Method for extracting bearing fault characteristic frequency through information-entropy optimized VMD and application thereof

A technology of fault characteristic frequency and information entropy, applied in special data processing applications, mechanical bearing testing, design optimization/simulation, etc., to achieve wide practicability, simple principle, and save operating costs

Active Publication Date: 2018-03-23
KUNMING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, VMD has the problem that the influencing parameters (mode number and penalty factor) need to be determined in advance, and its parameters need to be optimized to determine the optimal mode number and penalty factor

Method used

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  • Method for extracting bearing fault characteristic frequency through information-entropy optimized VMD and application thereof
  • Method for extracting bearing fault characteristic frequency through information-entropy optimized VMD and application thereof
  • Method for extracting bearing fault characteristic frequency through information-entropy optimized VMD and application thereof

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Embodiment 1

[0024] Embodiment 1: as Figure 1-4 Shown, a kind of information entropy optimization VMD extracts the method for bearing fault characteristic frequency, and the specific steps of described method are as follows:

[0025] According to the process described in the above invention, the fault simulation signal of the inner ring of the bearing is analyzed and processed in Matlab software.

[0026] Step1. First optimize the number of modes. Initialize the number of modes K min =2, penalty factor α and bandwidth τ Use default values: α =2000, τ =0; perform VMD decomposition on the original vibration signal of the bearing, calculate the information entropy of each mode, and obtain the minimum value of information entropy under this mode number by comparison, and then K = K +1 to continue the above analysis until fetched K = 16; compare the size of the information entropy minimum value obtained under each modal number, and the modal number corresponding to the smallest infor...

Embodiment 2

[0032] Embodiment 2: as figure 1 ,with Figure 5-7 Shown, a kind of information entropy optimization VMD extracts the method for bearing fault characteristic frequency, and the specific steps of described method are as follows:

[0033] According to the process described in the above invention, the actual bearing outer ring fault signal is analyzed, and the Matlab software analysis result is given.

[0034] Step1. First optimize the number of modes. Initialize the number of modes K min =2, penalty factor α and bandwidth τ Use default values: α =2000, τ =0; calculate the information entropy of each mode, and obtain the minimum value of information entropy under this mode number by comparison, and then K = K +1 to continue the above analysis until fetched K =16; compare the numbers in each mode K The size of the minimum value of information entropy obtained under the following conditions, the modal number corresponding to the minimum value of information entropy K D...

Embodiment 3

[0040] Embodiment 3: as figure 1 , Figure 8 Shown, a kind of information entropy optimization VMD extracts the method for bearing fault characteristic frequency, and the specific steps of described method are as follows:

[0041] According to the process described in the above invention, the actual bearing inner ring fault signal was analyzed (in order to enhance the contrast, Gaussian white noise of SNR=-1dB was added to the original signal).

[0042] Step1. First optimize the number of modes. Initialize the number of modes K min =2, penalty factor α and bandwidth τ Use default values: α =2000, τ =0; perform VMD decomposition on the original vibration signal of the bearing, calculate the information entropy of each mode, and obtain the minimum value of information entropy under this mode number by comparison, and then K = K +1 to continue the above analysis until fetched K =16; compare the numbers in each mode K The size of the minimum value of information entrop...

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Abstract

The invention discloses a method for extracting bearing fault characteristic frequency through information-entropy optimized VMD and application thereof. The method for extracting the bearing fault characteristic frequency through the information-entropy optimized VMD includes the steps that firstly, according to the information entropy minimum principle, the modal number of VMD is optimized, andthe optimized modal number is adopted to optimize the penalty factor of the VMD according to the information entropy minimum principle; then, the optimized modal number and penalty factor are adoptedto conduct VMD on bearing original vibration signals to obtain IMF components of the vested modal number, and through comparison, the IMF component where the information-entropy minimum is located canbe obtained and serve as a sensitive IMF component; finally, the selected IMF component is subjected to envelope demodulation analysis to extract the bearing fault characteristic frequency. By the adoption of the method for extracting the bearing fault characteristic frequency through the information-entropy optimized VMD, the bearing fault characteristic frequency can be effectively extracted; the method for extracting the bearing fault characteristic frequency through the information-entropy optimized VMD is applied to analyzing bearing simulation signals and actual bearing signals and hasrelatively broad practicability.

Description

technical field [0001] The invention relates to a method for extracting characteristic frequencies of bearing faults by information entropy optimization VMD and an application thereof, belonging 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 causes of mechanical failure. Therefore, the condition monitoring and fault diagnosis of bearings has always been a hot spot in the 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 environment. Noise interference from equipment and the environment mak...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06F17/18G01M13/04
CPCG01M13/045G06F17/18G06F30/20
Inventor 伍星李华刘韬陈庆
Owner KUNMING UNIV OF SCI & TECH
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