A new predictive model for evaluating the vibration of a sawing machine was developed using a new rock classification system. The predictors are machine parameters and a rock sawability index. The new rock classificat...A new predictive model for evaluating the vibration of a sawing machine was developed using a new rock classification system. The predictors are machine parameters and a rock sawability index. The new rock classification system includes four major parameters of the rock： uniaxial compressive strength, abrasiv- ity index, mean MoWs hardness, and Young＇s modulus. The FAHP approach was used when determining the weights of these parameters by six decision makers. Two groups of carbonate rocks were sawn using a fully-instrumented laboratory sawing rig at different feed rates and depths of cut. During the sawing trials system vibration was monitored as a measure of saw performance. Then, a new statistical model was obtained by multiple regression on the machining parameters and the rock sawability index. The model is very useful for the evaluation of the system vibration, and for selecting suitable machining parameters, from a limited set of mechanical properties.展开更多
There has been a lot of research has been performed regarding diagnosing rolling element bearing faults using wavelet analysis, but almost all methods are not ideal for picking up fault signal characteristics under st...There has been a lot of research has been performed regarding diagnosing rolling element bearing faults using wavelet analysis, but almost all methods are not ideal for picking up fault signal characteristics under strong noise. Therefore, this paper proposes auto-correlation, cross-correlation and weighted average fault diagnosis methods based on wavelet transform (WT) de-noising which combine correlation analysis with WT for the first time. These three methods compute the auto-correlation, the cross-correlation and the weighted average of the measured vibration signals, then de-noise by thresholding and computing the auto-correlation of de-noised coefficients of WT and FFT of energy sequence. The simulation results indicate that all methods enhance the capabilities of fault diagnosis of rolling bearings and pick up the fault characteristics effectively.展开更多
文摘A new predictive model for evaluating the vibration of a sawing machine was developed using a new rock classification system. The predictors are machine parameters and a rock sawability index. The new rock classification system includes four major parameters of the rock： uniaxial compressive strength, abrasiv- ity index, mean MoWs hardness, and Young＇s modulus. The FAHP approach was used when determining the weights of these parameters by six decision makers. Two groups of carbonate rocks were sawn using a fully-instrumented laboratory sawing rig at different feed rates and depths of cut. During the sawing trials system vibration was monitored as a measure of saw performance. Then, a new statistical model was obtained by multiple regression on the machining parameters and the rock sawability index. The model is very useful for the evaluation of the system vibration, and for selecting suitable machining parameters, from a limited set of mechanical properties.
文摘There has been a lot of research has been performed regarding diagnosing rolling element bearing faults using wavelet analysis, but almost all methods are not ideal for picking up fault signal characteristics under strong noise. Therefore, this paper proposes auto-correlation, cross-correlation and weighted average fault diagnosis methods based on wavelet transform (WT) de-noising which combine correlation analysis with WT for the first time. These three methods compute the auto-correlation, the cross-correlation and the weighted average of the measured vibration signals, then de-noise by thresholding and computing the auto-correlation of de-noised coefficients of WT and FFT of energy sequence. The simulation results indicate that all methods enhance the capabilities of fault diagnosis of rolling bearings and pick up the fault characteristics effectively.
