Equipment natural vibration mode self-learning recognition method based on online vibration data

Abstract

The invention discloses an equipment natural vibration mode self-learning recognition method based on online vibration data, which is characterized by comprising the steps of carrying out primary denoising on equipment vibration signals x(n) by using a wavelet packet transform algorithm so as to acquire denoised signals f1<^>(n); carrying out secondary denoising on the signals f1<^>(n) by using singular value decomposition so as to acquire secondarily denoised vibration signals f2<^>(n); carrying out spectral analysis on the denoised vibration signals f2<^>(n) by using windowed discrete Fourier algorithm, and calculating to acquire equipment vibration spectrum; training by using a self-learning algorithm to acquire the equipment vibration spectrum, and finally acquiring the natural characteristic frequency and the amplitude of equipment. The equipment natural vibration mode self-learning recognition method based on the online vibration data has the beneficial effect that the characteristic frequency of components can be recognized accurately when a research object of a complicated system.

Description

technical field [0001] The invention belongs to the technical field of on-line monitoring, and relates to a self-learning identification method of a natural vibration mode of equipment based on online vibration data. Background technique [0002] Online monitoring technology is the most basic measure to estimate the health status of equipment, especially for mechanical equipment (or systems), vibration monitoring and analysis is the most practical and effective method. The vibration signal of the equipment is closely related to its mechanical structure, and the condition of its internal mechanical structure during the operation of the equipment can be directly and effectively reflected from the vibration signal. By processing and analyzing the vibration signal of the equipment, the inherent vibration mode of the equipment under normal operating conditions is extracted, which is used to distinguish the vibration mode when the equipment is abnormal or faulty, so as to make tim...

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Problems solved by technology

[0007] The purpose of the present invention is to provide a self-learning identification method for the natural vibration mode of equipment based on online vibration data, which solves the problem of the calculated vibration in the finite element modal analysis of the prior art, especially when the research object is a relatively complex system. The mode is often too messy, it is difficult to identify the eigenfrequency of the component that is really concerned, and it is difficult to judge the value of the eigenfrequency

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