolt.

[0024] Further, preferably, the material properties include elastic modulus, Poisson's ratio, and density.

[0025] Further, preferably, the bolt vibration characteristic data includes vibration frequency, amplitude, and waveform.

[0026] Further, preferably, in step (2), the specific method for identifying the loosening condition of the transmission line tower bolt using acoustic imaging technology is:

[0027] The sound waves at the bolt connection of the transmission line tower are captured by a microphone array. If the detected sound waves of the bolt are inconsistent with the sound waves of the normally fixed bolt, it is considered that there may be bolt loosening. Then, the sound wave information is converted into a visual image, so that the operator can intuitively see the possible loosening position and degree.

[0028] Further, preferably, in step (2), the specific method for identifying the loosening condition of the transmission line tower bolt using infrared imaging technology is:

[0029] The thermal image of the bolt connection of the transmission line tower is collected by an infrared thermal imager, and the thermal image is analyzed to obtain the temperature distribution. The temperature distribution is compared with the temperature distribution when the bolt is normally fixed. If there is a significant abnormal hot spot, it is considered that there may be bolt loosening.

[0030] Further, preferably, in step (2), the specific method for identifying the loosening condition of the transmission line tower bolt using electromagnetic field detection technology is:

[0031] The electromagnetic fluctuation at the bolt connection of the transmission line tower is detected using alternating electromagnetic field detection technology, and the electromagnetic fluctuation is compared with the electromagnetic fluctuation when the bolt is normally fixed. If there is a significant change in the electromagnetic field, it is considered that there may be bolt loosening.

[0032] Further, preferably, the specific method of step (3) is:

[0033] The historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection are taken as inputs, and the actual loosening of the transmission line tower bolt is taken as output, and the convolutional neural network is trained, so that the transmission line tower bolt loosening early warning model is obtained; Wherein, the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection are respectively the acoustic wave, thermal image and electromagnetic wave data of the transmission line tower bolt connection.

[0034] Further, preferably, in step (4), when the transmission line tower bolt loosening early warning is performed, the bolt loosening display is performed to prompt the corresponding operator.

[0035] In order to further quickly and accurately detect the signs of transmission tower bolt loosening, the present application combines acoustic imaging technology, infrared temperature measurement technology and electromagnetic field detection technology to jointly detect whether the transmission tower bolt is loose and the degree of loosening. This is conducive to reducing the influence of external environment such as climate change, terrain influence, etc., and improving the intelligent level of transmission line operation and inspection.

[0036] The transmission line tower bolt loosening defect fast non-contact sensing detection method researched by the present application can effectively improve the accuracy of bolt loosening detection, and has strong anti-interference ability in detection, which can significantly improve the stable operation and safety level of power transmission system.

[0037] Compared with the prior art, the present application has the following beneficial effects:

[0038] The present application detects the loosening of transmission tower bolts and line clamp bolts by combining a series of technical measures, significantly improving the accuracy and quality of detection. The specific technical effects are as follows:

[0039] 1. Improve detection speed and accuracy: by constructing a comprehensive bolt vibration feature dynamic database, a reliable reference standard is provided for detection work, significantly improving the speed and accuracy of detection.

[0040] 2. Realize diversified detection strategy: combine acoustic imaging, infrared imaging and electromagnetic field detection three technologies to form a comprehensive detection method, which can remotely and non-contactly quickly identify and locate potential bolt loosening defects.

[0041] 3. Promote the rapidity and effectiveness of operation and maintenance response: use unmanned aerial vehicles to carry related detection equipment for large-scale and rapid monitoring, which greatly reduces the risk of line accidents caused by bolt problems, and ensures the stable operation of power grid and the safety of power supply. BRIEF DESCRIPTION OF DRAWINGS

[0042] Fig. 1 is a flowchart for detecting bolt loosening by impact elastic wave principle;

[0043] Fig. 2 is a beam forming algorithm flowchart.

[0044] Figure 3 is an infrared imaging technology detection process

[0045] Figure 4 is an alternating electromagnetic field detection technology flow chart;

[0046] Figure 5 is a pre-warning flow chart;

[0047] Figure 6 is a whole function architecture diagram. DETAILED DESCRIPTION

[0048] The application will be further described in detail below in conjunction with examples.

[0049] Those skilled in the art will understand that the following examples are only for illustration of the application and should not be considered as limiting the scope of the application. If the specific technology or conditions are not specified in the examples, the technology or conditions described in the literature in the art or according to the product instructions are used. If the manufacturer of the materials or equipment is not specified, it is a conventional product that can be obtained by purchase.

[0050] The power line tower bolt loosening defect fast non-contact sensing detection method comprises the following steps:

[0051] Step (1), constructing a bolt vibration feature dynamic database, and detecting the bolt loosening condition using the impact elastic wave principle;

[0052] Step (2), using acoustic imaging technology, infrared imaging technology and electromagnetic field detection technology to identify the power line tower bolt loosening condition;

[0053] Step (3), using the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection and the corresponding power line tower bolt loosening condition to construct a power line tower bolt loosening pre-warning model;

[0054] Step (4), using the power line tower bolt loosening pre-warning model and using the detection data of real-time acoustic imaging, infrared imaging and electromagnetic field detection to obtain the power line tower bolt loosening condition;

[0055] Step (5), if any result in the detection results of step (1), step (2) and step (4) is bolt loosening, then the power line tower bolt loosening pre-warning is performed.

[0056] The specific method of step (1) is:

[0057] An accurate model of the tower bolt connection is established in the finite element software, including the bolt, nut, contact surface and surrounding structure;

[0058] Material properties are defined for each component in the model;

[0059] The model is meshed;

[0060] Boundary conditions are set to simulate the actual working environment, and external loads or impacts are applied to the model to simulate the operating conditions that may cause bolt loosening;

[0061] The propagation of elastic waves generated by the bolt after impact is simulated, and the dynamic response at different time points is calculated;

[0062] The reflection and transmission of impact elastic waves at the bolt connection are focused on, the waveform changes under normal fixation and different loosening states are recorded, and the bolt vibration characteristic data are extracted to build a dynamic database of bolt vibration characteristics;

[0063] The impact elastic wave waveform at the bolt connection to be detected is collected in real time, and the bolt vibration characteristic data are obtained, which are compared with the data in the dynamic database of bolt vibration characteristics to obtain the loosening condition of the bolt.

[0064] Material properties include elastic modulus, Poisson's ratio and density.

[0065] Bolt vibration characteristic data include vibration frequency, amplitude and waveform.

[0066] In step (2), the specific method for identifying the loosening of the transmission line tower bolt using acoustic imaging technology is:

[0067] A microphone array is used to capture the sound waves at the bolt connection of the transmission line tower. If the detected sound waves of the bolt are inconsistent with the sound waves of the normally fixed bolt, it is considered that there may be bolt loosening. Then, the sound wave information is converted into a visual image, so that the operator can intuitively see the possible loosening position and degree.

[0068] In step (2), the specific method for identifying the loosening of the transmission line tower bolt using infrared imaging technology is:

[0069] An infrared thermal imager is used to collect thermal images at the bolt connection of the transmission line tower, and the thermal images are analyzed to obtain the temperature distribution, which is compared with the temperature distribution when the bolt is normally fixed. If there is a significant abnormal hot spot, it is considered that there may be bolt loosening.

[0070] In step (2), the specific method for identifying the loosening of the transmission line tower bolt using electromagnetic field detection technology is:

[0071] An alternating electromagnetic field detection technology is used to detect the electromagnetic fluctuation at the bolt connection of the transmission line tower, and the electromagnetic fluctuation is compared with the electromagnetic fluctuation when the bolt is normally fixed. If there is a significant change in the electromagnetic field, it is considered that there may be bolt loosening.

[0072] The specific method of step (3) is:

[0073] The historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection are taken as inputs, and the actual loosening of the corresponding transmission line tower bolt is taken as output, and the convolutional neural network is trained, so that the transmission line tower bolt loosening early warning model is obtained; wherein the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection are respectively the acoustic wave, thermal image and electromagnetic wave data of the transmission line tower bolt connection.

[0074] In step (4), when the transmission line tower bolt loosening early warning is performed, the display of the bolt loosening is performed to prompt the corresponding operator.

[0075] The present application studies the detection method according to the characteristics of the transmission tower bolt loosening.

[0076] (1) When the transmission line tower bolt is loosened, the inherent frequency of the connected tower material will change, thereby affecting the vibration characteristics of the whole structure;

[0077] (2) Bolt loosening may also cause the local contact resistance of the transmission tower to increase, and heat will be generated when the current passes through, which will cause the temperature of the area to rise;

[0078] (3) When the transmission line is in normal operation, a stable electromagnetic field distribution will be generated around it, and when the bolt is loosened, the position of the wire may change, thereby affecting the distribution of the local electromagnetic field.

[0079] According to these characteristics, first, the vibration frequency and characteristics of the transmission tower bolt are studied, the purpose is to identify and distinguish the vibration frequency and characteristic mode of the bolt in the normal fixed state and various loosening states; second, the vibration frequency characteristic data of various hanger types and specifications in different states are integrated to construct a database, which is used as a reference standard for detection, which helps to improve the accuracy and efficiency of detection; then, combined with acoustic imaging, infrared imaging and electromagnetic field detection technology, a comprehensive detection strategy is proposed, which can remotely and non-contactly quickly identify and locate potential bolt loosening defects. Finally, the related detection equipment is carried by the unmanned aerial vehicle to detect the transmission tower bolt, and the detection data obtained by the detection is fused to obtain the detection result, realizing the fast non-contact sensing detection of the transmission line tower bolt loosening defect.

[0080] (1) In-depth study of the vibration characteristics of fittings, such as bolts and clamps, to ensure the stability and safety of the power transmission system. The main goal is to identify the vibration frequencies and modes of fittings under normal fixation and different loosening states. Using finite element software for simulation tests, transient dynamics analysis of bolt connections before and after complete loosening is conducted, involving the use of impact elastic wave principles to detect bolt loosening, and analyzing the response of bolt connections under various load conditions to assess their performance under different conditions.

[0081] The impact elastic wave principle is that when an object is subjected to an impact, stress waves (elastic waves) are generated, which will propagate within the material. When these waves encounter an interface, such as a bolt connection, they will undergo reflection and transmission processes. By analyzing the characteristics of these reflected waves, the condition of the interface, such as the presence of loosening, can be determined. The implementation process of detecting bolt loosening using the impact elastic wave principle is shown in Figure 1.

[0082] Through these simulations, changes in the vibration characteristics of the structure when the bolts are loosened can be predicted, providing data support for establishing a database and important frequency domain information for acoustic imaging.

[0083] (2) Based on the bolt loosening characteristics obtained from simulation tests, combined with the collected field bolt vibration data, a comprehensive, accurate, and reliable dynamic database of bolt vibration characteristics is constructed, providing solid foundation data for subsequent detection. At the same time, the database is continuously updated and expanded through data obtained from later detection, ensuring that it covers new types of fittings and loosening modes to adapt to changing detection needs. In the integrated database, the vibration frequency range of some power line fittings is shown in Table 1. If the detected vibration frequency exceeds the highest frequency value, it is considered that there is loosening.

[0084] Table 1 Vibration frequency range of power line fittings

[0085] According to this database, reliable and continuously updated reference data can be provided for subsequent detection work, thereby significantly improving the accuracy and efficiency of detection. In order to ensure the accuracy, reliability, and convenient queryability of information, while ensuring the safe preservation and rapid access of information, it is necessary to periodically perform necessary maintenance and expansion of the database to ensure that detection needs are met and detection accuracy is improved.

[0086] (3) To achieve rapid identification of bolt loosening defects in transmission line towers, acoustic imaging, infrared imaging, and electromagnetic field detection technologies need to be combined. Using these technologies, the acoustic information of fittings can be captured from a distance, small temperature changes can be detected, and abnormal fluctuations in the electromagnetic field can be monitored, thereby discovering potential fault points. By using unmanned aerial vehicles to carry detection equipment and using machine learning algorithms to compare the collected data with a feature database, the health status of fittings and towers can be evaluated, early warning and fault diagnosis can be achieved.

[0087] The specific detection of acoustic imaging, infrared imaging, and electromagnetic field detection technologies is as follows:

[0088] ① Acoustic imaging technology is used to detect bolt loosening defects in transmission line towers. Acoustic imaging technology is a technology that uses sound waves to capture and convert sound information into visual images. It usually uses a microphone array to receive sound from a specific object or environment from a distance. In the application scenario of detecting bolt loosening defects in transmission line towers, when the bolt begins to loosen, it will produce specific sound waves that can be captured by the microphone array. Subsequently, through image processing technology, these sound wave information is converted into visual images, allowing the operator to visually see the possible loosening position and degree.

[0089] Among them, the beamforming algorithm is used for signal processing, through which the phase and amplitude of the signals from each microphone in the microphone array are adjusted and optimized, which can enhance the sound source signal in a specific direction while suppressing noise and interference in other directions. The flowchart of the beamforming algorithm is shown in Figure 2.

[0090] ② Infrared imaging technology is used to detect bolt loosening defects in transmission line towers. When the bolts in the transmission line are loose, it will cause a change in local resistance, resulting in a temperature difference from the surrounding environment. This temperature difference can be effectively detected by infrared thermal imaging technology, which is based on the relationship between the temperature of an object and its infrared radiation. Any object with a temperature greater than absolute zero will emit infrared radiation. Therefore, by capturing the infrared radiation emitted by the surface of an object, the temperature distribution of the object can be detected.

[0091] During detection, the thermal imager should be fixed steadily to avoid any shaking caused by environmental factors such as wind, thereby ensuring the clarity of the image and the accuracy of the data; then data collection is performed, this stage is susceptible to environmental factors such as temperature, humidity, and wind speed, when analyzing the data, these external factors that may affect the temperature measurement results need to be considered; next, the thermal imaging technology generates a thermal image, which can clearly show the heat distribution. The red area usually represents the high temperature area, which means that the bolt there may be loose or have other problems. The detection process is shown in Figure 3.

[0092] ③Using electromagnetic field detection technology to detect the bolt loosening defect of the transmission line tower. Considering that bolt loosening will affect the distribution of the surrounding electromagnetic field, the AC electromagnetic field detection technology is used to detect the area around the fitting, monitor abnormal electromagnetic fluctuations, and capture the electromagnetic field changes caused by bolt loosening. The AC electromagnetic field detection technology generates an alternating current in the conductor, thereby generating a changing electromagnetic field in the surrounding space. When the conductor or its surrounding bolts have defects, the distribution of the magnetic field will be affected. The detection implementation process is shown in FIG. 4.

[0093] The acoustic imaging technology, infrared imaging technology and electromagnetic field detection technology are combined to fully utilize their respective unique advantages and jointly detect the bolt loosening defect of the transmission tower. At this time, early warning technology is needed to realize the integration of detection technology, which is realized by using convolution neural algorithm. First, the data detected in three different aspects need to be data fused and preprocessed, which not only increases the detection dimension, but also improves the accuracy and reliability of the whole monitoring system; then the convolution neural network is designed, which needs to be adjusted and optimized according to the specific problem and data set; then the model is trained, combined with the bolt vibration feature dynamic database to enhance the training data and improve the model accuracy; finally, the trained model is used for detection and recognition, and the newly obtained data and analysis results are fed back to the bolt vibration feature database. When bolt loosening problems or potential problems are detected, there will be a warning display, and finally the bolt loosening defect of the transmission line tower is detected efficiently. The warning display implementation process is shown in FIG. 5.

[0094] Combined with the above technical research points, the use of unmanned aerial vehicles carrying sensors and other equipment can realize the complete and rapid detection of the bolt loosening defect of the transmission line tower. First, the vibration frequency and mode of the transmission tower bolt in different states are analyzed in depth to distinguish its normal fixation and loosening state. Then, the vibration data of various fittings in various states are integrated to construct a comprehensive database, which provides a reference standard for detection work and significantly improves the detection speed and accuracy. In addition, this technology integrates acoustic imaging, infrared imaging and electromagnetic field detection methods to form a diversified detection strategy. Using unmanned aerial vehicles to carry these devices can efficiently cover a wide area and achieve rapid and extensive monitoring. The system can also automatically adjust the detection parameters to adapt to different environments and conditions, enhancing the flexibility and adaptability of the detection technology. The overall function implementation architecture of the invention is shown in FIG. 6.

[0095] The use of the technical research of the invention can timely discover and handle bolt loosening defects, greatly reduce the risk of line accidents caused by bolt problems, and thus ensure the stable operation of the power grid and the safety of power supply. Combined with multiple detection technologies and database support, the detection rate and accuracy are improved, making the operation and maintenance response more rapid and effective.

[0096] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower, characterized in that, The method comprises the following steps: Step (1), constructing a bolt vibration feature dynamic database, and detecting the bolt loosening condition by using the elastic wave principle of impact; Step (2), identifying the bolt loosening condition of the transmission line tower by using acoustic imaging technology, infrared imaging technology and electromagnetic field detection technology; Step (3), constructing a transmission line tower bolt loosening early warning model by using the historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection and the corresponding bolt loosening condition of the transmission line tower; Step (4), obtaining the bolt loosening condition of the transmission line tower by using the transmission line tower bolt loosening early warning model and the detection data of real-time acoustic imaging, infrared imaging and electromagnetic field detection; Step (5), if any result of the detection results of step (1), step (2) and step (4) is bolt loosening, the transmission line tower bolt loosening early warning is carried out.

2. The method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower according to claim 1, characterized in that, The specific method of step (1) is: An accurate model of the tower bolt connection is established in the finite element software, including the bolt, nut, contact surface and surrounding structure; Material properties are defined for each component in the model; The model is meshed; Boundary conditions are set to simulate the actual working environment, and external loads or impacts are applied to the model to simulate the operating conditions that may cause bolt loosening; The propagation of elastic waves generated by the bolt after impact is simulated, and the dynamic response at different time points is calculated; The reflection and transmission of the impact elastic wave at the bolt connection are focused on, the waveform changes under normal fixation and different loosening states are recorded, and the bolt vibration feature data are extracted to construct a bolt vibration feature dynamic database; The impact elastic wave waveform at the bolt connection to be detected is collected in real time, and the bolt vibration feature data are obtained, which are compared with the data in the bolt vibration feature dynamic database, so that the loosening condition of the bolt is obtained.

3. The method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower according to claim 2, characterized in that, The material properties include elastic modulus, Poisson's ratio and density.

4. The method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower according to claim 2, characterized in that, The bolt vibration feature data include vibration frequency, amplitude and waveform.

5. The method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower according to claim 1, characterized in that, In step (2), the specific method of identifying the bolt loosening condition of the transmission line tower by using acoustic imaging technology is: A microphone array is used to capture the sound waves at the bolt connection of the transmission line tower, and if the detected sound waves of the bolt are inconsistent with the sound waves of the normally fixed bolt, it is considered that there may be bolt loosening; then the sound wave information is converted into visual images, so that the operator can intuitively see the possible loosening position and degree.

6. The method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower according to claim 1, characterized in that, In step (2), the specific method of identifying the bolt loosening condition of the transmission line tower by using infrared imaging technology is: An infrared thermal imager is used to collect thermal images at the bolt connection of the transmission line tower, and the thermal images are analyzed to obtain the temperature distribution, which is compared with the temperature distribution when the bolt is normally fixed, and if there is an obvious abnormal hot spot, it is considered that there may be bolt loosening.

7. The method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower according to claim 1, characterized in that, In step (2), the specific method of identifying the bolt loosening condition of the transmission line tower by using electromagnetic field detection technology is: The alternating electromagnetic field detection technology is used to detect the electromagnetic fluctuation of the bolt connection of the power transmission line tower, and the electromagnetic fluctuation is compared with the electromagnetic fluctuation when the bolt is normally fixed. If there is an obvious change in the electromagnetic field, it is considered that the bolt may be loose.

8. The method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower according to claim 1, characterized in that, The specific method of step (3) is: The historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection are taken as inputs, and the actual loosening condition of the corresponding power transmission line tower bolt is taken as output. The convolutional neural network is trained to obtain a power transmission line tower bolt loosening early warning model. The historical detection data of acoustic imaging, infrared imaging and electromagnetic field detection are respectively the acoustic wave, thermal image and electromagnetic fluctuation data of the bolt connection of the power transmission line tower.

9. The method for fast non-contact sensing and detection of bolt loosening defects of a power transmission line tower according to claim 1, characterized in that, In step (4), when the power transmission line tower bolt loosening is warned, the bolt loosening display is performed to prompt the corresponding operator.

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