Method for drawing axis trajectory diagram by using variational mode decomposition reconstruction signal

Abstract

The invention relates to the technical field of fault diagnosis of rotating mechanical equipment, and discloses a method for drawing an axis trajectory diagram by using a variational mode decomposition reconstruction signal, which comprises the following steps: determining the number of layers of a variational mode decomposition model through an information entropy mean value of an intrinsic mode function component, and reconstructing the signal by using the magnitude of correlation to obtain an axis trajectory diagram; and finally, an axis trajectory diagram of the reconstructed signal is made. The axis trajectory diagram obtained by the method can effectively reduce the noise in the original axis trajectory diagram, and improves the accuracy of subsequent fault diagnosis.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of rotating mechanical equipment, and in particular relates to a method for drawing an axis track diagram by using variational mode decomposition reconstruction signals. Background technique [0002] As an important mechanical equipment in modern industry, rotating machinery is widely used in industrial production. Once the rotating equipment fails, it will cause huge economic losses. Rotating equipment often has faults such as unbalance and misalignment, which can often be identified by observing the axis trajectory diagram, but the displacement signal obtained in practical applications is often submerged by noise, so the obtained axis trajectory diagram is often not accurate enough , resulting in low accuracy of fault identification. [0003] A large number of experts and scholars have conducted research on how to remove signal noise. Due to the multiple vibration sources and serious ...

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Problems solved by technology

Due to the multiple vibration sources and serious noise interference of the on-site rotating equipment, the traditional time-frequency analysis method has low resolution and has great limitations; the wavelet transform (WT) has a better resolution and can distinguish the time-frequency characteristics of the signal. and improve the characteristics of time-frequency multi-resolution, but the wavelet transform only decomposes low-frequency signals, and the effect is not obvious for high-frequency signals; Empirical Mode Decomposition (EMD) is an adaptive signal analysis method that can extract signal Local features are highly efficient in the processing of nonlinear and non-stationary signals, but there will be overlap between modes; on the basis of empirical mode decomposition, a variational mode decomposition method (VMD) is proposed, which can Determine the eigenmode components according to the center frequency, thereby avoiding mode overlap. For variational mode decomposition, the center frequency is often used to determine the number of eigenmodes, but the method of determining the number of components according to the center frequency is cumbersome

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