Signal variation modal decomposition preset scale parameter selection method based on frequency domain information

Abstract

The invention relates to a signal variation modal decomposition preset scale parameter selection method based on frequency domain information. The method comprises the following steps: S1, performing parameter initialization: initializing a preset scale parameter K value of variational mode decomposition; S2, performing signal decomposition: carrying out variation modal decomposition on the collected signals to obtain K intrinsic mode function components; S3, calculating a frequency domain cross correlation coefficient; S4, calculating a frequency domain cross correlation coefficient difference value; S5, performing threshold discrimination: comparing a difference value delta k of frequency domain cross correlation coefficients between two adjacent intrinsic mode function components and an original signal with a discrimination threshold theta; S6, if delta klt is less than theta, determining that over-decomposition occurs, and presetting the optimal value of the scale parameter to be K; and S7, if delta k is greater than or equal to theta, determining that underdecomposition occurs, and presetting the optimal value of the scale parameter to be K-1. Compared with the prior art, the method has the advantages that the influence of noise, background signals and other components on selection of the preset scale parameter K is effectively reduced, and efficient and accurate selection of the parameter is achieved.

Description

technical field [0001] The invention relates to the field of fault information processing of rotating machinery, in particular to a method for selecting preset scale parameters based on frequency domain information for signal variation mode decomposition. Background technique [0002] Rotating machinery is an important equipment widely used in modern industrial production. It is widely used in shipbuilding, electric power, metallurgy, aviation, rail transit and other industries, and it is also a key equipment in these industries. On the one hand, due to the harsh working environment of rotating machinery, its components will inevitably deteriorate; on the other hand, with the advancement of science and technology, rotating machinery is developing in the direction of large-scale, complex and precise Rotating machinery has a high failure rate. When such equipment breaks down, not only the maintenance cost is expensive, the maintenance cycle is long, but in serious cases, it w...

AI Technical Summary

Problems solved by technology

In the setting process, if the value of the preset scale parameter K is too small, multiple components in the signal may be decomposed into the same intrinsic mode function (IMF), or a certain component cannot be decomposed. A certain component of may be decomposed into multiple modal functions, causing modal aliasing problems

[0006] However, at present, researchers often use the default

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