Rolling bearing fault diagnosis method based on composite multi-scale permutation entropies

A technology for fault diagnosis and rolling bearings, applied in mechanical bearing testing, measuring devices, instruments, etc., can solve problems such as large deviations in permutation entropy values, achieve high classification efficiency, reduce dimensionality, and fast training speed

Active Publication Date: 2015-08-19
ANHUI UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0006] In order to overcome the problem that the permutation entropy value in the multi-scale permutation entropy has a large deviation with the increase of the scale factor, and at the same time to improve the efficiency of fault diagnosis and reduce the influence of human experience factors on the diagnosis result, the present invention provides a method based on

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  • Rolling bearing fault diagnosis method based on composite multi-scale permutation entropies
  • Rolling bearing fault diagnosis method based on composite multi-scale permutation entropies
  • Rolling bearing fault diagnosis method based on composite multi-scale permutation entropies

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Embodiment 1

[0063] see figure 1 In this embodiment, the rolling bearing diagnosis method based on compound multi-scale permutation entropy, Laplacian score and support vector machine includes the following steps:

[0064] Step S11, measuring the vibration signal of the faulty object. For example, the acceleration sensor can be used to measure the vibration of the rolling bearing supporting seat to obtain the vibration acceleration signal.

[0065] Step S12, extracting a plurality of composite multi-scale permutation entropy values ​​from the vibration signal (for example, the vibration acceleration signal).

[0066] Step S13 , performing feature dimensionality reduction on the compound permutation entropy value by using the Laplacian score.

[0067] Step S14, taking the first several composite multi-scale permutation entropy values ​​with lower scores after dimensionality reduction as feature vectors, and dividing them into multiple training samples and multiple testing samples.

[006...

Embodiment 2

[0135] In this embodiment, a rolling bearing is used as a fault object to further illustrate the effectiveness of the diagnosis method. see Figure 12 , Figure 12 It is the time-domain waveform diagram of vibration signals in four states of rolling bearing normal (Normal), rolling element fault (BEF), inner ring fault (IRF) and outer ring fault (ORF).

[0136] Test data The test bearing is 6205-2RSJEM SKF deep groove ball bearing. The single point fault is arranged on the bearing by EDM technology. The fault diameter is 0.5334mm, the depth is 0.2794mm, the bearing speed is 1797r / min, and the signal sampling frequency is 12kHz , the vibration signals of four states of normal, inner ring single-point electric erosion, outer ring single-point electric erosion and rolling element single-point electric erosion are collected, and 20 sets of data are intercepted for each state, and the length of each data is 4096.

[0137] Three samples are randomly selected from each of the above...

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Abstract

The invention discloses a rolling bearing fault diagnosis method based on composite multi-scale permutation entropies, and belongs to the technical field of fault diagnosis. The method comprises the following steps: measuring a vibration signal of a faulty object; extracting composite multi-scale permutation entropies from the vibration signal; reducing the dimension of the composite multi-scale permutation entropies with use of a Laplacian score; taking the first multiple composite multi-scale permutation entropies with low scores after dimension reduction as fault feature vectors and dividing the fault feature vectors into multiple training samples and multiple test samples; inputting the multiple training samples into a multi-fault classifier established based on a support vector machine to perform learning so as to classify the test samples; and identifying the working mode and the fault type of the faulty object according to the classifying result. According to the fault diagnosis method disclosed by the invention, feature extraction is highly innovative, and the degree of identification is high in the process of fault mode identification.

Description

technical field [0001] The present invention relates to the technical field of rolling bearing fault diagnosis, in particular to a feature selection based on composite multi-scale permutation entropy (Composite multi-scale permutation entropy, CMPE), Laplacian score (Laplacian score, LS) and support vector machine (Support vector machine). vector machine (SVM) rolling bearing fault diagnosis method. Background technique [0002] Due to the complexity of the mechanical system, friction, vibration and load will inevitably occur during the operation of the equipment, and the vibration signal of the system often exhibits nonlinear behavior. Therefore, the nonlinear analysis method has more unique advantages than the linear analysis method in extracting fault features, and can extract fault feature information hidden in vibration signals that cannot be extracted by other methods. In recent years, many nonlinear analysis methods, such as fractal, approximate entropy, sample entro...

Claims

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Application Information

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IPC IPC(8): G01M13/04G01H17/00G06K9/62
Inventor 郑近德潘海洋徐培民张俊
Owner ANHUI UNIVERSITY OF TECHNOLOGY
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