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Method for extracting bearing fault feature frequency based on singular value decomposition and optimized frequency band entropy and application thereof

A technology of fault characteristic frequency and singular value decomposition, which is applied in mechanical bearing testing, mechanical component testing, machine/structural component testing, etc., can solve the problems that the SVD noise reduction effect is not as expected and the SVD effect has a great influence , to achieve the effect of excellent denoising effect, wide applicability and simple principle

Active Publication Date: 2018-11-13
KUNMING UNIV OF SCI & TECH
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The reconstruction order of SVD has a great influence on its effect. Therefore, how to determine its reconstruction order is a problem that needs to be studied; moreover, the noise reduction effect of a single SVD often fails to meet expectations, and it is necessary to reconstruct the signal Follow-up further noise reduction processing

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  • Method for extracting bearing fault feature frequency based on singular value decomposition and optimized frequency band entropy and application thereof
  • Method for extracting bearing fault feature frequency based on singular value decomposition and optimized frequency band entropy and application thereof
  • Method for extracting bearing fault feature frequency based on singular value decomposition and optimized frequency band entropy and application thereof

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Embodiment 1

[0038] Embodiment 1: as Figure 1-9 As shown, a method based on singular value decomposition and optimized frequency band entropy to extract bearing fault characteristic frequency, the specific steps of the method are as follows:

[0039] According to the flow process described in the above-mentioned invention, the fault simulation signal of the inner ring of the bearing is analyzed (f s 12000Hz, f n 3000Hz), and processed in Matlab software.

[0040] The SVD reconstruction order is selected based on the relative change rate of the singular kurtosis value and compared with the relative change rate of the singular value. Such as Figure 2-3shown. In the case of different signal-to-noise ratios, from the analysis of the kurtosis index of the reconstructed signal, the effect of the singular kurtosis value is basically better than the relative change rate (or equal to) of the singular value, and its reconstruction order value is relatively stable , there will be no greater vo...

Embodiment 2

[0047] Embodiment 2: as figure 1 ,and Figure 10-13 As shown, a method based on singular value decomposition and optimized frequency band entropy to extract bearing fault characteristic frequency, the specific steps of the method are as follows:

[0048] According to the flow process described in the above-mentioned invention, the actual bearing inner ring fault signal has been analyzed (f s 12000Hz, f n is 2830Hz), and the Matlab software analysis results are given.

[0049] Step 1. First, determine the SVD reconstruction order by using the relative rate of change of the singular kurtosis value. Such as Figure 10 As shown, the relationship diagram is given. It can be seen that the selected reconstruction order is 2 (because the maximum absolute value of the relative change rate of the obtained singular kurtosis value comes from a positive value, the first 2 order components are selected for reconstruction) . Therefore, reconstruct the signal and find its envelope spect...

Embodiment 3

[0053] Embodiment 3: as Figure 14-16 As shown, a method based on singular value decomposition and optimized frequency band entropy to extract bearing fault characteristic frequency, the specific steps of the method are as follows:

[0054] According to the flow process described in the above-mentioned invention, the actual bearing outer ring fault signal has been analyzed (f s 25600Hz, f n is 8148Hz), and the Matlab software analysis results are given. f shown in the figure r is the bearing rotation frequency, f o is the fault characteristic frequency of the outer ring of the bearing.

[0055] Step 1. First, determine the SVD reconstruction order by using the relative rate of change of the singular kurtosis value. Such as Figure 14 As shown, the relationship diagram is given. It can be seen that the selected reconstruction order is 1 (because the maximum absolute value of the relative change rate of the obtained singular kurtosis value comes from a positive value, the ...

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Abstract

The invention relates to a method for extracting bearing fault feature frequency based on singular value decomposition and optimized frequency band entropy and the application thereof, and belongs tothe field of mechanical fault diagnosis and signal processing. The concept of the singularity kurtosis value relative change rate is provided based on kurtosis indexes, and the SVD reconstruction order is determined by adopting a singular kurtosis value relative change rate. The method is simple in principle and takes kurtosis value as a theoretical basis and has a solid theoretical basis when compared with other methods. The denoising effect can be better than that of other methods. After the SVD reconstruction order is determined, a reconstructed signal is obtained, and an optimized band-pass filter is designed by utilizing the optimized frequency band entropy, further noise reduction processing is carried out on the reconstructed signals, and an analysis result is good in effect. According to the method, the bearing fault feature frequency can be effectively extracted. The method is applied to bearing simulation signals and actual bearing signal analysis, and has wide practicability.

Description

technical field [0001] The invention relates to a method and an application thereof for extracting characteristic frequencies of bearing faults based on singular value decomposition and optimized frequency band entropy, and belongs to the field of mechanical fault diagnosis and signal processing. Background technique [0002] Bearing is the core component of mechanical transmission system, and its failure is one of the important reasons for the failure of rotating machinery. Therefore, the condition monitoring and fault diagnosis of bearings has always been a hot and difficult point in fault diagnosis of mechanical equipment. When a rolling bearing fails, its vibration signal contains a large amount of operating state information, which is manifested as a non-stationary and multi-component modulation signal. Especially in the early stage of the fault, due to the weak modulation source, the early fault features are usually very weak, and are affected by the surrounding enviro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 刘韬李华伍星陈庆
Owner KUNMING UNIV OF SCI & TECH
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