Rolling bearing fault feature extraction method based on CEEMD and FastICA

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...

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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

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