Xiong Zhengqiang, Han Chang, Zhang Guorong
School of Mechanical and Electrical Engineering, Wuhan Business University, Wuhan, 430056, China.
School of Electronic Information, Wuhan University, Wuhan, 430072, China.
Sci Rep. 2023 Oct 18;13(1):17784. doi: 10.1038/s41598-023-44996-6.
In order to ensure the normal operation of rotating equipment, it is very important to quickly and efficiently diagnose the faults of anti-friction bearings. Hereto, fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine (LSSVM) is presented in this paper. Bi-dimensional ensemble local mean decomposition, an extension of ensemble local mean decomposition from one-dimensional signal processing to Bi-dimensional signal processing, is used to extract the features of anti-friction bearings. Moreover, an optimized dynamic LSSVM is used to fault diagnosis of anti-friction bearings. The experimental results show that Bi-dimensional ensemble local mean decomposition is superior to Bi-dimensional local mean decomposition, optimized dynamic LSSVM is superior to traditional LSSVM, and the proposed Bi-dimensional ensemble local mean decomposition and optimized dynamic LSSVM method is effective for fault diagnosis of anti-friction bearings.
为确保旋转设备的正常运行，快速高效地诊断减摩轴承故障非常重要。为此，本文提出了基于二维总体局部均值分解和优化动态最小二乘支持向量机（LSSVM）的减摩轴承故障诊断方法。二维总体局部均值分解是总体局部均值分解从一维信号处理到二维信号处理的扩展，用于提取减摩轴承的特征。此外，优化动态LSSVM用于减摩轴承的故障诊断。实验结果表明，二维总体局部均值分解优于二维局部均值分解，优化动态LSSVM优于传统LSSVM，所提出的二维总体局部均值分解和优化动态LSSVM方法对减摩轴承故障诊断是有效的。
