Method for extracting fault features of rolling bearing based on equal-angle double sampling

Abstract

The invention discloses a method for extracting fault features of a rolling bearing based on equal-angle double sampling. The method comprises the steps of: acquiring an envelope signal of a resonancefrequency band signal, calculating a rotation speed by using a key-phase signal, determining whether the rotation speed is stable by setting a threshold value, performing primary equal-angle re-sampling on an envelope signal of a resonance frequency band of a variable rotation speed signal exceeding a threshold value range by utilizing phase information, regarding the envelope signal of the resonance frequency band as an equal-angle re-sampling signal of the resonance frequency band when fluctuations of the rotation speed do not exceed the threshold value, and calculating an envelope signal of the equal-angle re-sampling signal; and performing secondary narrow-band filtering on the equal-angle re-sampling signal while only reserving feature orders of interest, calculating phases of a narrow-band signal, performing secondary re-sampling on the narrow-band signal by utilizing the calculated phases, and realizing extraction of the fault features of the rolling bearing by means of an envelope order spectra of a double sampling signal. The method can obviously suppress the aliasing phenomenon of feature orders caused by random sliding of a rolling body, realize the aggregation of the fault feature order energy, and enhance the fault features.

Description

technical field [0001] The invention relates to the technical field of rolling bearing fault diagnosis, in particular to a method for extracting rolling bearing fault features based on equal-angle double sampling. Background technique [0002] As an important part of rotating machinery, rolling bearings are often accompanied by major economic losses and even casualties once they fail. Therefore, health monitoring and fault diagnosis of rolling bearings are of great significance. As an effective fault diagnosis method, vibration analysis has been widely used in the fault diagnosis of rolling bearings. How to enhance the fault features in the presence of interference such as strong background noise so as to effectively extract the fault features, and then accurately judge the fault characteristics of the bearing? Health status has been extensively studied by many scientific researchers. [0003] In recent years, the fault feature extraction technology of rolling bearings has ...

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Problems solved by technology

[0004] However, the traditional order tracking technology can only compensate for speed fluctuations. When the bearing is in the acceleration process or the load changes, the rolling elements often slip randomly. For rolling bearings with relative sliding, an angle domain resampling is required. It is difficult to effectively compensate for this random sliding, which in turn leads to aliasing of the fault characteristic order in the fault envelope order spectrum, which affects the diagnosis effect

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