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

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,...

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Problems solved by technology

Including bearing internal fault detection and bearing end face fault detection

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