A collaborative diagnosis method for rolling bearing faults based on casing vibration signals

Abstract

The invention discloses a method for collaboratively diagnosing the fault of a rolling bearing based on vibration signals of cartridge receiver. The method includes the following steps: S1. arranginga sensor on a cartridge receiver to acquire vibration signals; S2. based on smallest entropy convolution, enhancing the impact part of a to-be-detected signal; S3. based on wavelet transformation, extracting the resonance waveband of the rolling bearing; S4. suppressing the non-periodic part within the resonance waveband by performing autocorrelation analysis; and S5. based on Hilbert transformation envelope demodulation, positioning the fault of the rolling bearing. According to the invention, the method herein can effectively eliminate influence on signal transmission path of the rolling bearing and interference by complex background noises, highlights the spectral peaks corresponding to the feature frequency of the fault, can position the fault which causes damage to the surface of therolling bearing, and addresses difficult diagnosis, low precision and poor robustness in monitoring the fault of the rolling bearing based on vibration signals that are tested on test points of the cartridge receiver.

Description

technical field [0001] The invention relates to the field of fault diagnosis of rolling bearings, in particular to a cooperative diagnosis method for rolling bearing faults aimed at casing vibration signals. Background technique [0002] The vibration method is sensitive to early faults of rolling bearings and has been widely used in fault diagnosis of rolling bearings. However, the existing research basically takes the bearing seat signal on the bearing tester as the analysis object, and the signal is less affected by the transmission path and noise. Taking the rolling bearing of an aero-engine as an example, without changing the structure of the engine, the sensor measuring points can only be arranged on the casing, and the transmission of the vibration signal needs to go through a very complicated transmission path, coupled with the system excitation under complex working conditions There are many sources, and structural vibration noise, aerodynamic noise sources, and co...

AI Technical Summary

Problems solved by technology

However, minimum entropy deconvolution may also enhance shocks at non-bearing resonance frequency bands and aperiodic shocks, which can interfere with fault diagnosis of rolling bearings

Images