Bearing fault detection algorithm combining genetic algorithm optimization parameters and machine vision

A technology of fault detection and machine vision, applied in the field of analysis and measurement control, to achieve the effect of clear weak features and accurate classification results

Inactive Publication Date: 2018-12-18
CHINA JILIANG UNIV
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Problems solved by technology

Including bearing internal fault detect

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  • Bearing fault detection algorithm combining genetic algorithm optimization parameters and machine vision
  • Bearing fault detection algorithm combining genetic algorithm optimization parameters and machine vision
  • Bearing fault detection algorithm combining genetic algorithm optimization parameters and machine vision

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Embodiment Construction

[0030] In order to complete bearing fault detection, the following describes specific implementations of the present invention in conjunction with the accompanying drawings.

[0031] The invention is an algorithm for detecting faults inside and on the end face of a bearing combined with genetic algorithm optimization parameters and machine vision. figure 1 Check out the flowchart for its system.

[0032] The algorithm system first detects the fault inside the bearing, the method is as follows:

[0033] Firstly, the energy of each sub-frequency band after the vibration signal wavelet packet decomposition is used as the fault detection feature, and the wavelet packet decomposition coefficient is obtained Among them, d is the number of wavelet packet decomposition layers, and k is the number of signal sub-bands. Reconstructing the wavelet packet decomposition coefficients, the total signal can be expressed as Therefore, the signal energy E of each sub-band is obtained, and t...

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Abstract

The invention provides a bearing interior and end face fault detection algorithm combining genetic algorithm optimization parameters and machine vision, and belongs to the technical field of analysisand measurement control. The bearing interior and end face fault detection algorithm comprises bearing interior fault detection and bearing end face fault detection. Firstly, the energy of each sub-band after vibration signal wavelet packet decomposition is used as a fault detection feature, and multi-modal fusion parameters are optimized in a training process of a support vector machine (SVM) byusing a genetic algorithm (GA) to achieve the bearing interior fault detection; and image collection is performed on an end face of a bearing, filtering processing is performed on a collected image byusing the machine vision technology, edge detection is completed by using a Canny operator, an optimal threshold value of a circular ring area is calculated by an Otsu algorithm to realize defect segmentation, and finally the bearing end face fault detection is realized. The accuracy of bearing fault detection is improved to a certain extent.

Description

technical field [0001] The invention relates to a bearing internal and end surface fault detection algorithm combined with genetic algorithm optimization parameters and machine vision, belonging to the technical field of analysis and measurement control. Background technique [0002] In recent years, major safety accidents caused by internal or surface cracks of special equipment such as bearings have occurred from time to time. The fault diagnosis method of bearings has always been one of the key development technologies in mechanical fault diagnosis. Therefore, the automatic detection of bearing faults is of great significance. [0003] Nowadays, for the internal fault detection of bearing faults, most of them choose to use machine learning methods. In the process of bearing fault detection, due to the complex working environment and strong background noise, the sample size of the fault signal is small, and the effect of data analysis by SVM is generally better. However,...

Claims

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Application Information

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IPC IPC(8): G01M13/04G06N3/12
CPCG01M13/045G06N3/126
Inventor 陈亮徐玮鑫金尚忠张淑琴徐时清刘泽森孟庆阳华静谷振寰
Owner CHINA JILIANG UNIV
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