Rolling bearing fault diagnosis method based on clustering K-SVD algorithm

Abstract

The invention discloses a rolling bearing fault diagnosis method based on a clustering K-SVD algorithm, and the method mainly comprises the following steps: firstly carrying out adaptive decompositionon an original signal by employing a time-varying filtering empirical mode decomposition algorithm based on particle swarm optimization to obtain each intrinsic mode component, and calculating the related kurtosis index (Kcr) value of each component; then, selecting an intrinsic mode component with the maximum Kcr index value as an input sample of the clustering K-SVD algorithm to perform dictionary learning to obtain an over-complete dictionary DNew; and finally, carrying out sparse feature extraction on the original signal of the rolling bearing by utilizing the over-complete dictionary DNew and combining an orthogonal matching pursuit algorithm, and carrying out envelope spectrum analysis on a sparse representation result to extract the fault frequency feature of the rolling bearing. The method effectively solves the problem that a classical K-SVD algorithm is low in learning precision of the fault impact characteristics of the rolling bearing, and is of great significance for achieving weak fault diagnosis of the rolling bearing.

Description

technical field [0001] The invention relates to the technical field of equipment maintenance, in particular to a rolling bearing fault diagnosis method based on a clustering K-SVD algorithm. Background technique [0002] As an important supporting component, rolling bearings have been widely used in rotating machinery. However, due to the harsh operating environment and complex and changeable operating conditions, various damages will inevitably occur to rolling bearings, which will affect the safe and reliable operation of the whole machine. . However, due to the installation location of the sensor, other rotating parts, noisy working environment, electromagnetic interference and other factors, the vibration signal collected by the vibration sensor not only contains periodic pulse components caused by faults, but also has a lot of noise and harmonic interference , so that the effective feature information is submerged, which increases the difficulty of rolling bearing faul...

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Problems solved by technology

[0004] However, the vibration signal of rolling bearings not only contains periodic impact components, but also contains a large number of harmonic components and noise interference, so that the learned dictionary will inevitably contain atoms similar to noise or harmonic components, thereby reducing the impact of rolling bearing faults The effective extraction of features leads to the inability to diagnose rolling bearing faults well

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