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Filtering method based on iteration self-adaption multiscale morphological analysis

It is an iterative self-adaptive and morphological analysis technology, which can be applied to self-adaptive networks, impedance networks, electrical components, etc. It can solve the problem of inconspicuous extraction of fault feature information, and achieve the effect of suppressing noise, filtering out noise effects, and enriching theoretical methods.

Inactive Publication Date: 2014-04-09
YANSHAN UNIV
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Problems solved by technology

[0005] Aiming at the problem that the extraction of fault feature information is not obvious after traditional adaptive multi-scale morphology analysis and filtering under strong noise background and high sampling frequency

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  • Filtering method based on iteration self-adaption multiscale morphological analysis
  • Filtering method based on iteration self-adaption multiscale morphological analysis
  • Filtering method based on iteration self-adaption multiscale morphological analysis

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Embodiment Construction

[0024] The filtering method based on iterative adaptive multi-scale morphological analysis of the present invention will be further described below in conjunction with the accompanying drawings.

[0025] A filtering method based on iterative adaptive multiscale morphological analysis, such as figure 2 As shown, the method includes specific steps as follows:

[0026] Step 1 Select the Structural Element Shape

[0027] According to the influence of different shapes of structural elements on the signal filtering results and the purpose of signal filtering, select the appropriate shape of the structural elements.

[0028] Step 2 Adaptive Multiscale Morphological Analysis

[0029] First, the collected discrete state signal f(n) is zero-meanized, and its discrete sequence f is obtained on this basis 1 (n)={x n |n=1,2,…,N}, find f 1 All positive peak points in (n) P={p m |m=1,2,...,M}, according to each positive peak point p m The horizontal axis position (abscissa) p m,x , ...

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Abstract

The invention discloses a filtering method based on iteration self-adaption multiscale morphological analysis. The content includes: after performing self-adaption multiscale morphological analysis filtering once, applying a self-adaption method to a filtering result of the last time to determine a new structural element scale range, using the newly determined scale range to perform multiscale morphological filtering on an original signal, iterating the abovementioned processes till the structural element scale range determined by the self-adaption method is stable, and finally averaging the scale results as a final result. The filtering method based on iteration self-adaption multiscale morphological analysis in the invention, a self-adaption multiscale morphological analysis method is used iteratively, and thus in the circumstance of a strong noise background and relatively high sampling frequency, interference of noise and harmonic can still be effective suppressed, fault features can be clearly extracted, and an ideal filtering result can be obtained. The filtering method overcomes the defects of self-adaption multiscale morphological analysis, and enriches a theoretical method of morphological filtering.

Description

technical field [0001] The invention relates to a filtering method in the field of rotating machinery fault signal processing, in particular to a filtering method based on iterative self-adaptive multi-scale morphological analysis. Background technique [0002] Fault detection and diagnosis, fault prediction technology is an important subject of operation, maintenance and management of modern mechanical equipment. Among many mechanical equipment, rotating machinery has been widely used, and its fault signal is very complex, often showing obvious nonlinearity and non-stationarity. How to effectively suppress the interference of noise, harmonics, etc., and extract the fault characteristics of the signal become a key issue. [0003] The traditional signal processing method is based on the premise of the stationarity of the signal, so the signal reflecting the fault characteristic information cannot be extracted; in modern signal processing technology, wavelet transform, Hilber...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03H21/00
Inventor 姜万录李扬董彩云
Owner YANSHAN UNIV
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