Equipment Detecting Technology Based On Distributed Graphene Flexible Sensors

With the improvement of the performance requirements of modern industry for mechanical equipment,the complexity,refinement,intelligence and automation are the development trends of modern machinery and equipment.At the same time,higher requirements have also been put forward for fault detection of equipment.More and more researchers are beginning to study better fault diagnosis techniques for mechanical equipment.In many mechanical devices,the roller group is a common component.Once the roller group fails,the pressure between the roller groups is not uniform,which has a significant impact on the product's qualification rate.Therefore,it is of great significance to study the failure detection technology of the roller group to improve the yield and pass rate.This article summarizes the previous research experience,uses the distributed graphene flexible sensors to detect the failure of the roller group,the main work is as follows:First,we analyze the research background and significance of equipment fault detection,and describe the development of distributed sensor networks,then analyze the research progress of equipment fault diagnosis technology in other countries and the progress of domestic research.This paper introduces some fault diagnosis methods that are often used in equipment fault diagnosis technology,and analyzes the characteristics and applicable scenarios of these methods.Secondly,the research status of distributed pressure sensors at home and abroad is introduced.According to the characteristics of mechanical fault detection,a distributed flexible pressure sensor based on graphene material is designed,and pressure calibration test and pressure distribution test are performed on the device,to ensure the reliability of the collection of data processing.Thirdly,the design of distributed flexible pressure sensor and roller set fault data acquisition system is introduced,including hardware design and software design.In the hardware design,the schematic diagram is designed to solve the problem of the coupling of row and column signals of distributed pressure sensors.The software design includes the programming of acquisition system of the lower computer,the design of the data receiving processing program and the data communication protocol rules of the upper computer.Fourthly,the principle of classifying by BP neural network and probabilistic neural network algorithm is introduced,and the fault monitoring models of the roller group are designed using these two algorithms,then the algorithm is implemented and comparedThe results show that the fault recognition rate of the wheel set based on the probabilistic neural network diagnosis algorithm is as high as 98.64%,and the fault detection algorithm based on probabilistic neural network has a great advantage in the time of creating and training the network.The fault diagnosis algorithm based on BP neural network is only 96.52%,and the convergence speed is relatively slow.Therefore,under the condition of real-time performance,fault diagnosis system based on probabilistic neural network diagnosis algorithm and distributed graphene flexible pressure sensor is an excellent equipment detection technology.