Research Of Beam Pumping Unit Condition Monitoring And Fault Diagnosis System Based On ZigBee

Automatic digital management of oilfield digitization is the future development trend ofoilfield. The effective ways to realize the automation of management of oilfield is to improveproduction efficiency. As with beam pumping unit study of this paper, the pumping unitaround the clock non-stop work, so its working state normal or not is directly impacts onproduction safety and economic benefits. it also takes building data acquisition network andfault diagnosis method of pumping unit of oil field pumping unit working state monitoringsystem for the study of the visual angle, by comparison with scientific demonstration andsimulation methods to provide strong evidence for systematic study of this paper. The maintask of paper as follows:First, according to the actual situation of data acquisition transmission of wells in oilfield,this paper takes single well beam pumping unit as study object based on ZigBee wirelesssensor network technology, so it builds pumping condition parameter monitoring system, atthe same time, which fuses the mature technology of GPRS data communication transmissionmode, implements a new type of oil well working condition monitoring system for datacolletion and transmission.Secondly, this paper takes CC2430as the core chip and the RS232bus circuit is designedfor load an displacement sensors and the power circuit for meeting senor and ZigBee RF chip,gateway interface circuit, in the laboratory environment to test the module nodes for wirelesspower. This paper provides the entire networking process and workflow, improving thecapabilities and flexible performance of the network, reducing the cost.Last, this paper analysis the woking principle and fault principle of beam pumping unit,applying fuzzy neural network in diagnosis of pumping unit, proving the improved fuzzyneural network learning algorithm, which improves the gassian membership functions andfuzzy rules, through the simulation experiments show that improved algorithm is better inanti-interference and training error convergence then traditional methods. Further the systemmeasured multi-port pumping experiment data as samples, selected eight kinds of faultsymptoms and five kinds of failure data, writed calculation programming simulation inMATLAB7.10version. It turned out that the fuzzy neural network model can diagnoseaccurately and efficiency faults, such as sucker rod parting, pumping unit imbalance, eccentricwear of the sucker rod.