Fault diagnosis method for rolling bearing based on LCD-MF (Local Characteristic Scale Decomposition )-(Multifractal)

Abstract

The invention discloses a fault diagnosis method for a rolling bearing based on LCD-MF (Local Characteristic Scale Decomposition)-(Multifractal). The fault diagnosis method comprises the following steps: firstly, obtaining a plurality of intrinsic scale components (ISC) by using LCD, and selecting useful ISCs to respectively calculate Teager energy operators (TEO) to obtain instantaneous amplitude and instantaneous frequency corresponding to each ISC; secondly, analyzing the instantaneous amplitude of each ISC by using multifractal detrended fluctuation analysis (MFDFA), and extracting a generalized Hurst index of each ISC as the multifractal characteristic of each ISC; thirdly, performing dimension reduction by using principal component analysis (PCA) and taking a result obtained by the PCA as a fault feature vector; acquiring a vibration signal of the rolling bearing during work under variable working conditions in real time, processing the acquired vibration signal according to the steps to obtain a corresponding fault feature vector M, and identifying the fault feature vector to realize fault detection and fault positioning of the rolling bearing.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of rolling bearings, in particular to an LCD-MF-based fault diagnosis method for rolling bearings. Background technique [0002] The role of rolling bearings is to support the rotating shaft and the parts on the shaft, and maintain the normal working position and rotation accuracy of the shaft. It is characterized by convenient use and maintenance, reliable operation, good starting performance, and high bearing capacity at medium speeds. Rolling bearings are key components commonly used in mechanical equipment, and whether their working status is normal is directly related to the normal operation status of the entire production line. Faults of rolling bearings often lead to a significant reduction in productivity, and in severe cases, even huge property losses. In order to ensure that rolling bearings operate in good condition, it is necessary to carry out condition monitoring and fault d...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the defects of lack of sufficient robustness, lack of adaptability to changes in working conditions and poor classification effect on different fault degrees in commonly used rolling bearing fault diagnosis methods

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