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FDM multi-scale fluctuation analysis state monitoring method and device

A multi-scale, state technology, applied in the direction of measuring devices, testing of mechanical components, testing of machine/structural components, etc., can solve problems such as manual determination of analysis scale, difficulty in determining the order of fitting polynomial trend, destruction of original signal fractal structure, etc. question

Inactive Publication Date: 2021-04-09
寇兴磊
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Problems solved by technology

Using the method proposed in the present invention to analyze the equipment vibration signal can effectively extract the multi-fractal characteristics of the equipment vibration signal, and overcome the analysis scale existing in the MFDFA method that needs to be manually determined, the order of the fitting polynomial trend is difficult to determine, and the gap between data segments Continuous problem, solve the original signal fractal structure damage and negative frequency phenomenon existing in MFDFAemd method, has the advantages of high accuracy and precision of analysis results, high accuracy of equipment operation status identification results, etc.

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  • FDM multi-scale fluctuation analysis state monitoring method and device
  • FDM multi-scale fluctuation analysis state monitoring method and device
  • FDM multi-scale fluctuation analysis state monitoring method and device

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Embodiment Construction

[0102] Examples such as figure 1 , figure 2 As shown, a FMD multi-scale fluctuation analysis state monitoring method and device, comprising the following steps:

[0103] Step 1: Use the acceleration sensor to measure the equipment vibration signal x(k) at the sampling frequency fs, k=1, 2, ..., N, N is the length of the sampling signal;

[0104] Step 2: Use the Fourier Decomposition Method (FDM) algorithm to decompose the signal x(k) into the sum of n components and a trend item, namely , where, c i (k) represents the i-th component obtained by the FMD algorithm, r n (k) represents the trend item obtained by the FMD algorithm, in this example, n=10; the FMD algorithm is known, see the literature Singh P, Joshi S D, Patney R K, et al. The Fourier decomposition method for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A, 2017, 473(2199): 20160871;

[0105] Step 3: Use nonlinear discriminant algorithm to exclude noise components and tre...

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Abstract

The invention discloses an FDM multi-scale fluctuation analysis state monitoring method and device. The method comprises the following steps: decomposing a vibration signal through an FDM algorithm, removing a noise component and a trend term in a decomposition result through a nonlinear discrimination algorithm, reserving a fractal signal component, carrying out the interpolation of a local extreme point of the fractal signal component through a segmented Hermite interpolation function, fitting the envelope of the fractal signal component by using a least square method, separating the frequency modulation part of each fractal signal component, estimating the instantaneous frequency of the fractal signal component by using a TEO algorithm, calculating the instantaneous scale, automatically determining the trend result of the vibration signal according to the analysis scale, calculating the multi-fractal spectrum of the trend-removed signal, and extracting coordinates of a left end point, a right end point and an extreme point of the multi-fractal spectrum to serve as characteristic parameters to recognize the operation state of the equipment. The operation state of the equipment can be distinguished, and good noise immunity, robustness and adaptability are achieved.

Description

technical field [0001] The invention relates to the field of equipment state monitoring and fault diagnosis, in particular to an FDM multi-scale fluctuation analysis state monitoring method and device. Background technique [0002] Equipment vibration signals contain rich fractal features, which can describe the operating state of equipment. Box dimension, power spectrum analysis and rescaled range method can estimate single fractal parameters of stationary signals, and detrended fluctuation analysis (DFA) can estimate single fractal dimensions of non-stationary signals. However, when the equipment fails, its vibration signal is usually non-stationary and has multi-fractal characteristics. At this time, the traditional fractal dimension estimation method will produce relatively large errors. Multifractal detrended fluctuation analysis (MFDFA) can estimate the multifractal parameters of non-stationary signals, but the MFDFA method has the problems of manual determination of ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/021G01M13/028G06K9/00
CPCG01M13/021G01M13/028G06F2218/10G06F2218/12
Inventor 寇兴磊
Owner 寇兴磊
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