Weak fault feature extraction method based on selective integration and improved local feature decomposition

A technology of fault characteristics and local characteristics, applied in the testing of computer components and mechanical components, and the recognition of patterns in signals, etc., can solve problems such as pattern confusion

Active Publication Date: 2020-11-24
XUZHOU NORMAL UNIVERSITY
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Problems solved by technology

Due to the difference in complexity and smoothness of each signal subcomponent, it is difficult to avoid the interpolation cu

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  • Weak fault feature extraction method based on selective integration and improved local feature decomposition
  • Weak fault feature extraction method based on selective integration and improved local feature decomposition
  • Weak fault feature extraction method based on selective integration and improved local feature decomposition

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Embodiment Construction

[0066] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0067] The present invention proposes an improved local characteristic scale decomposition method of integrated selection. Firstly, the improvement of LCD mainly includes boundary extension and the selection envelope interpolation mean curve of integrated selection learning, so as to realize the effectiveness of LCD for decomposition of different complex signals. ; and then adopt the proposed AWOGS and minmax adaptive denoising strategy to denoise the decomposed single-component ISCs. details as follows:

[0068] 1. LCD boundary extension

[0069] Boundary extension needs to reflect the overall trend of the data at both ends, in order to eliminate component distortion ...

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Abstract

The invention discloses a weak fault feature extraction method based on selective integration and improved local feature decomposition. The method specifically comprises the steps that collecting vibration signals and carrying out normalization processing; adopting a boundary continuation method based on mirror image continuation symmetric points to carry out continuation on two ends of the normalized signal; decomposing the extended signal into a plurality of ISC components by adopting an SEILCD method; estimating the energy of each ISC component at the confidence of 95% and 99%; judging whether each ISC component belongs to noise or not, if yes, denoising the ISC by adopting a minmax threshold denoising method, and otherwise, denoising the ISC by adopting an AWOGS method; and normalizingand orthogonalizing the denoised ISC and carrying out time-frequency analysis. According to the method, the LCD interpolation mean value curve and the adaptive signal denoising can be adaptively selected, the complex vibration signal processing capability is improved, the fault characteristics are effectively enhanced, and the accuracy and interpretability of fault diagnosis are further improved.

Description

technical field [0001] The invention relates to a selective integrated improved local characteristic-scale decomposition method for extracting weak fault features (selective ensemble improved local characteristic-scale decomposition, SEILCD), which belongs to the technical field of weak mechanical fault feature extraction. Background technique [0002] Rotating machinery is the key core equipment in coal mine production, mainly composed of motors, reducers, hydraulic brakes and other parts. Extracting fault-related information from mechanical operating parameters such as vibration, pressure, and temperature to monitor the operating status of rotating machinery is the main content of current mechanical fault monitoring research. A large number of production practices and theoretical studies have shown that more than 70% of faults are hidden in vibration signals. [0003] Time-frequency analysis method is the mainstream method of mechanical fault diagnosis, such as wavelet an...

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Application Information

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IPC IPC(8): G01M13/00G06K9/00
CPCG01M13/00G06F2218/00G06F2218/04G06F2218/08
Inventor 任世锦潘剑寒唐娴魏明生
Owner XUZHOU NORMAL UNIVERSITY
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