Fault Diagnosis of Rolling Bearings Based on Acoustics and Vibration Engineering

被引：2

作者：

Guo, Xinwen ^{[1
]}

机构：

[1] Shenzhen Inst Technol, Coll Continuing Educ, Shenzhen 518116, Peoples R China

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Accuracy; adaptive algorithm; classification algorithms; digital signal processing; electromechanical devices; fault detection; feature detection; image processing; mechanical bearings; rolling bearings; EMISSION; SIGNAL;

D O I：

10.1109/ACCESS.2024.3466154

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Currently, the fault diagnosis and maintenance of rolling bearings have become an urgent problem to be solved. A fault diagnosis method based on feature extraction and word bag model was designed based on the theories of acoustics and vibration engineering science. At the same time, the traditional word bag model was optimized, and a rolling bearing fault diagnosis method based on the adaptive extended word bag model was designed. This method mainly expands the word bag model into a 3-layer structure and constructs codebooks for the feature vectors of each layer. The results indicate that the fault diagnosis method for rolling bearings designed in the study has high diagnostic accuracy and stability, providing reliable technical support for the normal operation and safe maintenance of mechanical equipment.

引用

页码：139632 / 139648

页数：17

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