Research On Remote Monitoring And Fault Diagnosis System Of Loom Based On Cloud Platform

With the introduction of the "German Industry 4.0" and "China Intelligent Manufacturing 2025" strategy,enterprises have put forward higher requirements for production efficiency and product quality.Improving the intelligent level of traditional manufacturing has become the trend of the times,workshop equipment interconnection and workshops.Networking is one of the prerequisites for smart manufacturing.The remote monitoring and fault diagnosis system of the loom is of great significance for realizing the interconnection of workshop equipment,the networking function of the workshop and the improvement of the intelligent fault diagnosis level of the loom.In the research of remote monitoring and fault diagnosis system of loom,there is still a big gap with foreign countries,mainly in the absence of remote control,limited user access,lack of fault diagnosis function,and lack of unified management system.In view of the above deficiencies,this paper designs a set of remote monitoring and fault diagnosis system for loom based on cloud platform.First of all,the overall design of the system.Analyze the functional requirements of the loom remote monitoring and fault diagnosis system,and design the overall design of the loom remote monitoring and fault diagnosis system.The B/S architecture is chosen as the development architecture for communication between the remote client and the server,and the functions of each module in the solution and the communication implementation between the modules are explained.Secondly,the construction of fault diagnosis model.The rough set theory and Bayesian network theory are applied to the loom fault diagnosis system.The loom fault diagnosis model based on rough set theory and Bayesian network is constructed to improve the efficiency and accuracy of loom fault diagnosis.A large number of incomplete loom fault data are collected as samples.The diagnosis model is trained and simulated by Weka software.The simulation experiments with several other artificial intelligence fault diagnosis methods prove that the model is feasible and accurate when the data is incomplete.Third,the system is built.The ESP8266 wireless module is used as the client of TCP/IP communication(TCPCli);the Redis database and the Nginx server are deployed on the Alibaba Cloud server as the TCP/IP communication server(TCPServer).Design the function modules in the cloud server,including data transmission and reception,format conversion,save,extract call,fault diagnosis,etc.;use HTML5+CSS3+Java Script to design a simple,easy-to-operate and stable client interface;Ajax technology enables data interaction between the Nginx server and the remote client.Finally,the remote monitoring and fault diagnosis function of the loom is realized.Finally,the system tests.Build a remote monitoring and fault diagnosis system platform for looms in the laboratory and industrial sites,and test the system;test system real-time monitoring,management functions,fault diagnosis performance and client compatibility with different browsers,at the industrial site Verification of data real-time,consistency and accuracy of fault diagnosis.