Mechanical fault diagnosis method based on dual-tree complex wavelet packet sub-band average kurtosis graph

Abstract

The invention, which relates to the field of mechanical fault diagnosis, discloses a mechanical fault diagnosis method based on a dual-tree complex wavelet packet sub-band average kurtosis graph, thereby improving the robustness and accuracy of mechanical fault feature extraction and fault type identification. According to the invention, with utilization of non-stationary and instantaneous impactcharacteristics of a mechanical fault vibration signal, a sub-band average kurtosis graph method based on a dual-tree complex wavelet packet is provided, wherein the efficiency and the accuracy are both considered in dual-tree complex wavelet packet transformation; the sub-band average spectral kurtosis can eliminate the influence of random impact interference on the kurtosis graph, can find the frequency band where the fault characteristic signal is located more accurately; the filtered signal is analyzed through a square envelope spectrum and thus the accurate extraction of the bearing faultcharacteristics and the precise recognition of the fault type are realized. The method is suitable for mechanical fault diagnosis.

Description

technical field [0001] The invention relates to the field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method based on a dual-tree complex wavelet subband average kurtosis graph. Background technique [0002] Bearings, gears, etc., as key components of power machinery, are widely used in various industrial fields such as aerospace, energy, and machinery manufacturing. Their operating status monitoring and fault diagnosis are of great significance for ensuring equipment reliability and avoiding safety accidents. The vibration signal can reflect the structural change of the machine, so the fault of the machine can be diagnosed through the vibration signal. However, in actual engineering, since the information of mechanical fault characteristics is often overwhelmed by strong background noise and other unstable components, the extraction of fault characteristic information has become a difficult challenge. [0003] When local faults such as pitt...

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Problems solved by technology

[0004] However, the mechanical fault diagnosis method based on the original fast kurtosis map is susceptible to occasional impulse pulse interference, so that the wrong frequency band signal is selected, resulting in failure of diagnosis, which reduces the robustness and accuracy of the method

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