Rolling bearing fault feature extraction method based on independent component analysis and cepstrum theory

Abstract

The invention provides a rolling bearing fault feature extraction method based on an independent component analysis and cepstrum theory. The rolling bearing fault feature extraction method comprises the steps of acquiring a vibration acceleration testing signal of a rolling bearing by using an acceleration sensor; decoupling and separating the vibration acceleration testing signal by using FastICA based on negentropy maximization; selecting a separated signal capable of representing fault feather information to the maximum extent; carrying out cepstrum analysis on the selected separated signal, and drawing a cepstrum chart; observing whether the cepstrum chart has a fault feature frequency or an obvious peak value at a frequency multiplication position, and furthermore, judging whether the rolling bearing has a fault. By using the rolling bearing fault feature extraction method, the feature information of a fault signal of the rolling bearing can be effectively recognized from a complex sideband signal, a periodical fault component in a sideband can be conveniently extracted, the fault information is remarkably enhanced, the fault diagnosis precision is greatly improved, the fault diagnosis time period is shortened, and the spectral analysis difficulty is simplified; in addition, the rolling bearing fault feature extraction method is easy to realize and good in real-time property.

Description

technical field [0001] The invention relates to a rolling bearing fault feature extraction method, in particular to a rolling bearing fault feature extraction method based on independent component analysis and cepstrum theory. Background technique [0002] Rolling bearings are important components commonly used in rotating machinery, and their faults are one of the most common faults in transmission systems. Rolling bearing faults not only damage the running accuracy of rotating machinery, but also cause the entire machine to shut down or even cause major safety accidents in severe cases. Therefore, the detection and diagnosis of bearing faults is very important. [0003] At present, the feature extraction of bearing faults based on vibration signals has been extensively studied. However, the vibration signals of moving parts such as bearings and gears are often coupled together in the entire frequency band, which affects the detection of bearing faults. Secondly, usually...

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Problems solved by technology

However, the vibration signals of moving parts such as bearings and gears are often coupled together in the entire frequency band, which affects the detection of bearing faults

Secondly, usually the measured signal also includes the components formed by the convolution of the system transfer function composed of rolling inner and outer rings and other components, which also greatly increases the difficulty of fault feature extraction

Furthermore, when the rolling bearing fails, the number of modulation sidebands increases and the amplitude increases, which makes it difficult to extract fault information, increase the difficulty of spectrum analysis, and the accuracy is not high enough

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