No abstract

In view of the settlement problem of transmission tower foundation, the vibration characteristics of transmission towers under wind force are measured experimentally. In this paper, the 110 kV cat head transmission tower of Xi’an Polytechnic University is measured and analyzed. Firstly, the acceleration sensor and meteorological sensor are installed on the tower to collect the vibration response and environment parameters of the tower in real time. Then, an experiment platform is built to simulate the tower settlement, and the vibration response of the tower after settlement is measured in time. Finally, the low-order modal frequencies of the transmission tower before and after settlement under wind force load are extracted by stochastic subspace identification (SSI), and the relationship between modal frequencies of different modes is analyzed via temperature correction. By comparison and analysis, it is obvious that the X-direction modal frequencies before and after settlement under natural wind load are changed, and the change rate increases with the increase of settlement displacement, which can be used as effective evidence for judging the settlement of transmission tower foundation.

In the production and manufacturing industry, factors such as rolling equipment and processes may cause various defects on the surface of the steel plate, which greatly affect the performance and subsequent machining accuracy. Therefore, it is essential to identify defects in time and improve the quality of production. An intelligent detection system was constructed, and some improved algorithms such as dataset enhancement, annotation and lightweight convolution neural network are proposed in this paper. (1) Compared with the original YOLOV5 (You Only Look Once), the precision is 0.924, and the inference time is 29.8 ms, which is 13.8 ms faster than the original model. Additionally, the parameters and calculations are also far less than YOLOV5. (2) Ablation experiments were designed to verify the effectiveness of the proposed algorithms. The overall accuracy was improved by 0.062; meanwhile, the inference time was reduced by 21.7 ms. (3) Compared with other detection models, although RetinaNet has the highest accuracy, it takes the longest time. The overall performance of the proposed method is better than other methods. This research can better meet the requirements of the industry for precision and real-time performance. It can also provide ideas for industrial detection and lay the foundation for industrial automation.

Finding out the wind deviation fault of the insulators of transmission lines in time can not only help people better complete the wind bias warning and prevention work, but also reduce the economic loss and the maintenance difficulty. A technology of Actual Wind Deviation Monitoring for Suspension Insulator Strings Based on Improved Edge Detection is proposed in this paper. It can calculate the actual wind deviation angle and distance of the insulator through image processing technology and camera calibration. Firstly, the video monitoring device is installed on the tower to collect the field insulator images, and the R + G improved grayscale processing and median filtering are carried out. Secondly, two-dimensional Otsu threshold segmentation is applied to the pre-processed image, and the insulator string target is obtained by combining morphological filtering and connection domain extraction. Then, the improved Kirsch operator is used to obtain the complete single-pixel wide edge of the insulator string. Searching the left and right boundary points of each insulator separately and the center points of each insulator can be calculated. Finally, these center points are transformed into world coordinates by camera calibration and fitted by least square method (LSM). The deviation angle θ of the insulator string can be obtained according to the slope of the fitting line, and the deviation distance d according to the length of the insulator string. When θ is greater than 7.5 • and d is greater than 300mm, an alarm is issued to alert the staff. We analyze the performance of the technology by a series of experiments, the maximum error between the proposed method and the manual method is 8.72% and the minimum is 1.01%. Moreover, by analyzing 200 field insulator images, the identification accuracy are 93.75% and 91.36% respectively, which shows that the proposed method is effective and practical. It provides a new idea for the wind deviation monitoring of suspension insulator strings. INDEX TERMS Insulator wind deviation, improved gray processing, improved Kirsch operator, camera calibration, deviation angle and distance.

In the casting process of the steel plate, due to the influence of rolling equipment and technology, the defects such as cracks and scratches appear on the surface of steel plate, which affect the performance of steel plate and even cause production accidents. In this paper, an automatic detection method for steel plate scratch is proposed. Firstly, the steel plate image is decomposed by channel and the enhanced image is obtained by the improved MSR (Multi-Scale Retinex) enhancement algorithm. Then, the phase consistency is detected after the Log Gabor wavelet transform and the scratch areas are obtained by the threshold segmentation and intersection of three channels. Finally, the scratch position is identified and the scratch characteristics such as width and length can be calculated. The results show that the minimum error of the characteristics measurement is only 2.28% in the experimental environment and 4.15% in the field environment, and the mean running time is 0.2826 s in the experimental environment and 0.3193 s in the field environment. It verifies that the proposed method is effective and practical.