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Rolling element bearing outer ring fault detection.
2022-07-19
Rolling element bearings are widely used in today's industry, so the maintenance of these bearings becomes an important task for professional maintenance personnel. Rolling element bearings are prone to wear due to metal-to-metal contact, which can cause failures in the outer ring, inner ring and balls.
Rolling element bearings are also the most vulnerable parts of the machine due to the frequent exposure to high loads and high operating speeds. Regular diagnosis of rolling element bearing failures is critical for industrial safety and machine operation as well as for reducing maintenance costs or avoiding downtime. Of the outer ring, inner ring and balls, the outer ring is more prone to failures and defects.
Whether or not the natural frequencies of the bearing components are excited when the rolling elements pass through defects in the outer race is open to discussion. Therefore, we need to identify the natural frequency of the bearing outer ring and its harmonics.
Bearing faults generate pulses and result in strong harmonics of the fault frequency in the vibration signal spectrum. Due to the small energy, these fault frequencies are sometimes masked by adjacent frequencies in the spectrum. Therefore, during fast Fourier transform analysis, a very high spectral resolution is usually required to identify these frequencies.
The natural frequency of rolling bearings under free boundary conditions is 3 kHz. Therefore, in order to detect bearing faults at the initial stage using the bearing component resonance bandwidth method, a high frequency range accelerometer should be used and the data needs to be acquired over a long duration.
Fault characteristic frequencies can only be identified when the fault is severe, such as the presence of holes in the outer ring. Harmonics of the fault frequency are more sensitive indicators of bearing outer ring faults. For more severe fault bearing fault waveform detection, spectrum and envelope techniques will help to analyze these faults. Of course
However, if high-frequency demodulation is used in an envelope analysis to detect bearing fault characteristic frequencies, maintenance professionals must be more careful in the analysis because the resonance may or may not contain the fault frequency component.
Using spectral analysis as a tool to identify bearing faults presents significant challenges due to low energy, signal smearing, cyclostationarity, etc.
High resolution is often required to distinguish fault frequency components from other high-amplitude adjacent frequencies. Therefore, when acquiring a signal for fast Fourier transform analysis, the sampling length should be large enough to give sufficient frequency resolution in the spectrum.
Rolling element bearings are also the most vulnerable parts of the machine due to the frequent exposure to high loads and high operating speeds. Regular diagnosis of rolling element bearing failures is critical for industrial safety and machine operation as well as for reducing maintenance costs or avoiding downtime. Of the outer ring, inner ring and balls, the outer ring is more prone to failures and defects.
Whether or not the natural frequencies of the bearing components are excited when the rolling elements pass through defects in the outer race is open to discussion. Therefore, we need to identify the natural frequency of the bearing outer ring and its harmonics.
Bearing faults generate pulses and result in strong harmonics of the fault frequency in the vibration signal spectrum. Due to the small energy, these fault frequencies are sometimes masked by adjacent frequencies in the spectrum. Therefore, during fast Fourier transform analysis, a very high spectral resolution is usually required to identify these frequencies.
The natural frequency of rolling bearings under free boundary conditions is 3 kHz. Therefore, in order to detect bearing faults at the initial stage using the bearing component resonance bandwidth method, a high frequency range accelerometer should be used and the data needs to be acquired over a long duration.
Fault characteristic frequencies can only be identified when the fault is severe, such as the presence of holes in the outer ring. Harmonics of the fault frequency are more sensitive indicators of bearing outer ring faults. For more severe fault bearing fault waveform detection, spectrum and envelope techniques will help to analyze these faults. Of course
However, if high-frequency demodulation is used in an envelope analysis to detect bearing fault characteristic frequencies, maintenance professionals must be more careful in the analysis because the resonance may or may not contain the fault frequency component.
Using spectral analysis as a tool to identify bearing faults presents significant challenges due to low energy, signal smearing, cyclostationarity, etc.
High resolution is often required to distinguish fault frequency components from other high-amplitude adjacent frequencies. Therefore, when acquiring a signal for fast Fourier transform analysis, the sampling length should be large enough to give sufficient frequency resolution in the spectrum.
Also, keeping computation time and memory within bounds and avoiding unnecessary aliasing can be difficult. However, by estimating bearing fault frequencies and other vibration frequency components and their harmonics due to shaft speed, misalignment, line frequency, gearbox, etc., the required minimum frequency resolution can be obtained.
Previous:Classification of bearings.
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