Research On Construction And Predictive Maintenance Method Of Shearer Hydraulic System Digital Twin

The traditional maintenance of mining equipment is generally based on past experience to formulate a periodic inspection plan,and the maintenance method relies on on-site inspection and maintenance by experienced technicians.However,with the continuous improvement of mine intelligence and measurement and control level,higher requirements are put forward for effective maintenance of mining equipment.As the core equipment of the fully mechanized mining face,it is particularly important to improve the maintenance efficiency of the shearer.Therefore,this paper studies the condition monitoring,fault prediction and predictive maintenance scheme of the shearer hydraulic system.The common fault characteristics of the hydraulic system of the shearer are analyzed,the construction plan of the digital twin of the hydraulic system of the shearer is formulated,the mechanism and implementation process of the predictive maintenance system are clarified,and the state monitoring and fault prediction method based on the hydraulic state signal is proposed.The digital twin of the hydraulic system of the shearer is realized,the function of the predictive maintenance system is realized,and the safe and stable operation of the shearer is guaranteed.Aiming at the difficulties in the application of complex and difficult charts in traditional condition monitoring methods,comprehensively consider the scope of common faults in the hydraulic system of shearers,and study the intuitive acquisition method of equipment dynamic data based on the condition monitoring model,build a three-dimensional visual equipment state monitoring platform,independently select the observation target in the virtual space,and correspondingly display the composition structure and state parameters of the shearer hydraulic system.Aiming at the problem of redundant and complex data sets in the process of fault prediction,a bidirectional data reduction method based on gray rough sets is studied,and an optimized artificial neural network prediction model suitable for the hydraulic system of the the four common prediction methods was compared to verify that the scheme is suitable for this system.According to the prediction results,the inspection and minor repair suggestions for the hydraulic system of the shearer are given.Under the condition of ensuring the normal inspection,if the system is applied to the current coal mine,it is expected to reduce 43.6%inspection and 27.1%downtime for minor repairs within two years.Finally,according to the actual maintenance process of the shearer hydraulic system,a predictive maintenance strategy for the shearer hydraulic system based on Mixed Reality(MR)is formulated.Using the Matlab-MySQL-Unity3D joint programming method,the prediction results are pushed to the fault countermeasure library,and then the auxiliary maintenance process is driven to deploy to the HoloLens glasses.The system has been validated for condition monitoring,fault prediction and predictive maintenance functions,and the results show that each module performs as expected.