Research On Remote Monitoring Platform And Fault Analysis Of Combine Harvester

The real-time acquisition of the combine harvester information flow can timely monitor the working status of the combine harvester,improve its operation quality and production efficiency,and lay a solid foundation for the automation and intelligent development of the combine harvester equipment.This paper reviews the domestic and international research status of combine harvester operation monitoring and fault diagnosis.Based on this,the overall design scheme of remote monitoring platform based on B/S architecture is given.The monitoring platform database service server is completed,based on the web.The server's system function access interface and the design of the communication server based on the netty network programming framework.Basic information management,equipment information management and equipment monitoring are implemented.In response to problems such as abnormal or missing original data,the pre-processing rules for monitoring data were developed and related data pre-processing procedures were developed.Aiming at the problem of low diagnostic accuracy of the current threshing drum clogging fault,the softmax classification model of the clogging fault is constructed.The model input is the ratio of the feed auger to the fan speed ratio,and the ratio of the speed of the drum to the fan speed.The output is The state of the threshing drum is severely blocked,moderately blocked,slightly blocked,and normal.The model construction and testing were completed based on tensorflow,and the accuracy of the classification model was 95%.