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Rolling bearing diagnosis method based on multi-view feature fusion

A rolling bearing and feature fusion technology, applied in the field of rolling bearing diagnosis based on multi-view feature fusion, can solve problems such as redundant and unfavorable fault diagnosis, and achieve the effect of reducing dependence

Inactive Publication Date: 2021-01-01
SOUTHEAST UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

Although multi-view capabilities are highly complementary, these features are often redundant, which is not conducive to fault diagnosis

Method used

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  • Rolling bearing diagnosis method based on multi-view feature fusion
  • Rolling bearing diagnosis method based on multi-view feature fusion
  • Rolling bearing diagnosis method based on multi-view feature fusion

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Embodiment Construction

[0024] figure 1 It is a flowchart of multi-view feature extraction and fusion method. Below in conjunction with accompanying drawing and embodiment, the technical solution of the present invention is described further.

[0025] Step 1: Use the rolling bearing public dataset from Case Western Reserve University. like figure 2 As shown in (a), the test bench consists of a 2-horsepower motor, torque sensor / encoder, dynamometer, and control electronics (not shown), which are arranged on the left, middle, and right sides of the test bench, respectively. image 3 The waveform diagrams of vibration signals of rolling bearings in four 0.007-inch crack states are shown in the middle. The feature extraction of vibration signal comes from three aspects: time domain, frequency domain and time-frequency domain. The characteristics of the vibration signal are extracted from multiple perspectives. On the basis of the spectrum diagram and the envelope spectrum diagram, the time-frequency...

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Abstract

The invention studies a feature fusion process of a rolling bearing vibration signal multi-view feature set, and provides feature fusion based on random forest feature selection and auto-encoder dimension reduction. A rolling bearing diagnosis method disclosed by the invention comprises the steps of: 1) extracting rolling bearing multi-view characteristics by using statistical characteristics anda time sequence signal spectrum analysis method; 2) outputting feature importance of high-dimensional features by using a random forest, and eliminating invalid features based on the feature importance to reduce feature dimensions; and 3) performing nonlinear dimensionality reduction on redundant features with the same feature importance in the feature set by using an auto-encoder so as to obtaina small-redundancy low-dimensional feature set capable of clearly expressing the bearing state difference.

Description

technical field [0001] The invention belongs to the field of bearing vibration signal processing, and relates to a rolling bearing diagnosis method based on multi-view feature fusion. Background technique [0002] Rolling bearings are one of the key components in wind turbine drive trains. Due to the harsh working environment of wind turbines, rolling bearing failures often occur. According to statistics, 30% of rotating machinery failures are caused by rolling bearings, and 80% of wind turbine gearbox failures are caused by bearing failures. Therefore, bearing fault diagnosis is crucial for efficient and reliable operation of wind turbines. Traditionally, the fault diagnosis of rolling bearings in wind turbines is based on frequency spectrum analysis of vibration signals. Its key technology is to extract the fault characteristic frequency from the noise signal. Spectrum analysis methods include Fourier transform, Hilbert transform and some joint time-frequency analysis ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G01M13/045
CPCG01M13/045G06F2218/08G06F2218/12G06F18/2411G06F18/24323
Inventor 邓艾东孙文卿邓敏强朱静史曜炜王煜伟马天霆王姗刘洋程强
Owner SOUTHEAST UNIV
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