Slewing bearing fault diagnosis method and device and storage medium

Abstract

The invention discloses a slewing bearing fault diagnosis method and device and a storage medium, and the method comprises the steps: S1, obtaining a vibration signal of a slewing bearing, and setting the vibration signal as an original signal; s2, processing the original signal through an EEMD method, then obtaining an intrinsic mode component IMF of the vibration signal through decomposition, and then carrying out preferential processing on the IMF to obtain an optimal component; s3, performing MCKD parameter optimization processing on the optimal component by using a GWO algorithm and taking the related kurtosis as a fitness function to obtain an optimal parameter combination; s4, substituting the optimal parameter combination obtained through optimization processing of the GWO algorithm into MCKD to analyze the optimal component so as to obtain a noise reduction signal; and s5, carrying out envelope spectrum analysis on the noise reduction signals, then comparing and analyzing the fault characteristic frequency found in an envelope spectrum and the theoretical fault characteristic frequency, obtaining a diagnosis result. The scheme is reliable in implementation, high in accuracy and rapid in response.

Description

Technical field [0001] The present invention relates to the field of bearing detection techniques, and more particularly to a swivel support fault diagnosis method, apparatus, and storage medium. Background technique [0002] Swiring support is a special large rolling bearing, widely used in a variety of large machinery, such as port seat cranes. The force unevenness of each portion is complex and the motion state of each part is complex in the working state, and the impact of the fault is submerged in the background noise due to the operating characteristics and a large background noise of low speed overload. The extraction of features is difficult than in general high-speed bearing. However, in the long-term use of the door crane, the failure of the rotary support is inevitable. If these faults cannot be found in time and respond to measures, it will affect the normal operation of the door crane while lengthening huge economic losses. [0003] At present, a small number of rese...

AI Technical Summary

Problems solved by technology

However, because the data comes from the test bench, and the fault is caused by artificial processing, there is still a certain difference from the actual fault on site.

Images