An Elmd Algorithm for Optimal Noise Parameter Selection for Bearing Fault Diagnosis

A technology for fault diagnosis and noise parameters, applied in computing, electrical digital data processing, special data processing applications, etc., can solve the problem of not considering the optimal noise coefficient selection, etc., to suppress the modal aliasing phenomenon and accurately process the analysis. Effect

Inactive Publication Date: 2017-08-25
INNER MONGOLIA UNIV OF SCI & TECH
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Problems solved by technology

Throughout the existing patents and papers, the problem of optimal noise figure selection is not considered in the ELMD decomposition process

Method used

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  • An Elmd Algorithm for Optimal Noise Parameter Selection for Bearing Fault Diagnosis
  • An Elmd Algorithm for Optimal Noise Parameter Selection for Bearing Fault Diagnosis
  • An Elmd Algorithm for Optimal Noise Parameter Selection for Bearing Fault Diagnosis

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Embodiment Construction

[0029] see figure 1 , figure 2 .

[0030] There are two important parameters to consider in the ELMD algorithm: the magnitude of the white noise and the number of times white noise is added . The relative root mean square error criterion (Relative-RSME) is used to judge the decomposition performance of ELMD under different noise amplitudes; the signal-to-noise ratio (SNR) is used to measure the residual noise in the decomposition results after adding different noise times.

[0031] The relative root mean square error is defined as follows:

[0032] (1)

[0033] (2)

[0034] in, is the original vibration signal; for the original vibration signal the PF component with the highest correlation; is the number of sampling points of the original vibration signal.

[0035] When the relative root mean square error criterion (Relative-RSME) is very small, close to zero, it means infinitely close to ,Right now contains the same components as the original sig...

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Abstract

The invention relates to the field of mechanical fault diagnosis, in particular to an ELMD algorithm for optimal noise parameter selection for bearing fault diagnosis. In particular, the present disclosure discloses an ensemble local mean decomposition (Ensemble local mean decomposition, ELMD) method for optimal noise parameter selection for bearing fault diagnosis. In order to obtain a better decomposition effect in the present invention, before performing ELMD decomposition on the bearing vibration signal, it is necessary to optimally select the added white noise parameters (the amplitude of the white noise and the number of times of adding white noise), and the optimal noise parameter selection is directly related to The performance of ELMD algorithm is good or bad. The final experiment proves that the ELMD algorithm with the optimal noise parameter selection can accurately process and analyze the vibration signal of the faulty bearing.

Description

[0001] Technical field: [0002] The invention relates to the field of mechanical fault diagnosis, in particular to an ELMD algorithm for optimal noise parameter selection for bearing fault diagnosis. [0003] Background technique: [0004] The vibration signals generated by rotating mechanical equipment usually carry a large amount of equipment state information, and the process of extracting these state information is actually the process of analyzing and processing the vibration signals. However, the vibration signals of rotating machinery are mostly non-stationary and nonlinear, and the main consideration for such signals should be their local characteristics. Therefore, the traditional frequency-domain analysis method based on the global transformation of Fourier transform is obviously inappropriate. However, the time-frequency analysis method based on joint analysis of time-frequency domain, which can provide local information of signal time-frequency domain at the same t...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00
Inventor 张超郭宇秦波杨斌王建国高君王昱晨
Owner INNER MONGOLIA UNIV OF SCI & TECH
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