Bearing fault diagnosis method based on multi-feature manifold learning and support vector machine

Abstract

The invention discloses a bearing fault diagnosis method based on multi-feature manifold learning and a support vector machine, comprising the following steps: (1) collecting vibration acceleration signals of a rolling bearing at different speeds under all working conditions through an acceleration sensor as training samples; (2) extracting time domain, frequency domain and frequency domain feature parameters of the training samples; (3) carrying out manifold learning to get low-dimensional manifold structures; (4) collecting a vibration acceleration signal of a to-be-tested rolling bearing during rotation through an acceleration sensor as a test sample; (5) extracting time domain, frequency domain and frequency domain feature parameters of the test sample; (6) carrying out manifold learning on the test sample to get a low-dimensional manifold structure; and (7) using a support vector machine classification method to match the test sample with the training samples, and determining the working condition category to which the training sample matching the test sample most belongs as the working condition category of the test sample. Through the method, the accuracy and effectiveness of rolling bearing fault diagnosis are improved.

Description

technical field [0001] The invention relates to bearing mechanical fault diagnosis, in particular to a rolling bearing fault diagnosis method based on multi-feature manifold learning and support vector machine, and belongs to the technical fields of mechanical fault diagnosis and computer artificial intelligence. Background technique [0002] Rolling bearings are key components in rotating machinery, with the advantages of low friction, high precision, low cost, and good interchangeability, and are widely used in various sectors such as metallurgy, petroleum, chemical industry, aerospace, and coal power. However, rolling bearings are also one of the most easily damaged parts in rotating machinery. Rolling bearings have weak impact resistance and are prone to failures under impact. Once the rolling bearing fails, it will easily lead to the paralysis of the entire mechanical system. Therefore, early state monitoring, analysis and diagnosis of rolling bearings are of great sign...

AI Technical Summary

Problems solved by technology

The FFT method can directly find the frequency components of interest from the signal, but it cannot take into account the overall picture and localized information of the signal in the time domain and frequency domain at the same time.

Sparse representation extracts transient features, but due to its large amount of calculation, its development is limited to a certain extent

The result of wavelet transform largely depends on the choice of wavelet base, and the selection of wavelet base is usually difficult

Both the EMD method and the LMD method belong to the recursive mode decomposition method, and both have the disadvantages of mode aliasing, endpoint effect, the influence of sampling frequency, and the inability to correctly separate two components with similar frequencies.

Among the fault diagnosis methods based on artificial intelligence, the artificial neural network is currently widely used, but its disadvantage is that the reasoning process is poorly interpretable, and when the sample to be diagnosed is incomplete (the data is missing), the neural network cannot perform effective reasoning. , it is impossible to use the early characteristics of the fault to diagnose the bearing accordingly

[0005] Because the existing mechanical fault diagnosis methods are flawed to a certain extent, it is difficult to accurately identify and diagnose bearing faults using the existing mechanical fault diagnosis methods

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