Research On Detection System Of Belt Conveyor

Belt conveyor is the key equipment for bulk material transportation,ensuring its stability and safety in operation is very important.In order to meet the demand for material transportation,belt conveyor evolves with features of long distances,large volumes,high speeds and high reliability.Under this trend,inspection robots are used to perform full inspections on belt conveyors,which not only improves the efficiency and scope of detection,but also eliminates the dependence of traditional methods on the density of sensor arrangement,thereby achieving the purpose of reducing the detection cost and the difficulty of maintenance.Based on the wireless power supply rail-type inspection robot independently developed by the laboratory,the detection system for belt conveyors is researched in this thesis.The main contents of the research are as follows:(1)Build a distributed detection system.The required functions of the detection system are analyzed and formulated,and a distributed detection system including a downhole detection platform and an upper computer is proposed,which is meant to achieve real-time and accuracy.According to the processing target of the downhole detection platform,the ARM+FPGA architecture is selected as the computing unit.The co-design of software and hardware is applied in the ARM+FPGA computing architecture.The ARM processor is used to establish Gigabit Ethernet communication with the upper computer,and the collection and process of environmental parameters are completed;In the FPGA,the data path and processing module are constructed with function of collection,interpretation,processing and writing for image data.(2)Design the deviation diagnosis algorithm of conveyor belt.the reasons for the deviation of the conveyor belt and the diagnosis method are analyzed and summarized.Because the traditional vision technology uses software to diagnose the fault of the conveyor belt deviation,there are some issues concerning the mutual restriction between the processing speed and the diagnosis accuracy.To deal with the issues above,this paper proposes a segmented conveyor belt deviation diagnosis method,and the algorithm is divided according to the processing characteristics of the downhole detection platform and the upper computer.The conveyor belt deviation diagnosis method and FPGA parallel computing technology are researched,and a line segment detection IP core suitable for the FPGA computing platform is constructed based on the LSD algorithm.Further more,the upper computer MATLAB software is used to design the deviation diagnosis method based on the extracted line segment information.(3)Verify the detection system through experiments.According to the different working conditions of the belt conveyor,the established functions of the detection system are verified through experiments,including the real-time detection of environmental parameters,image collection of belt conveyor operation,and the real-time calculation of the deviation of the conveyor belt.The processing delay of the system is analyzed,and the processing frame rate of the detection system is obtained when images of different formats are uploaded,so as to verify the real-time performance and effectiveness of the detection system.This thesis combines machine vision technology and FPGA high-performance computing methods.On the basis of ensuring the intuitiveness and accuracy of the visual detection method,the proposed method improves the flexibility of the inspection system and the real-time performance of the calculation method of the conveyor belt deviation.