Fault Diagnosis Of Motor Bearing Based On Current And Vibration Information Fusion Technology

As one of the important electrical equipment, motors are widely used in various areas from commercial manufacture and agricultural industries to our daily lives. Rolling bearing is one of the most important parts of motor in normal operation. Rolling bearing is one of the most easily damaged parts of motor components systems. The probability of damage is equivalent to about40%of the total failure of the induction motor. Diagnosing early faults of rolling bearing is meaningful to extend its life and reduce its costs.It is important for motor bearing fault characteristic extraction to study the bearing fault. This paper first analyzes the principle and vibration characteristic for typical fault of bearing, then explaining the principle of analysising and diagnosing bearing faults information in detail. According to the feature of both the vibration signals and the stator current when a fault in the bearing of induction motor, the amplitude recovery method is carried out to filter out the fundamental component of the current signal, thus highlighting the fault feature information, the auto-correlation functions of vibration signals measured on bearing cases are first computed, then the auto-correlation functions are delayed for sometime lags in order to decrease the affection of noise before demodulated by Hilbert Transform. Finally, after the two signals fusion-related processing to obtain an accurate characterization of the failure of the bearing failure characteristics, the key algorithm of success is that there was a correlation and related information between the current feature information and the vibration feature information. The amplitude recovery method shows the obvious virtues of little operation quantity, rapid processing speed and effectively project harmonic. The delay demodulation technique is a good noise reduction demodulation technology, which can filter out the noise interference in the vibration signal to obtain fault signal through Hilbert Transform envelope. Through the fusion of the two methods, the weak fault information can be better highlighted.