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Fault extracting method based on improved wavelet packet threshold denoising and local mean value decomposing

A local mean decomposition and wavelet packet decomposition technology, applied in the field of signal processing, can solve the problems of incomplete operation evaluation of mechanical equipment, incomplete fault feature extraction, misjudgment and missed judgment, etc., to solve the frequency band disorder and filter out random noise. , the effect of low distortion

Inactive Publication Date: 2019-03-22
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0003] Traditionally used methods such as spectrum analysis, envelope analysis, wavelet analysis, morphological filtering, etc., in practical applications, due to the interference of multiple fault sources and background noise, the fault feature extraction is not complete, resulting in misjudgments and missed judgments. Incomplete evaluation of mechanical equipment operation

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  • Fault extracting method based on improved wavelet packet threshold denoising and local mean value decomposing
  • Fault extracting method based on improved wavelet packet threshold denoising and local mean value decomposing
  • Fault extracting method based on improved wavelet packet threshold denoising and local mean value decomposing

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Embodiment Construction

[0034] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0035] Fault Extraction Method Based on Improved Wavelet Packet Threshold Denoising and Local Mean Decomposition

[0036] Such as figure 1 Shown is the fault extraction method based on the improved wavelet packet threshold denoising and local mean value decomposition of the present invention, and the specific steps are as follows.

[0037] Step 1: Select the bearing data from the Department of Electrical Engineering and Computer Science of Case Western Reserve University as the sampling signal x for analysis. According to the correspondence between scale and frequency, that is: in is the pseudo-frequency corresponding to the scale a, f c is the center frequency corresponding to the wavelet packet, and Δt is the sampling period. The pseudo-frequency should cover the entire frequency range of the useful signal, namely: Therefore the minimum frequ...

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Abstract

The invention belongs to the technical field of the signal processing, and relates to a fault extracting method based on improved wavelet packet threshold denoising and local mean value decomposing. The method comprises the following steps: firstly sampling a signal, determining a number of wavelet packet decomposing layers and performing wavelet packet decomposing on the sampled signal, to obtaina decomposed wavelet packet coefficient; and performing quadratic weighting related threshold denoising on the wavelet packet coefficient, reconstructing the denoised wavelet packet coefficient to obtain a denoising signal, and performing LMD decomposing on the denoising signal, to obtain multiple PF components; further calculating a mean square error value, kurtosis and a root-mean-square valueof the PF components, and an energy proportion and a K-L divergence degree of the denoising signal, and according to these index values, to obtain a comprehensive evaluation index, screening out the PF components corresponding to the previous indexes which are as shown in the description; finally, performing envelope demodulation on the PF components, and extracting fault character frequency. Themethod is capable of improving the denoising effect of the sampled signal, highlighting a weak fault character in strong background noise, and effectively improving accuracy of bearing fault diagnosis.

Description

technical field [0001] The invention relates to a fault feature extraction method of a bearing, in particular to a fault extraction method based on improved wavelet packet threshold denoising and local mean value decomposition, and belongs to the technical field of signal processing. Background technique [0002] Bearing is an important part of mechanical equipment, and it is also a component that is prone to failure. Its operating status has a great impact on the overall performance of the machine. When the rolling bearing is abnormal, its vibration signal will also change accordingly. The vibration signal contains a wealth of bearing status information. Therefore, by collecting the vibration signal and analyzing it combined with signal processing technology, fault diagnosis can be realized. The bearing is a periodic rotary motion system, which is affected by equipment and load at the same time. Its vibration signal is a non-stationary modulation signal, especially in the e...

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Application Information

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IPC IPC(8): G01M13/045G06K9/00
CPCG01M13/045G06F2218/06G06F2218/08
Inventor 李双丽刘增力冯镜儒
Owner KUNMING UNIV OF SCI & TECH
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