Study On Monitoring And Fault Diagnosis Of Mine Hoist

Mine hoist is the main production equipment in mine,its safety will directly affect the interests of enterprises and the safety of staff.With the progress of mechanical technology,the parts of mine hoist become more precise.In view of the important position of hoist in mine,people began to monitor its safety early,but in the past the technology is relatively backward,the accuracy is poor,and it needs professional personnel to carry out this work.Therefore,it is necessary to integrate modern technology into it and further improve its security performance.In view of this,the research on fault diagnosis should be carried out.Is of practical significance.In this paper,various faults of mine hoist are analyzed,and the existing fault diagnosis technology is studied.The corresponding calculation of BP neural network and genetic algorithm is given.Based on the advantages of genetic algorithm,the common shortcomings of BP neural network are improved,and the information fusion technology is introduced into it.On this basis,the fault diagnosis algorithm needed in this paper is obtained.Then,the hardware system of sensor,data acquisition card and so on are designed according to the need to collect data.The traditional anti-interference measures are improved,and the user management,parameter design and parameter monitoring are designed on the platform of Labview.Test,fault diagnosis,help five main modules of the software system.Then the fault diagnosis module is designed,and a fault diagnosis method of mine hoist is designed,that is,using Matlab as the calculation background,the information fusion of each parameter of hoist is carried out,and the training sample is obtained.The trained samples are obtained by genetic algorithm optimized BP neural network,and the fault diagnosis is carried out in the system.Finally,the feasibility of the system is verified by the data samples.The following conclusions are obtained: ?The BP neural network optimized by genetic algorithm has faster convergence speed and will not fall into the local optimal solution than the BP neural network optimized by geneticalgorithm,and the calculated results are more accurate after the introduction of information fusion technology.?With the reasonable arrangement of multiple sensors,the monitoring of different space and time of the same position can be realized,and the obtained data is more accurate and comprehensive,and the accuracy of fault diagnosis is improved.?The brake shoe gap sensor is designed.It is more convenient and reliable than the displacement sensor.?Using the designed system to diagnose the vibration and space time of the spindle,the diagnosis result is obtained successfully,and the result is very good.It shows that the system designed in this paper is simple and accurate,which can contribute to the fault diagnosis of mine hoist to a certain extent,and then improve the safety performance of mine hoist.