A signal decomposition method based on improved empirical wavelet decomposition

A technology of signal decomposition and empirical wavelet, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as ineffective decomposition, achieve the effects of suppressing excessive decomposition, enriching theoretical methods, and eliminating interference
CN109948286APending Publication Date: 2019-06-28NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY
Publication Date
2019-06-28

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Abstract

The invention relates to a signal decomposition method based on improved empirical wavelet decomposition. The method comprises the following steps: carrying out Fourier power spectrum density spectralline solving on a fault signal; selecting a plurality of different threshold values to remove the interference component spectrum values, decomposing the fault signals based on the new spectral line,and screening an optimal threshold value, an optimal decomposition result and a modal component containing rich fault characteristic information by using the fault characteristic energy ratio information. Under the interference background, modal components containing rich fault feature information can still be effectively and optimally decomposed, interference is eliminated, modal aliasing and excessive decomposition phenomena are restrained, an ideal decomposition result is obtained, the defects of EWT are overcome, and the theoretical method of modal decomposition is enriched.
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Description

technical field

[0001] The invention relates to a fault signal processing method of a rotating machine, in particular to a signal decomposition method based on improved empirical wavelet decomposition. Background technique

[0002] Rotating machinery such as hydraulic pumps, hydraulic motors, motors, bearings, gears and rotors are widely used in various important industrial fields. The deterioration of the health status of rotating machinery is of great significance to the intelligent fault diagnosis of rotating machinery. The vibration form and transmission path of rotating machinery faults are very complex, and the fault signal has the characteristics of nonlinearity and non-stationarity, and is easily introduced by equipment such as noise, background noise and electromagnetic interference. Therefore, how to effectively extract the modal components with rich fault feature information and suppress interference has become a key issue.

[0003] The disadvantage of wavelet t...

Claims

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