Rolling-element bearings have wide applications in the industries today, and these bearings maintenance becomes an important task for the maintenance professionals. The rolling-element bearings wear out easily due to metal-to-metal contact, which creates bearing faults in the outer race, inner race, and ball. It is also the most vulnerable component of a machine because it is often under high load and high running speed conditions.
Bearing fault regular diagnostics is critical for industrial safety and machine operations along with reducing the maintenance costs or avoiding shutdown time. Among the outer race, inner race, and ball, the outer race tends to be more vulnerable to faults and defects.
The natural frequencies of a rolling bearing with the free boundary conditions are 3 kHz. Therefore, in order to use the bearing component resonance bandwidth method to detect the bearing fault at an initial stage. You should adopt a high-frequency range accelerometer. And you may obtain data from a long duration. Identify a fault characteristic frequency when the fault extent is severe, such as that of the presence of a hole in the outer race.
The harmonics of fault frequency is a more sensitive indicator of a bearing outer race fault. For a more rigorous bearing fault detection, you should detect waveform, spectrum, and envelope techniques. And these will help reveal these faults. However, if you use high-frequency demodulation in the envelope analysis in order to detect bearing fault characteristic frequencies. The maintenance professionals have to be more careful in the analysis because of resonance. Because it may or may not contain fault frequency components.
Using spectral analysis as a tool to identify the faults in the bearings faces challenges due to issues like low energy, signal smearing, etc. It usually requires high resolution to differentiate the fault frequency components from the other high-amplitude adjacent frequencies. Hence, when do the FFT sample signal analysis, enough sample length to give adequate frequency resolution in the spectrum. Also, keeping the computation time and memory within limits and avoiding unwanted aliasing may be demanding.
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