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Improved variation mode decomposition diagnosis method for engine main shaft bearing fault diagnosis

A technology of variational mode decomposition and spindle bearings, which is applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., can solve problems such as difficulty in breaking through the limitations of window functions, cross-interference, and hindering effective analysis of signals

Inactive Publication Date: 2019-04-26
WENZHOU UNIVERSITY
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AI Technical Summary

Problems solved by technology

However, for multi-component signals, the Wigner-Ville distribution has serious cross-interference, which hinders the effective analysis of the signal
Wavelet analysis is based on Fourier transform, the limitation of window function is difficult to break through, and it is impossible to accurately describe the change of frequency with time

Method used

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  • Improved variation mode decomposition diagnosis method for engine main shaft bearing fault diagnosis
  • Improved variation mode decomposition diagnosis method for engine main shaft bearing fault diagnosis
  • Improved variation mode decomposition diagnosis method for engine main shaft bearing fault diagnosis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment example 2

[0098] Implementation case 2: Fault diagnosis of the outer ring of the engine main shaft bearing

[0099] According to the characteristic frequency formula of the bearing outer ring fault, the frequency of the bearing outer ring fault can be calculated:

[0100]

[0101] In formula (11), f s Indicates the sampling frequency, n indicates the number of rolling elements, d indicates the diameter of the rolling elements, D indicates the diameter of the pitch circle, and α indicates the contact angle of the bearing. The bearing model of this experiment is ER-12K, and the outer ring has early pitting corrosion failure. During the signal acquisition process, the sampling frequency is 25600Hz; the bearing is unloaded and the running speed is 2400r / min; the number of sampling points is 32768. The number of bearing rolling elements is 8, the rolling element diameter is 7.9375mm, and the pitch circle diameter is 33.4772mm. Select sample N=12288 as the original signal, and calculate...

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Abstract

The invention discloses an improved variation mode decomposition diagnosis method for engine main shaft bearing fault diagnosis. Firstly, a fault original signal is input into inherent time scale decomposition, the signal is decomposed into a plurality of inherent rotation components and a residual term, the residual term is filtered out, and key components of the signal are completely retained while the original signal is denoised; secondly, each inherent rotation component is subjected to variation mode decomposition, the optimal component in each group of IMFs is selected according to the kurtosis principle, and the signal is reconstructed; finally, the reconstructed signal is subjected to hilbert envelope transformation to diagnose the fault types of bearings. According to the method,on the one hand, the signal is denoised by means of inherent time scale decomposition, and the signal-to-noise ratio is increased; on the other hand, each inherent rotation component is self-adaptively decomposed to be close to the respective center frequency by means of variable mode decomposition, and the optimal components are selected to reconstruct the signal. The method has good denoising capability, completely retains fault information, and has strong fault diagnosis advantages.

Description

technical field [0001] The invention belongs to the field of automobile fault diagnosis, and specifically refers to an improved variational mode decomposition diagnosis method for fault diagnosis of engine main shaft bearings. Background technique [0002] The engine is called the heart of the car, and its performance affects all the running indicators of the car in an all-round way. Numerous bearings are used in the engine to support the drive train. Among them, the main shaft bearing is a key component of the engine, and its reliability is very important to the comfort and safety of driving. With the improvement of engine performance, the working conditions of main shaft bearings are becoming more and more severe, so the probability of failure is also greater. According to statistics, the main shaft bearing is one of the components with the highest failure probability of the engine, so the diagnosis of the main shaft bearing failure of the engine is of great significance...

Claims

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Application Information

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IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 向家伟王璐
Owner WENZHOU UNIVERSITY
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