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

A rolling bearing and fault diagnosis technology, applied in mechanical bearing testing, special data processing applications, instruments, etc., can solve the problems of lack of robustness, poor classification effect of different fault degrees, lack of adaptability to changing working conditions, etc., to improve judgment The effect of precision

Inactive Publication Date: 2015-05-20
BEIHANG UNIV
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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 i

Method used

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

Examples

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Embodiment

[0092] This example uses the experimental data of 6205-2RS deep groove ball bearings for verification. The outer diameter of the bearing is 39.04mm, the thickness is 12mm, the pitch diameter is 28.5mm, the diameter of the rolling elements is 7.94mm, and the number of rolling elements is 9. The angle is 0°. The bearing test consists of a motor with a power of 1.5kW, a torque sensor / encoder, a dynamometer and an electrical control device. The motor drives the input shaft, and the output shaft drives the load.

[0093] The sample signals collected during the normal state of the rolling bearing, the inner ring fault, the outer ring fault, and the rolling element fault are respectively used to detect and verify the rolling bearing fault diagnosis method based on local characteristic scale decomposition and multi-fractal detrending fluctuation analysis of the present invention. The specific steps are as follows:

[0094] Step 1. In the running state of the rolling bearing, collect t...

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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...

Claims

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Application Information

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IPC IPC(8): G01M13/04G06F17/50
Inventor 刘红梅张吉昌王轩吕琛
Owner BEIHANG UNIV
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