VMD 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 the difficulty of determining the order of the fitting polynomial trend, the destruction of the fractal structure of the original signal, and the manual determination of the analysis scale, etc. problem, to achieve the effect of avoiding negative frequency phenomenon, high accuracy rate, high accuracy and precision
Inactive Publication Date: 2021-04-23
山东柯瑞申智能科技有限公司
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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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[0104] Examples such as figure 1 , figure 2 As shown, a VMD multi-scale fluctuation analysis state monitoring method includes the following steps:
[0105] 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;
[0106] Step 2: Use the Variational Mode Decomposition (VMD) 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 VMD algorithm, r n (k) represents the trend item obtained by the VMD algorithm, in this example, n=10; the VMD algorithm is known, see literature
[0107] Konstantin Dragomiretskiy, Dominique Zosso. Variational Mode Decomposition, IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62(3): 531-544;
[0108] Step 3: Use nonlinear discriminant algorithm to exclude noise components and trend items from the VMD decomposition results, and retain the ...
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The invention discloses a VMD multi-scale fluctuation analysis state monitoring method and device. The method comprises the steps of: carrying out decomposition on an equipment vibration signal through a VMD algorithm; removing a noise component and a trend term through a nonlinear discrimination algorithm, and reserving fractal signal components; carrying out interpolation on a local extreme point of each fractal signal component through a piecewise linear interpolation function; fitting an envelope by using a least square method; separating a frequency modulation part; estimating an instantaneous frequency by using a TEO algorithm, and calculating a corresponding instantaneous scale; determining a detrending result of the vibration signal according to an analysis scale; calculating a multi-fractal spectrum of the detrending signal; extracting singular indexes corresponding to the left end point, the right end point and the extreme point of the multi-fractal spectrum to serve as characteristic parameters of an equipment operation state so as to recognize the equipment operation state; and deploying the algorithm to the equipment state monitoring device so as to accurately distinguish the equipment operation state. The equipment state monitoring system has good flexibility and portability, is high in practicability. and convenient for engineering applications.
Description
technical field [0001] The invention relates to the field of equipment state monitoring and fault diagnosis, in particular to a VMD 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 t...
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Login to View More IPC IPC(8): G01M99/00G01M13/021G01M13/028G06F17/15
CPCG01M13/021G01M13/028G01M99/004G06F17/15
Inventor 豆春玲寇兴磊
Owner 山东柯瑞申智能科技有限公司