Bearing fault classification method based on wavelet mutation particle swarm optimization

Abstract

The invention discloses a bearing fault classification method based on wavelet mutation particle swarm optimization. The method comprises the following steps: S1, acquiring bearing original data, andextracting energy features of the bearing original data; S2, inputting the energy features into a least squares support vector machine classification model based on the wavelet mutation particle swarmoptimization; S3, obtaining a fault classification result. The method provided by the invention can improve classification precision of detection of bearing faults, and provides convenience for bearing operation and detection.

Description

technical field [0001] The invention relates to the field of bearing fault classification, in particular to a bearing fault classification method based on wavelet variation particle swarm algorithm. Background technique [0002] With the development of the times and the prosperity of the economy, my country's bearing industry has developed rapidly. The variety of bearings has changed from few to many, the product quality and technical level have ranged from low to high, and the scale of the industry has grown from small to large. It has formed a complete product category and a production layout. More reasonable professional production system. Bearing is an important part of contemporary industrial machinery and equipment. Its main function is to support the mechanical rotating body, reduce the friction coefficient during its movement, and ensure its rotation accuracy. However, there are still many problems in my country's mechanical bearing manufacturing. At present, the pro...

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Problems solved by technology

[0003] At present, my country's design and manufacturing technology basically comes from the imitation of foreign technology, and the level of manufacturing technology is low. my country's bearing industry manufacturing technology and process equipment technology develop slowly, and the CNC rate of turning is low.

These reasons lead to low bearing process capability index, poor consistency, large dispersion of product processing size, and unstable internal quality of the product, which affects the accuracy, performance, life and reliability of the bearing, but the bearing plays an essential role in mechanical operation. Therefore, it is an indispensable research to find the faults in the bearings in time and distinguish the normal bearings from all kinds of faulty bearings.

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