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Empirical Mode decomposition (EMD)- and Aproximate entropy (ApEn)-based acoustic emission signal characteristic extraction method of rolling bearing

An acoustic emission signal, rolling bearing technology, applied in mechanical bearing testing, pattern recognition in signals, character and pattern recognition, etc. Effect

Inactive Publication Date: 2018-12-14
CHINA JILIANG UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of difficult feature extraction of acoustic emission signals of rolling bearings at present, and propose a feature extraction method of acoustic emission signals of rolling bearings based on empirical mode decomposition and approximate entropy

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  • Empirical Mode decomposition (EMD)- and Aproximate entropy (ApEn)-based acoustic emission signal characteristic extraction method of rolling bearing
  • Empirical Mode decomposition (EMD)- and Aproximate entropy (ApEn)-based acoustic emission signal characteristic extraction method of rolling bearing
  • Empirical Mode decomposition (EMD)- and Aproximate entropy (ApEn)-based acoustic emission signal characteristic extraction method of rolling bearing

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Embodiment Construction

[0051] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0052] Such as figure 1 As shown, a kind of rolling bearing acoustic emission signal feature extraction method based on EMD and approximate entropy provided by the present invention, the method comprises the following steps:

[0053] (1) Perform empirical mode decomposition on the acoustic emission signal of the rolling bearing fault to obtain n IMF components and a residual component. This operation includes the following steps:

[0054] (1.1) Determine all local maximum and minimum points in the acoustic emission signal X(t), and use cubic spline curves to connect all local maximum points and local minimum points to form an upper envelope and a lower envelope network;

[0055] (1.2) Calculate the mean value of the upper and lower envelopes, denoted as m 1 , put m 1 Separated from the original signal X(t), we get:

[0056] h 1 ...

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Abstract

The invention discloses an Empirical Mode decomposition (EMD)- and Aproximate entropy (ApEn)-based acoustic emission signal characteristic extraction method of a rolling bearing. The acoustic emissionsignal characteristic extraction method comprises the following steps of firstly, decomposing a fault acoustic emission signal of the rolling bearing to a linear combination of a plurality of intrinsic mode functions (IMFs) by an EMD method; secondly, selecting an IMF component which can reflect fault characteristic by a correlation coefficient method, and finding out an IMF component with maximum energy; and finally, performing ApEn calculation. By the acoustic emission signal characteristic extraction method, the acoustic emission signal characteristic of the rolling bearing can be extracted very well to judge whether a fault exists or not.

Description

technical field [0001] The invention relates to a feature extraction method of rolling bearing acoustic emission signals based on EMD and approximate entropy, belongs to the field of nondestructive detection signal analysis, and is applied to bearing fault monitoring and diagnosis. Background technique [0002] 30% of the failures of rotating machinery are caused by bearing failures, and rolling bearings are one of the general components of rotating machinery, and their failure may directly cause mechanical equipment accidents, resulting in casualties and economic losses. The acoustic emission signal has a high frequency, is not easily disturbed by environmental noise, and is sensitive to early failure signals of bearings. Therefore, the application of acoustic emission technology to the fault diagnosis of rolling bearings has its unique advantages. [0003] Acoustic emission signals contain a lot of information related to defects, but they also contain various interference...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04G06K9/00G06K9/62
CPCG01M13/045G06F2218/04G06F2218/08G06F18/211
Inventor 金榕舜王强范昕炜
Owner CHINA JILIANG UNIV
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