Rolling bearing fault diagnosis method based on cyclic correlation entropy

Abstract

The invention provides a rolling bearing fault diagnosis method based on cyclic correlation entropy. The rolling bearing fault diagnosis method includes the steps that bearing data of a rolling bearing under different fault states are collected correspondingly, and the fault characteristic frequency of each fault is calculated; the bearing data are subjected to windowing treatment, and divided into a plurality of data blocks, a gaussian kernel function is selected as a kernel function of the cyclic correlation entropy, and average cyclic correlation entropy of each data block and Fourier transformation results of Fourier transformation mean values of the average cyclic correlation entropy in an f domain are calculated; and according to projections of the Fourier transformation results in an alpha domain, spectrum distribution results of fault signals are obtained, and according to the spectrum distribution results of the fault signals and the fault characteristic frequency of the bearing data under each fault state, fault types of the bearing are judged. According to the rolling bearing fault diagnosis method based on the cyclic correlation entropy, fault bearing signals under thenon-gaussian noise environment, especially under the impact noise interference condition can be better processed, and the requirements of engineering practice can be better met.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a method for fault diagnosis of rolling bearings based on cyclic correlation entropy. Background technique [0002] Rolling bearings are widely used in the field of mechanical engineering, and rolling bearings have a high failure rate under high-strength and high-density working conditions. Therefore, it is particularly important to monitor the state of rolling bearings through fault diagnosis methods and replace faulty bearings in real time to ensure the normal operation of mechanical equipment. [0003] When a fault occurs on the surface of a bearing component, it will collide with the surface of other components during the rotation process, thus generating a series of modulated impact signals. This kind of fault information is distributed in a certain frequency band of the signal. If the fault frequency band information with high signal-to-noise ratio can be o...

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Problems solved by technology

However, in actual research, it is found that the bearing vibration signals in engineering practice are often accompanied by a large amount of non-Gaussian noise, and the stability of the frequency band optimization algorithm will be disturbed by such non-Gaussian noise, resulting in inaccurate fault diagnosis results.

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