In many structures, most of the destruction and damage are caused by torsion. At the same time, in many cases, the torsion effect plays a controlling role in architectural design. Therefore, the monitoring of structural torsion has become more and more important, but there is less research at present. Based on machine vision technology, this paper proposes a monitoring method for the torsional deformation of building structure. Firstly, the theoretical basis of the scheme and the composition of the monitoring system are introduced. Then the feasibility of the scheme is proved through free rotation and elastic torsion experiments. It is verified that the scheme has the advantages of high precision and low cost, and can realize the real-time monitoring of structural torsional deformation, It can avoid the large deformation of the structure due to torsion, affect the use and even damage, and make a certain contribution to the safety evaluation and health evaluation of the structure.
The large-span transmission tower line system is an important lifeline power engineering facility, because of long-term exposure to the natural environment, it needs to face a variety of different complex environment, especially some complex environment, such as traffic, vehicles influence and lush vegetation influence and so on. Operation maintenance work is an important part for the structural health assessment of transmission tower. The routine management and maintenance work mainly relies on engineers and technicians with practical experience to carry out visual inspection and fill in the questionnaire. However, human based visual inspection is an arduous and time-consuming task, and its detection results largely depend on the subjective judgment of human inspectors, as the same time the workers working at height are very dangerous. For environmental changes such as personnel, vehicles and illegal planting, some transmission towers are in remote locations, and the staff cannot find them in time. Aiming at the deficiency of artificial vision detection method, the research on the environmental perception technology of transmission tower based on deep learning is proposed. A large amount of data collected is trained, verified and tested with deep learning algorithm. In order to solve the problem of transmission towers exposed to complex environmental influence, an appropriate model was established based on deep learning algorithm, and the image was used to verify and test. The trained model was tested on some new images that were not used in the training and verification process. Experimental results show that this method can accurately identify the complex environmental objects.
Strain is an important parameter reflecting structural state, which is particularly important in the field of structural health monitoring (SHM). It can be used to evaluate the mechanical properties, failure behavior, crack development and residual stress of structural members and materials. It is particularly important in the field of structural health monitoring (SHM). In this paper, a new type of large gauge strain sensor is proposed. The proposed sensor is based on micro vision and uses a camera to capture small displacement. The packaging structure of strain sensor with sensing gauge length of about 50 cm is designed. In order to realize the monitoring in the field environment, the microscopic images in the experiment are obtained by webcam, which has broad application prospects. In the field of view of the camera, the maximum distance that the probe in the sensor can move is certain, but when the sensing gauge distance increases, the accuracy of strain measurement will be improved. The strain data obtained by the sensor is compared with the data obtained by FBG sensors to verify the measurement accuracy. It is found that the measurement accuracy is suitable for SHM of infrastructure.
Operation maintenance work is an important part for the structural health assessment of transmission tower. The routine management and maintenance work mainly relies on engineers and technicians with practical experience to carry out visual inspection and fill in the questionnaire. However, human based visual inspection is an arduous and time-consuming task, and its detection results largely depend on the subjective judgment of human inspectors, as the same time the workers working at height are very dangerous. Aiming at the deficiency of artificial vision detection method, a detection method of transmission tower component recognition based on image recognition is proposed. UAV is used to detect the transmission tower in an all-round way, Thousands of images are used to train, verify and test the convolutional neural network (CNN) classifier based on Alexanet. Aiming at the problem of damage identification of transmission tower components, fast R-cnn based on improved ZF network is trained, verified and tested by using images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results show that the method can accurately identify the components and damage of transmission tower.
In recent years, the nonnegligible corrosion of steel strand for overhead ground wires has also been detected during operation. It is of great significance to efficiently monitor the corrosion of the steel strand in real-time. In this paper, a new kind of tensile corrosion sensor based on fiber Bragg grating sensing technique was proposed and developed, which can accurately monitor the corrosion of the steel strand in real-time. The performance of the corrosion sensor was studied by an electrochemical corrosion accelerated experiment. The experimental results show that the corrosion sensor can monitor slight and moderate corrosion of the steel strand accurately and effectively, therefore the corrosion sensor is practicable and has broad application prospects in transmission line engineering.
Transmission tower as a part of the power system, plays a significant role in supporting conductor, ground wire and other accessories in the overhead transmission line. It is very crucial to make sure it runs safely and stably for people's production and life. Due to the high flexibility of the transmission tower structure, the wind load is an important control load and plays a decisive role. Under the cyclic stress caused by wind load for a long time, some minor damages and defects of the tower can easily reach the limit of fatigue and cause local or overall damage. In this research work, one structural health monitoring system using wireless inclinometers is proposed. And then the proposed monitoring system was installed on the transmission tower for remote real-time on-line monitoring. The real inclination response of the structures under environmental load and operational load can be monitored in real time. The inclination response monitored was used to evaluate the structural health conditions of the transmission tower. This kind of monitoring scheme has a good practical application value and can effectively avoid the occurrence of major power accidents. Transmission tower as a part of the power system, plays a significant role in supporting conductor, ground wire and other accessories in the overhead transmission line. It is very crucial to make sure it runs safely and stably for people's production and life. Due to the high flexibility of the transmission tower structure, the wind load is an important control load and plays a decisive role. Under the cyclic stress caused by wind load for a long time, some minor damages and defects of the tower can easily reach the limit of fatigue and cause local or overall damage. In this research work, one structural health monitoring system using wireless inclinometers is proposed. And then the proposed monitoring system was installed on the transmission tower for remote real-time on-line monitoring. The real inclination response of the structures under environmental load and operational load can be monitored in real time. The inclination response monitored was used to evaluate the structural health conditions of the transmission tower. This kind of monitoring scheme has a good practical application value and can effectively avoid the occurrence of major power accidents.
Fiber Bragg grating (FBG) sensors are obviously attractive for many prominent advantages, such as strong antielectromagnetic interference ability, long transmission distance, and fast transmission speed. Due to these advantages, FBG sensors can be used for the health monitoring of high-voltage transmission lines. The FBG sensor can not only ensure the security of the transmission lines, but can realize real-time monitoring, which can effectively prevent serious accidents caused by rust breakage of overhead steel strand. In this paper, a new type of corrosion sensor based on FBG was developed to monitor and evaluate the degree of corrosion damage of the steel strand. To verify the performance of the FBG corrosion sensors, an electrochemical corrosion accelerated experiment was conducted on an actual steel strand. The experimental results show that the FBG corrosion sensors have a large measurement range of steel strand corrosion rate, and can monitor steel strand corrosion expansion damage in early stage effectively. The FBG corrosion sensor has practical application value in the field of electricity transmission engineering.
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