The Research Of Peal-time Monitoring And Fault Diagnosis System For Mine Hoister’s Shaft

The mine equipments are the links between the underground and surface production,their reliability influence the mine production and safety of personnel directly. In whichthe spindle reliability is especially important. Because at present the repair of domesticmining equipment almot is used the traditional way, it is hard to understand and masterthe status of equipment in time; a set of real-time monitoring and fault diagnosis systemabout the hoist spindle would be established with taking signal analysis technology as thecore and combining the sensor technology and computer technology. In the startup andshutdown stages, the running state of the equipment is non-stationary. The wavelettransform for research in this stage that obtain the fault characteristics, in order todiagnosis comprehensively for hoist spindle.Though research the knowledge of relevant fault diagnosis theoretical, the summaryis given about fault form and vibration characteristics of the spindle and the bearing.After the fault diagnosis methods are compared, the theoretical basis is provided for thedevelopment of the system.The data acquisition system is built by LabVIEW, with the help of vibration sensors,data acquisition card PCI9114, connecting terminal board and power amplifier, whichtest single end and differential input methods for data acquisition respectively, and makethe achievement of multi-channel data acquisition.According to the design requirements of system, the program is written to achievereal-time acquisition and display, the state monitoring and alarm, speed measurement,fault analysis and other functions, and to interact with MATLAB to achieve waveletanalysis for the signal. Through the LabSQL database package, the SQL Server databasecan be accessed, which can realise storage, query and delete functions for data and statusvalues, and achieve the system of data for effective management purposes.The space time-index plots are used to determine the non-stationarity of the signal inthe startup and shutdown stages, which can find out the non-stationary phase. Use thedecomposition and reconstruction of wavelet transform for non-stationary signal, totarget on the bandwidth and select information with pertinence, then analyse thereconstruct signal spectrum for extracting the typical character of fault. Contrast withanalyse results of non-stationary signal spectrum directly, to further verificate the wavelet transform ability for non-stationary signal of rotating machinery diagnosis.