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Pre-filtering extreme point optimization set empirical mode decomposition method and device

A technology integrating empirical modes and pre-filtering, applied in the field of signal processing, can solve the problems of long calculation time and slow decomposition speed, and achieve the effect of improving decomposition speed, reducing calculation amount, and reducing the number of integration times.

Pending Publication Date: 2021-02-12
HRG INT INST FOR RES & INNOVATION
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Problems solved by technology

[0005] The technical problem to be solved by the present invention lies in the problem of slow decomposition speed and long calculation time of the adaptive noise complete empirical mode decomposition algorithm in the prior art

Method used

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  • Pre-filtering extreme point optimization set empirical mode decomposition method and device
  • Pre-filtering extreme point optimization set empirical mode decomposition method and device
  • Pre-filtering extreme point optimization set empirical mode decomposition method and device

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Embodiment 1

[0049] Such as figure 1 As shown, a pre-filtering extreme point optimization ensemble empirical mode decomposition method is applied to the signal processing of mechanical equipment faults, and the method includes:

[0050] Step S1: Generate N groups of Gaussian white noise signals, obtain all extreme points of the Gaussian white noise signal, and extract its adjacent extreme points from each extreme point of the two sets of sequences of maximum value and minimum value respectively All signal points between the value points, get the Gaussian weight of each extreme point, and perform convolution on each extreme point and its Gaussian weight to obtain the updated extreme point, and every three adjacent updated extreme points Carry out weighted average to obtain the updated maximum value sequence and the updated minimum value sequence, obtain the mean value signal by cubic spline interpolation fitting method, judge whether the mean value signal satisfies the imf condition, and th...

Embodiment 2

[0068] Corresponding to Embodiment 1 of the present invention, Embodiment 2 of the present invention also provides a pre-filter extreme point optimization set empirical mode decomposition device, which is applied to signal processing of mechanical equipment failures. The device includes:

[0069] The noise component acquisition module is used to generate N groups of Gaussian white noise signals, obtain all extreme points of the Gaussian white noise signals, and extract each extreme point from the extreme points of the two sets of sequences of maximum and minimum values ​​respectively For all signal points between its adjacent extreme points, obtain the Gaussian weight of each extreme point, and perform convolution on each extreme point and its Gaussian weight to obtain the updated extreme point, after every three adjacent updates Weighted average of the extremum points to obtain the updated maximum value sequence and the updated minimum value sequence, the cubic spline interpol...

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Abstract

The invention discloses a pre-filtering extreme point optimization set empirical mode decomposition method, and the method comprises the steps: generating N groups of Gaussian white noise signals, andobtaining a noise component; adding corresponding noise components to the generated N groups of Gaussian white noise signals to form N groups of noise adding signals; decomposing the N groups of noisy signals, acquiring i components by each group of noisy signals, and superposing the i-th components of each group of noisy signals to obtain an average value so as to obtain an i-th modal component;acquiring a sampling signal, subtracting the first modal component from the sampling signal to obtain a first margin signal, subtracting the (i + 1) th modal component from the ith margin signal to obtain an (i + 1) th margin signal, wherein i is greater than or equal to 1, and decomposing the sampling signal until all margin signals are monotonous functions; and the method has the advantages ofbeing high in algorithm decomposition speed and short in calculation time.

Description

technical field [0001] The invention relates to the field of signal processing, and more specifically relates to an empirical mode decomposition method and device for an optimal set of pre-filtering extremum points. Background technique [0002] Adaptive Noise Complete Empirical Mode Decomposition CEEMDAN (Complete EEMD with Adaptive Noise) is an improvement on Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) algorithms, used to solve EEMD The mode mixing problem in the decomposition process, compared with the commonly used EEMD algorithm, effectively reduces the number of iterations and increases the reconstruction accuracy, which is very suitable for the analysis of nonlinear signals. [0003] However, CEEMDAN also has the following defects, slow decomposition speed and long calculation time. In practical applications, it will take up a lot of computing resources, especially in multi-line real-time signal processing, and in the applicati...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/14
CPCG06F17/14
Inventor 董健谭现虎何旭卞锦
Owner HRG INT INST FOR RES & INNOVATION
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