Method and device for extracting vibration signal characteristic frequency band

Abstract

The present invention provides a method and device for extracting a vibration signal characteristic frequency band. The method comprises a step of extracting the marginal spectrum of a vibration signal, a step of dividing the marginal spectrum into a plurality of window marginal spectrums by using a sliding window and carrying out clustering analysis on the window marginal spectrum sets under the same frequency band in different fault states so as to calculate and generate the clustering effect evaluation index of each frequency band window marginal spectrum set, and a step of extracting a fault sensitive characteristic frequency band according to the clustering effect evaluation index. The vibration signal characteristic frequency band extracted by the method and device of the invention has the advantages of high fault recognition rate and good noise immunity ability quality.

Description

technical field [0001] The invention relates to the field of mechanism detection, in particular to a method and device for extracting characteristic frequency bands of vibration signals. Background technique [0002] Condition monitoring of rolling bearings is of great significance to the safe operation of equipment. Equipment condition monitoring and fault diagnosis can be divided into three steps: signal acquisition, feature extraction, and state identification. Analyzing equipment vibration signals is the most commonly used means of fault diagnosis. How to extract the characteristic information representing the bearing fault state from the nonlinear and non-stationary vibration signals is the key to realizing bearing fault diagnosis. The time-frequency analysis method is a powerful tool for nonlinear and non-stationary signal analysis. Commonly used analysis methods include: wavelet transform, short-time Fourier transform, Wigner-Ville Distribution (WVD), Hilbert Huang T...

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Problems solved by technology

However, due to the noise signal and EMD (Empirical Mode Decomposition, EMD) itself, the marginal spectrum inevitably contains some information that has little or no correlation with the fault type, which not only makes the feature space of the fault type large, but also It will also bring interference to the classification of faults and reduce the accuracy of fault diagnosis

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