Realization method of frequency domain amplitude spectrum kurtosis diagram

Abstract

The invention discloses a realization method of frequency domain amplitude spectrum kurtosis diagram. The realization method comprises the following steps: firstly performing frequency slice wavelet transform on a signal to obtain a time frequency decomposition matrix, on this basis, performing signal reconstruction according to each sub frequency band with given scale, and solving an mean amplitude of a reconstructed signal, performing Fourier transform to obtain a square spectrum of the constructed signal, solving the spectrum kurtosis for an amplitude sequence of the square spectrum, and arranging the spectrum kurtosis diagram values according to scale-frequency band so as to obtain a spectrum kurtosis diagram. The size of the spectrum kurtosis is represented by using the color depth inthe spectrum kurtosis diagram, the larger the spectrum kurtosis value is, the deeper the color of the corresponding spectrum plane region is; in the application, the frequency band corresponding to the plane region with the deepest color is selected to reconstruct so as to obtain the time frequency feature signal for further analysis. The realization method disclosed by the invention can be usedfor signal analysis and fault feature extraction of the mechanical equipment and the electric system; and especially for the signal with the frequency-modulation and amplitude-modulation feature, theoptimal modulation frequency band of the frequency-modulation and amplitude-modulation component can be displayed in the diagram.

Description

technical field [0001] The invention belongs to the technical field of mechanical equipment fault diagnosis, and in particular relates to a method for realizing a frequency-domain amplitude spectrum kurtosis graph based on signal time-frequency decomposition. Background technique [0002] On-line monitoring of vibration signals is one of the commonly used methods for state monitoring and fault diagnosis of mechanical equipment. Due to factors such as mechanical transmission, environmental noise, and electromagnetic interference in mechanical equipment during the work process, vibration signals collected from on-site equipment monitoring points contain relatively high With strong noise, useful information is usually submerged. Therefore, separating the characteristic components containing fault characteristics from the noise-containing vibration signal plays an important role in correctly identifying faults. [0003] In mechanical equipment, gears and bearings are the most co...

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