Fault detection method of machine tool bearing based on empirical modal analysis

In order to solve the problem of fault detection of the outer ring for the motor spindle bearing using in the mainstream machine, a non-contact fault diagnosis method using the stator current signal of the machine tool spindle motor was proposed. The empirical mode decomposition (EMD) was used to analyze the non-stationary stator current signal of the machine tool motor, and the eigenmode function (IMF) of the stator current signal was extracted by the empirical mode decomposition method and applied to the Wigner -Ville distribution (WVD) to obtain the fault signal. Finally, the artificial neural network was used for pattern recognition of fault samples, which can effectively detect defects in the outer ring of machine tool spindle bearings. The test results show that the stator current monitoring with Wigner distribution based on empirical mode decomposition has the advantages of high accuracy, small amount of calculation and low detection cost. It has certain engineering practical and popularization value.