Research On Fault Diagnosis And Intelligent Lubrication Method Of Head Sheave Bearing Of Mine Hoisting System

Floor-type friction hoists are used more and more widely in mining production systems.Compared with other types of hoists,the head sheave bearing system of the floor-type friction hoist carries the entire lifting load,which is almost continuous operation,and its state directly affects the mine production efficiency and safety.The wide range of temperature changes in the working environment,the impact load of several hundred tons,and the impact of extreme weather on the lubrication effect may cause the failure of the head sheave bearing system.These reasons may indirectly or directly lead to the unreasonable lubrication of the head sheave bearing system,resulting in a series of failures caused by insufficient lubrication,such as the peeling of the working surface of the rolling bearing connecting shafts and supports at both ends,wear of sliding bearing between swimming wheel and shaft.If these faults are light,they will lead to the shutdown of production and maintenance,and the serious failure will lead to the occurrence of major accidents.Aiming at the importance and existing problems of the floor-type friction hoist bearing system,the influence of bearing fault on the response of the system was studied by establishing the bearing force model of different bearing faults,and its fault characteristic was analyzed.In order to find the fault in time and avoid the occurrence of major accidents,the research on the fault diagnosis method of the head sheave bearing was carried out.The fault diagnosis technology can be used to find the fault,but the fault cannot be reduced.Therefore,the research on the intelligent lubrication method of the head sheave bearing system was carried out.The main research contents of this paper are as follows:(1)Based on the loading characteristics and common faults,the influence of bearing faults on the response of head sheave system and the fault characteristics of head sheave bearings were analyzed.The results show that the responsed vibration waveform,intensity of the vibration and frequency spectrum of the head sheave system changed with the fault locations of the rolling bearings for the early spalling faults of the bearings.In view of this research,it was found that the vibration amplitude caused by inner and outer race fault of rolling bearing were 9.68 and 19.35 times that of normal condition,respectively.It is obviously the the bearing faults have a great impact on the system.In order to ensure the safety production of coal mines,it is of great significance to find out the fault as early as possible by fault diagnosis and adopt effective methods to reduce the fault.Besides,the reliability of the bearing fault diagnosis method based on vibration signal was verified by the research on the influence of bearing fault on the response of the head sheave system.(2)In order to prevent a series of safety acidents caused by the fault of the head sheave bearing by detecting the faults as early as possible,the research on fault diagnosis of the head sheave bearing was carried out from the point of signal processing.Because of the weak vibration signals collected on the field of the mine hoist usually contain a large amount of noise,and the effective components that characterize the fault will be submerged by the noise.Aiming at this problem,a hybrid noise reduction method based on minimum entropy deconvolution(MED)and improved complete ensemble EMD with adaptive noise(ICEEMDAN)was proposed.Using MED as a pre-filter can reduce the noise interference to ICEEMDAN on the extraction of weak fault signals under strong noise background.In order to eliminate the false components effectively,mutual information based sample entropy(MI-SE)was proposed to select effective intrinsic mode functions,which realizes retaining as many useful signals as possible while eliminating as many noise components as possible.The limitation of the traditional signal processing method in the feature extraction of the bearing weak fault signals was overcome by the proposed method.Finally,the effectiveness and reliability of the proposed method in the extraction of weak fault characteristics of bearings were verified by experiments.(3)Based on the research of fault feature extraction method of the vibration signal,the research on fault diagnosis algorithm of the head sheave bearing of the mine hoist based on support vector machine(SVM)was carried out.Aiming at the difficulty of choosing penalty factors and radial basis kernel parameters in SVM model,a diagnosis model(AFSA-SVM)based on support vector machine that optimized by the artificial fish-swarm algorithm(AFSA)was proposed.By comparing the diagnostic results of the extracted characteristic information of the bearing vibration signal based on EMD energy entropy,EEMD energy entropy and ICEEMDAN energy entropy,respectively,it is found that the fault diagnosis accuracy of the method by extracting the characteristic information of the bearing vibration signal based on ICEEMDAN energy entropy is the highest under the same conditions.Finally,the experimental data were analyzed using the method of extracting the characteristic information of the bearing vibration signal based on ICEEMDAN energy entropy and the intelligent fault diagnosis model of AFSA-SVM.The results show that the accuracy of the optimized fault diagnosis model is 10% higher than that without optimization,and the average mean square error is reduced by 74.4%.(4)A hardware and software system for collecting the vibration signals of the head sheave bearing of a floor-type friction hoist in coal mine were constructed.Based on the joint programming of Labview and MATLAB,an intelligent fault diagnosis system based on AFSA-SVM was designed.For the fault signal collected in the field,it was analyzed by the method of the proposed signal processing and the designed intelligent fault diagnosis system respectively.The results show that the analysis results are consistent with the actual situation,which verifies the effectiveness of the proposed method.(5)The fault diagnosis methods can detect faults,but it cannot reduce faults.Therefore,it is important to study ways to reduce the occurrence of faults.In view of the fact that the lubrication problem is the main factor causing the fault of the head sheave bearing,research on the intelligent lubrication method of the floor-type friction hoist head sheave bearing system was carried out,to reduce the occurrence of faults by ensuring that the head sheave bearing is in good lubrication state at all times.A research on the intelligent lubrication method for the floor-type friction hoist bearing of a certain coal mine was carried out.Aiming at the problems existing in the realization of intelligent lubrication,a new head sheave bearing system intelligent lubrication method based on an improved shaft was proposed.This method can realize the intelligent lubrication of two rolling bearings and three sliding bearings of the head sheave system at the same time by the new type shaft.Based on the objective of the basically equal oil injection at the three sliding bearings,the dimensions of the three radial oil flow channels in the head sheave shaft are optimized according to the combination of the theoretical calculation and the numerical simulation.The results show that when the inlet pressure is 30 MPa,the diameters of the three radial oil flow channels are set to 3 mm,4 mm and 5 mm,respectively,the minimum flow rate of the oil outlet is 29.5% less than the maximum flow rate.Compared with the other two models proposed in the paper,the model is the optimal model which can meet the requirement from the processing technology and the practical application angle.