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Bearing fault identification method based on LMD and wavelet de-noising

A wavelet denoising and fault identification technology, which is applied in mechanical bearing testing, machine/structural component testing, mechanical component testing, etc. It can solve the problem of difficult acquisition of signal pulse impact characteristics, and improve the efficiency of fault diagnosis with obvious advantages. , center frequency and bandwidth accurate effect

Inactive Publication Date: 2017-06-30
BEIJING UNIV OF TECH
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Problems solved by technology

[0005] The present invention first collects the vibration signal of the bearing fault on the mechanical fault comprehensive simulation test bench, and then aims at the problem that the pulse shock feature in the signal is difficult to obtain, and proposes a new bearing fault diagnosis method based on the combination of LMD and wavelet denoising, To achieve bearing fault diagnosis

Method used

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  • Bearing fault identification method based on LMD and wavelet de-noising
  • Bearing fault identification method based on LMD and wavelet de-noising
  • Bearing fault identification method based on LMD and wavelet de-noising

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Embodiment 1

[0120] Embodiment 1 of applying the present invention's bearing fault discrimination method based on the combination of LMD and wavelet denoising is as follows:

[0121] 1. Signal collection

[0122] On the MFS mechanical failure comprehensive simulation test bench with the sampling frequency f s =25600 Collect the vibration signals of three types of failures of bearing inner ring, outer ring and ball respectively, and the rotational speed is 1800r / min. The original signal collected is as Figure 5 , 6 , 7 shown.

[0123] 2. LMD decomposition of the signal

[0124] The LMD algorithm can decompose any signal into several instantaneous frequency PF components, and these PF components are obtained by multiplying the envelope signal and the pure frequency modulation signal. LMD is used to decompose the fault vibration signals of the bearing inner ring, outer ring, and ball into 9 layers, and the obtained PF components are as follows: Figure 8 , 9 , 10 shown.

[0125] 3. C...

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Abstract

The invention discloses a bearing fault identification method based on LMD (Local Mean Decomposition) and wavelet de-noising. First, a fault vibration signal of a rolling bearing is decomposed through LMD to get a plurality of natural vibration composition PF components, and suitable PF components are selected according to the principle of maximum kurtosis and a cross correlation coefficient. Then, the selected PF components are de-noised through wavelet de-noising, and high frequency bands are superposed and reconstructed. Finally, experimental results on an MFS (Machine Fault Simulator) show that the method has obvious advantages compared with the traditional bearing fault diagnosis method.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of rotating machinery bearings, and in particular relates to a bearing fault discrimination method based on the combination of LMD and wavelet denoising. Background technique [0002] With the continuous development of my country's economy, rotating machinery is widely used in various aspects of the machinery industry and social life. In rotating machinery equipment, bearings are an extremely important component, and their operating status directly affects the overall The working performance of the mechanical equipment. According to statistics, about 30% of the failures of mechanical equipment using bearings are caused by bearing failures. For example: In June 1992, during the overload experiment of the 600MW No. 3 supercritical thermal power generation unit of the Nankai Power Plant of Kuchimoto Kansai Electric Power Company, due to the failure of the unit bearing and the decrease of the ...

Claims

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Application Information

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IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 高学金温焕然王普李天垚
Owner BEIJING UNIV OF TECH
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