Abstract:
In this work, a comparative study of stator current and vibration signal analysis has been done for detecting faults of outer and inner race in rolling bearings of induction motor. The investigation has been carried out on machines of different ratings at different load conditions.
Defective rolling element bearings generate eccentricity in the air-gap with mechanical vibrations. The air-gap eccentricities cause variations in the stator air-gap flux density that produces visible changes in the stator current spectrum. This is why we use MCSA to detect bearing faults, along with vibration spectrum;. in addition to this MCSA is a noninvasive method. In many industrial applications it becomes difficult to access vibration signal and hence MCSA greatly contributes for fault detection. Why because MCSA uses the induction motor as an efficient transducer.
In this work, wavelet packet decomposition technique is used for the current and vibration spectrums to get fine resolution; along with this auto-correlation is applied for both the signals to eliminate the randomly varying high frequency-coefficients from the noise signals. This work indicates that detecting fault frequencies by motor current spectrum analysis is more difficult than the vibration spectrum analysis.
The analysis is carried out in two ways: one is on-line and the other one is off-line. For on-line analysis MATLAB® programming is used for data acquisition and for signal analysis; where as for off line analysis, LabVIEWTM is used for data acquisition and MATLAB is used for signal analysis. Efficient monitoring system has been used both for low (7.5 kw) and high ratings (400 kw and 600 kw) motors of laboratory model and of a steel rolling mill respectively
