Rolling bearing vibration signal characteristic extracting and analyzing method under early fault state

Abstract

The invention discloses a rolling bearing vibration signal characteristic extracting and analyzing method under early fault state, which includes the following steps: mounting a vibration acceleration transducer and collecting vibration signals through a vibration measurement instrument; performing autocorrelation function on the vibration signals through the processor in the vibration measurement instrument; extracting the partial maximum points in the signals and the corresponding sample point serial numbers, so as to obtain envelope signals; the resampling at the uniform sampling interval through the interpolation method based on the envelope signals and finally performing fast Fourier transform, so as to obtain an envelope spectrum. The vibration signal characteristic analyzing method can remarkably reflect the modulation characteristics in the vibration signals under the early fault state, and is particularly suitable for the analyzing and extracting of the modulation characteristics in the rolling bearing noisy vibration signals under the early weak fault.

Description

technical field [0001] The invention relates to a vibration signal analysis method, in particular to a feature extraction and analysis method of a rolling bearing vibration signal in an early fault state. Background technique [0002] At present, rolling bearings are important components of rotating machinery such as motors, fans, and compressors, and play a key role in supporting the rotor. When the rolling bearing outer ring, inner ring or rolling body fails, affected by its own structural characteristics, shock and modulation frequency characteristics will appear in the vibration signal measured from the bearing seat. The strength of the impact feature often reflects the fault degree, and the modulation frequency often reflects the fault location. Envelope analysis can extract the high-frequency vibration caused by the impact and the envelope signal attached to it from the unsteady signal, and the rolling bearing fault information can be extracted by performing spectrum ...

AI Technical Summary

Problems solved by technology

In the case of early faults, because the source signal is weak and the external interference is relatively large, it is difficult to effectively separate the two signals by using this method; (3) use the AR model and multiple analysis methods to reduce noise

However, the order of the AR model needs to be determined subjectively by people, which has a cert

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