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Rolling bearing fault feature extraction method based on CEEMD and FastICA

A technology of fault characteristics and extraction methods, applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc.

Inactive Publication Date: 2019-08-20
KUNMING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this method needs to meet the condition that the number of observed signals is greater than the number of source signals, and it needs to be analyzed in combination with other signal decomposition methods

Method used

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  • Rolling bearing fault feature extraction method based on CEEMD and FastICA
  • Rolling bearing fault feature extraction method based on CEEMD and FastICA
  • Rolling bearing fault feature extraction method based on CEEMD and FastICA

Examples

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Embodiment 1

[0070] Embodiment 1: as figure 1 As shown, a rolling bearing fault feature extraction method based on CEEMD and FastICA, first uses the CEEMD algorithm to decompose the original fault vibration signal x(t) into several IMF components of different frequencies, and then selects the corresponding IMF components according to the kurtosis criterion for reconstruction Get the observed signal G 1 (t), the remaining IMF components are reconstructed to obtain the virtual noise channel signal G 2 (t); use the FastICA algorithm to convert the observed signal G 1 (t) and virtual noise channel signal G 2 (t) Perform unmixing and denoising processing to obtain the signal Z(t) after joint noise reduction; then use the Teager energy operator to demodulate the signal Z(t) after joint noise reduction to obtain the demodulated signal W(n) ; Finally, perform FFT transformation on the demodulated signal W(n), analyze the spectral characteristics of the transformed signal Y(t), extract the fault...

Embodiment 2

[0077] Embodiment 2: In this embodiment, the fault signal of the outer ring of the rolling bearing is analyzed, and the fault signal of the inner ring of the bearing is decomposed by CEEMD to several IMF components; the kurtosis value of each IMF component signal is calculated separately, and the kurtosis value is selected to be larger The first two component signals are reconstructed to obtain the observed signal, and the remaining IMF components construct the virtual noise channel signal; the observed signal and the virtual channel signal are unmixed using the FastICA algorithm to obtain the joint noise-reduced signal; the noise-reduced The signal is demodulated by the Teager-Kaiser energy operator to obtain the demodulated signal; FFT is performed on the demodulated signal to obtain the signal spectrum as shown in Figure 7 shown by Figure 7 It can be clearly obtained that the fundamental frequency of the inner ring fault of the rolling bearing is 105.5 Hz (close to the op...

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Abstract

The invention relates to a rolling bearing fault feature extraction method based on CEEMD and FastICA and belongs to the technical field of fault diagnosis and signal processing and analysis. The method comprises the following steps that: vibration signals are decomposed into IMF components with different frequencies through the CEEMD algorithm, corresponding IMF components are selected accordingto kurtosis criteria so as to be reconstructed into observation signals, and the residual IMF components are reconstructed into virtual noise channel signals; unmixing and denoising processing is performed on the observation signals and the virtual noise channel signals through the FastICA algorithm; demodulation processing is performed on the denoised signals through the Teager energy operator; and FFT (fast Fourier transformation) is performed on the demodulated signals, the frequency spectrum characteristics of the transformed signals are analyzed, the fault characteristic frequencies of the signals are extracted, and a fault diagnosis result is obtained. With the method adopted, the problem of fault information loss during a denoising process and the problem that noises cannot be completely removed due to modal aliasing can be solved; fault fundamental frequencies and frequency multiplication information can be extracted clearly and accurately; and the fault diagnosis result can beobtained.

Description

technical field [0001] The invention relates to a rolling bearing fault feature extraction method based on CEEMD and FastICA, which belongs to the field of fault diagnosis technology and signal processing and analysis technology. Background technique [0002] Rolling bearings play an important role in our production and life. They are important parts in the field of machinery manufacturing and one of the mechanical parts with a high damage rate. The operating state of rolling bearings determines the working efficiency of industrial systems to a large extent. In severe cases, it may even lead to huge economic losses and casualties in industrial production, so it is extremely important to monitor its operating status to ensure normal industrial production. However, in actual engineering practice, the vibration signals monitored and collected are not only linear as the theoretical illusion. The actual vibration signals of rolling bearings usually have two characteristics: linea...

Claims

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Application Information

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IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 吴建德吴涛王晓东黄国勇范玉刚邹金慧冯早
Owner KUNMING UNIV OF SCI & TECH
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