We should know that when the vacuum pump unit is running as a mechanical equipment, it is inevitable to have some vibration, because the vibration will be generated when the pump itself rotates. Of course, there is also the vibration excitation of the prime mover (such as motor, diesel engine, etc.). However, these are normal vibration phenomena, and sometimes some vibrations in the unit are abnormal, which means that there is a fault in the equipment, so it is necessary to find out the vibration source in time. So how to find the vibration source of the unit?
The vibration of so many vibration sources will inevitably affect each other, and the fault signal will often be submerged in the background noise and interference, which brings great difficulty to the pump fault diagnosis. The existing signal analysis methods have not made a breakthrough in the vibration signal separation of multiple excitation sources and the feature extraction of low signal-to-noise ratio vibration signals, Further research is still needed.
At present, people mainly use the machine learning method based on data to classify the fault of vacuum pump unit. The characteristic of this method is that it needs a large amount of sample data, but when the sample data is difficult to obtain, this method shows its limitations. Therefore, it is necessary to study a small sample fault mode classification method with higher generalization ability, so that it can use limited data samples to obtain better diagnosis results.
Therefore, in order to effectively identify the vibration source of the vacuum pump unit, we need to adopt some more professional technology, so as to accurately find various vibration sources of the equipment. What the operator can do is to do a good job in the regular inspection of the equipment to avoid potential safety hazards and other problems inside the equipment, so that the equipment can operate stably and normally.
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