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Bearing fault diagnosis method based on variation modal decomposition and wavelet singular decomposition

A fault diagnosis and bearing technology, applied in the direction of mechanical bearing testing, mechanical component testing, machine/structural component testing, etc., can solve the problem of consuming the energy of the diagnostician, and achieve the effect of improving the accuracy and success rate

Inactive Publication Date: 2018-04-06
BEIJING INFORMATION SCI & TECH UNIV
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Problems solved by technology

Due to the influence of noise, structural deformation and other factors, the collected vibration signals generally have the characteristics of non-stationary, nonlinear, strong noise and big data. At the same time, big data information processing will consume a lot of energy for the diagnostician. Rapid development, as long as the appropriate information processing method is selected, more useful information can be accurately extracted from it

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  • Bearing fault diagnosis method based on variation modal decomposition and wavelet singular decomposition
  • Bearing fault diagnosis method based on variation modal decomposition and wavelet singular decomposition
  • Bearing fault diagnosis method based on variation modal decomposition and wavelet singular decomposition

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Embodiment Construction

[0022] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0023] The wavelet transform method is a time-frequency analysis method developed in the mid-1980s, which is superior to Fourier transform methods such as discrete cosine transform (DCT for short). The wavelet transform method has a multi-resolution analysis function and is known as a mathematical microscope. Considering the decomposition characteristics of the bearing vibration signal on different scales after wavelet transform, the new threshold function can well overcome the shortcomings of constant error, discontinuity at the threshold and complex parameter calculation. In order to better improve the denoising effect, the new threshold function and layered threshold are combined for wavelet threshold denoising, which has a goo...

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Abstract

The invention discloses a bearing fault diagnosis method based on variation modal decomposition and wavelet singular decomposition. The method includes: subjecting de-noising preprocessing on an acquired signal by using an optimal wavelet packet basis and a singular value decomposition method; after the signal is de-noised, extracting the intrinsic mode function of the signal by using a variationmodal decomposition algorithm; calculating the standard deviation of the obtained intrinsic mode function to construct a feature vector and using the obtained feature vector to diagnose a bearing fault.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a bearing fault diagnosis method. Background technique [0002] The collection of motor fault information mainly refers to the acquisition of relevant physical quantities of the working state of mechanical equipment, such as vibration, noise, speed, humidity, temperature, flow, etc. Usually, mechanical faults are more obvious in terms of vibration signals, so the current method of fault diagnosis is mainly to analyze vibration signals. In the process of fault information collection, the sensor is a component that directly obtains information and converts it into the required form for output. Its detection accuracy, reliability, and stability have a great impact on the quality of the acquired signal. The development trend of fault information collection is telemetry technology, acoustic emission detection technology, optical fiber sensing technology and other f...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 王占刚朱希安杨昊
Owner BEIJING INFORMATION SCI & TECH UNIV
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