A rolling bearing fault diagnosis method based on IAGA-SVM

A rolling bearing and fault diagnosis technology, which is applied in mechanical bearing testing, gene models, special data processing applications, etc., can solve problems such as low classification accuracy and low efficiency, and achieve clear and accurate expression, optimal classification and prediction effects, and fault diagnosis Reasonable and effective effect of the model

Inactive Publication Date: 2019-06-18
KUNMING UNIV OF SCI & TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In recent years, artificial intelligence methods such as expert systems, neural networks, and fuzzy theory have been widely used in the field of fault diagnosis of rotating machinery, but there are still problems such as low efficiency and low classification accuracy.

Method used

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  • A rolling bearing fault diagnosis method based on IAGA-SVM
  • A rolling bearing fault diagnosis method based on IAGA-SVM
  • A rolling bearing fault diagnosis method based on IAGA-SVM

Examples

Experimental program
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Effect test

Embodiment 1

[0053] Embodiment 1: as figure 1 As shown, a rolling bearing fault diagnosis method based on IAGA-SVM first adopts the method of wavelet change to extract the features of the rolling bearing fault data, and normalizes them to form training samples and train to obtain the SVM model; then use the improved The adaptive genetic algorithm optimizes the penalty factor C and the kernel function parameter γ of the SVM model to obtain the optimized SVM model, namely the SVM fault diagnosis model, and finally uses the SVM fault diagnosis model to diagnose the rolling bearing fault.

[0054] Because the process of data collection often contains inevitable noise and invalid information, if the original data is used without processing, the result will always be unsatisfactory. Therefore, in order to obtain better experimental results, the present invention uses wavelet transform to perform feature extraction on the original signal after denoising, so as to obtain effective information in t...

Embodiment 2

[0091] Embodiment 2: In this embodiment, specific rolling bearing fault signals are extracted, specifically, wavelet packets are selected to perform feature extraction on denoised signals. The 3-layer decomposition of the wavelet packet will obtain 8 frequency bands, and then reconstruct the signal, and the normalized energy values ​​of each frequency band form a feature vector. The energy of each frequency band in the four states is shown in Table 1:

[0092] Table 1 Energy table of each frequency band in four states

[0093]

[0094]

[0095] After normalization, figure 2 The energy distribution of each frequency band in the four states is intuitively expressed.

[0096] From figure 2 It can be seen that the energy distribution diagrams corresponding to the four states of rolling bearings are quite different, and this difference in energy distribution is very helpful for the identification of fault types.

[0097] Among them, the reconstruction signals of the eig...

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Abstract

The invention relates to a rolling bearing fault diagnosis method based on IAGA-SVM, and belongs to the technical field of mechanical engineering automation. The method comprises the following steps:firstly, carrying out feature extraction on fault data of the rolling bearing by adopting a wavelet transformation method, and carrying out normalization processing on the fault data to form a training sample and training to obtain an SVM model; And then utilizing an improved self-adaptive genetic algorithm to optimize penalty factors and kernel function parameters of the SVM model, obtaining an optimized SVM model, namely an SVM fault diagnosis model, and finally utilizing the SVM fault diagnosis model to carry out fault diagnosis on the rolling bearing. According to the method, the fault diagnosis process is clearly and accurately expressed, the fault diagnosis model is reasonable and effective, and the classification prediction effect is improved.

Description

technical field [0001] The invention relates to an IAGA-SVM-based rolling bearing fault diagnosis method, which belongs to the technical field of mechanical engineering automation. Background technique [0002] With the improvement of automation level, the performance requirements of mechanical equipment also increase. Rolling bearings are the main components of rotating machinery and equipment. Due to their long-term work in harsh natural environments and being affected by various uncertain factors, the result is that the reliability of rolling bearings is the worst in the entire track engineering machinery and equipment. Once a failure occurs, it will cause delays in the construction period and economic losses to some extent, and it is very likely to cause casualties in severe cases. Therefore, the fault diagnosis technology of mechanical equipment has received more and more attention and research. If judgments can be made in a timely manner and fed back to relevant staf...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06K9/62G06N3/12G01M13/04
Inventor 王海瑞靖婉婷林雅慧
Owner KUNMING UNIV OF SCI & TECH
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