Fault diagnosis method and system for rolling bearing based on relative entropy and k-nearest neighbor algorithm

Abstract

The invention discloses a fault diagnosis method for a rolling bearing based on a relative entropy and k-nearest neighbor algorithm. The method comprises the following steps: acquiring vibration datagenerated by a bearing running under various fault states and vibration data of the bearing running under a health state; calculating a relative entropy vector sequence between the vibration data generated by running under the health state and vibration signals generated by running under the fault states according to a division result; taking the fault types as training samples to obtain a trainedclassifying model; acquiring a relative entropy vector between vibration data generated by running under an unknown state and the vibration signals generated by running under the fault states; and taking the acquired relative entropy vector as a test sample of the classifying model, and testing the test sample by using the classifying model to continually diagnose the fault of the rolling bearingto obtain the diagnosis result. Through adoption of the method, the difference between the vibration signals generated by the bearings under different states is measured by adopting the relative entropy; different feature indexes do not need to be calculated and optimally combined; and the distribution of an original vibration signal can be utilized directly.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a method and system for fault diagnosis of rolling bearings based on relative entropy and K nearest neighbor algorithm. Background technique [0002] Rolling bearings are called "mechanical joints" and are one of the most critical structures in rotating machinery. They are widely used in aerospace, machinery manufacturing, automobiles and ships, and their operating status is often decisive for the safe and stable operation of these mechanical equipment. effect. According to statistics, one of the main failure causes of mechanical equipment is rolling bearing failure. Failure of rolling bearings directly causes equipment downtime, and if there is no good monitoring and diagnosis method, it may cause more serious accidents. Therefore, it is very important to carry out fault diagnosis on rolling bearings, which can be used not only to troubleshoot and locate faulty equipme...

AI Technical Summary

Problems solved by technology

However, the spectral analysis method based on Fourier transform also has great limitations: it is only suitable for steady-state signals

In fact, the original signals generated by rotating machinery often have very strong unsteady characteristics. For unsteady signals, the effect of this method is greatly reduced, and even wrong results; profound theoretical knowledge is required

In actual use, if you want to achieve the ideal effect, you need to make a lot of judgments when truncating the original signal to ensure the relative stability of the obtained time domain signal. The judgment algorithm increases the complexity of the algorithm and requires a very deep theoretical foundation and expert knowledge; Extensive expert experience required

Converting to frequency domain signals is very good, but due to the complexity of the bearing structure, the identification of characteristic frequencies in the frequency domain also depends on expert experience

[0005] Although the Fourier transform is very mature, the Fourier transform is only suitable for time-invariant signals, and in practical application scenarios, effective fault diagnosis frequency components are often buried in a large amount of noise and other useless vibration signals. Reliable diagnostic conclusions can only be obtained by analyzing the frequency spectrum combined with expert experience

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