基于二维集团局部平均分解和优化动态最小正方形支向量机器的抗摩擦轴承的故障诊断
Zhengqiang Xiong1, Chang Han2, Guorong Zhang3
1School of Mechanical and Electrical Engineering, Wuhan Business University, Wuhan, 430056, China.
Scientific reports
|October 18, 2023
概括
这项研究引入了一种新的方法来诊断抗摩擦轴承故障,使用二维集合局部平均分解和优化动态最小平方支向量机 (LSSVM). 提出的技术提供了一种有效的解决方案,以确保旋转设备的可靠运行.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 对抗摩擦轴承的有效故障诊断对于旋转机械的可靠运行至关重要.
- 传统方法可能缺乏复杂故障签名所需的效率和准确性.
研究的目的:
- 开发和验证一种针对抗摩擦轴承的新型故障诊断方法.
- 为了提高轴承故障检测中的特征提取和分类准确性.
主要方法:
- 使用二维集合局部平均分解 (BELMD) 来从轴承信号中提取特征.
- 采用优化动态最小正方形支向量机 (LSSVM) 进行故障分类.
- 将拟议的BELMD和优化的动态LSSVM与基线方法进行比较.
主要成果:
- 与二维组合局部平均分解相比,二维组合局部平均分解表现优越.
- 优化的动态LSSVM比传统LSSVM取得了更好的结果.
- 集成的BELMD和优化的动态LSSVM方法在抗摩擦轴承故障诊断方面被证明是有效的.
结论:
- 拟议的方法为诊断抗摩擦轴承故障提供了强大而准确的方法.
- BELMD和优化动态LSSVM是旋转设备健康监测的有希望的技术.
- 这项研究有助于提高工业机械的可靠性和减少停机时间.
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