Breakdown Properties of Cables with Different Inorganic, Insulating Nanomaterials

The insulation performance of cable insulating materials can be optimised via matrix modification. Typically, low-density polyethylene (LDPE) is used as the matrix, and a certain proportion of nanoparticles are added to this matrix. To explore the effects of nanoparticles with different forms on the structural interface and crystal morphology of the material, nano-MMT and nano-ZnO were added to LDPE, and comparative experiments were carried out. Based on microscopic test results, material insulation performance changes before and after optimisation were observed. Then, simulation cable models with different insulating materials were developed. Based on the simulated electrical measurements, the thermal breakdown performance of the different insulating materials was tested. According to infrared stereo vision detection results, anomalous temperature points in the cables can be located accurately. Finally, based on macroscopic test results, we verified whether the inorganic, insulating nanomaterials meet the requirements for high-voltage transmission.

Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation

Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents.

Research on Visual Detection Algorithm of 500kV High Voltage Live Operating Robot

The 500kV transmission line is exposed to the outdoor for a long time. It is affected by complex climate charge change and other factors which leads to line connection parts bolt loosening, wire breaking and fixture damage and other line failures. In order to ensure the stable transmission of electric energy, power operators need to wear shielding suits and work in the high-risk and high-voltage environment. Use of electric power operation robot instead of manual operation is an effective way to liberate maintenance staff labor. But robots still have some problems such as low degree of automation and low efficiency of power operation. Machine vision detection technology in recent years has been widely used in major areas including deep learning as emerging visual detection technology shows excellent performance. In this paper, the vision detection algorithm is studied respectively for the bolt fastening end working device and wire repairing end working device of the live transmission line robot to improve the operating efficiency of the robot.

Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Software Design

The removal of plastic contamination from cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales is plastic used to wrap cotton modules produced by John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during module unwrapping, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin, a machine-vision detection and removal system was developed that utilizes low-cost color cameras to see plastic coming down the gin-stand feeder apron, which upon detection, blows plastic out of the cotton stream to prevent contamination. This paper presents the software design of this inspection and removal system. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. The focus of this report is to describe the software design and discuss relevant issues that influenced the design of the software.