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Rolling bearing fault diagnosis method based on EEMD and distribution fitting testing

A technology of distribution fitting test and rolling bearing, which is applied in the field of fault diagnosis of rolling bearings based on EEMD and distribution fitting test, can solve problems such as modal aliasing, and achieve the goal of reducing modal aliasing, good theoretical basis, and obvious fault characteristics Effect

Inactive Publication Date: 2014-07-02
LANZHOU JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, EMD also has disadvantages, the main problem is the mode aliasing problem

Method used

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  • Rolling bearing fault diagnosis method based on EEMD and distribution fitting testing
  • Rolling bearing fault diagnosis method based on EEMD and distribution fitting testing
  • Rolling bearing fault diagnosis method based on EEMD and distribution fitting testing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0025] 1. Assuming a simulation signal s(t), its composition is the superposition of 7Hz sinusoidal component and Gauspuls pulse component, figure 1 is the result of s(t) after EMD decomposition.

[0026] from figure 1 It can be clearly seen that the modal aliasing phenomenon has obviously occurred in IMF1 and IMF2, and it is difficult to see the physical meanings represented by IMF1 and IMF2 from the figure.

[0027] 2. Perform EEMD decomposition on the simulation signal s(t) in 1, the result is as follows figure 2 shown. from figure 2 It can be seen from s(t) that after EEMD decomposition, 4 IMF components and 1 margin are obtained, where c1 and c2 represent the decomposed Gauspuls pulse components, and c4 well represents the 7Hz sine component. Therefore, after After EEMD decomposition, the components contained in the original signal can be well decomposed, and each IMF component has its own physical meaning. Therefore, EEMD can well solve the problem of mode mixing,...

Embodiment 2

[0029] The fault signal of the inner ring of the rolling bearing comes from the Bearing Data Center of Case Western Reserve University in the United States, where the sampling frequency is 12000Hz and the motor speed is 1750r / min. Firstly, the EEMD decomposition is performed on the fault signal of the inner ring of the rolling bearing, and several intrinsic mode function components are obtained. Then, according to the probability distribution characteristics of IMF components, the selection of useful IMF components and the removal of useless IMF components are carried out by using normal probability graph and Jarque-Bera test method. The useful IMF component is reconstructed to obtain the noise-reduced inner ring fault signal, and finally the characteristic frequency of the rolling bearing fault is identified by using the envelope spectrum.

[0030] image 3 It is the result of the EEMD decomposition of the fault signal of the inner ring of the rolling bearing. Figure 4 Nor...

Embodiment 3

[0036] The fault signal of the rolling bearing outer ring still comes from the Bearing Data Center of Case Western Reserve University in the United States, where the sampling frequency is 12000Hz and the motor speed is 1750r / min. Firstly, the EEMD decomposition is performed on the fault signal of the outer ring of the rolling bearing to obtain several eigenmode function components. Then, according to the probability distribution characteristics of IMF components, the selection of useful IMF components and the removal of useless IMF components are carried out by using normal probability graph and Jarque-Bera test method. The useful IMF component is reconstructed to obtain the noise-reduced outer ring fault signal, and finally the characteristic frequency of the rolling bearing fault is identified by using the envelope spectrum.

[0037] Figure 7 It is the result of the EEMD decomposition of the rolling bearing outer ring fault signal. Figure 8Normal probability plot of the ...

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Abstract

The invention discloses a rolling bearing fault diagnosis method based on EEMD and distribution fitting testing, and relates to the field of rotary machine fault diagnosis. The method includes the steps that original signals are decomposed into a series of IMF components by means of EEMD; then each IMF component is sampled to obtain a sampling point; testing is carried out through a normal probability plot and a Jarque-Bera test, and whether data accord with normal distribution or not is judged; the IMF components according with white noise characteristics are removed, and signals after other components are added and noise reduction is carried out are reserved; finally, fault diagnosis is carried out on rolling bearings by means of an envelope spectrum. According to the method, the aim of signal noise reduction can be well achieved, signal fault features can be more obvious, ultimately fault frequency is identified by means of the envelope spectrum, and fault types of the rolling bearings can be well diagnosed.

Description

technical field [0001] The invention relates to the field of fault diagnosis of rotating machinery, in particular to a rolling bearing fault diagnosis method based on EEMD and distribution fitting test. Background technique [0002] Rolling bearings are widely used in rotating machinery, and they are also key components of large rotating machinery. Due to its structural characteristics, rolling bearings are easily damaged parts. Among the various types of faults that occur in rotating machinery, many of them are related to the damage of rolling bearings. 70% of mechanical equipment failures are vibration failures, and about 30% of these vibration failures are caused by bearings. This is because the working environment of rolling bearings is very harsh. Once a rolling bearing fails, it will cause a series of chain reactions in the entire mechanical equipment, bringing huge safety hazards to the mechanical equipment. Therefore, in the fault diagnosis technology of mechanical...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
Inventor 张友鹏张霆杨蕾赵斌董海燕
Owner LANZHOU JIAOTONG UNIV
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