Research And Implementation Of On-line Monitoring System For Water Supply Critical Equipment Based On Internet Of Things

In recent years,China's urban population has exploded,leading to a sharp increase in urban water supply pressure.In order to meet the requirement of the super-large water supply,many water equipment must be operated at full power all the time,which increases the failure rate of these water supply equipment.At present,some water supply enterprises have used sensors such as temperature and pressure sensors to collect equipment running real-time data for real-time monitoring of equipment running state.However,the existing real-time monitoring system lacks the ability to predict the running state of equipment.Usually,when the exception state of equipment is detected,the equipment failure has also occurred,which has an impact on the normal water supply of the water supply enterprise.As one of the key parameters of rotating machinery,vibration data can reflect the running state of equipment comprehensively.This thesis researches and implements an on-line monitoring system for water supply critical equipment based on Internet of things.The vibration sensor is installed on different parts of the water supply critical equipment to collect the root mean square value of the vibration speed-vibration intensity and upload to the server,processing and analysis of the vibration intensity,predict running state of water supply critical equipment in the future for a period of time.The main work of this thesis is as follows:1.Acquisition of vibration intensity data.In this thesis,a device based on LoRa technology is developed for acquiring and transmitting vibration intensity data.The vibration sensor sends the collected data through LoRa nodes to LoRa gateway,which uses mobile Internet to transmit the data to the data server for storage.This set of equipment also has the characteristics of low cost,large amount of access,long transmission distance,etc.,which can meet the requirements of data collection in this thesis.2.Prediction of vibration intensity and equipment running state.Based on the cosine function particle swarm optimization algorithm,the value of learning factor and inertial weight strategy was improved and a grouping optimization strategy was proposed which is used for optimization of support vector machine forecasting model.Then,the improved particle swarm algorithm-support vector machine forecasting model was used to forecast the vibration intensity from drive end bearing,pump casing and inlet flange three parts of the critical equipment.Finally,according to methods of measuring and evaluating vibration of pumps(GB/T 29531-2013)and the artificial experience,the running state of equipment in the future for a period of time is predicted.3.Design and implementation of on-line monitoring system.The vibration intensity data prediction and equipment state prediction are integrated into the system,and then the system is improved according to the needs of water supply enterprise.Finally,an on-line monitoring system for water supply critical equipment integrating monitoring,prediction and management is formed.In this thesis,the on-line monitoring system for water supply critical equipment was developed.The system can not only monitor the trend of data in real time,but also predict the data and equipment running state.It can basically avoid the sudden failure of equipment and thus improve the production efficiency of water supply enterprises.