旋转机器的故障诊断使用内核邻居保护嵌入和修改的稀有贝叶斯分类模型
Lixin Lu1, Weihao Wang1, Dongdong Kong1
1School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200444, China.
这项研究引入了一种新的功能融合方法,用于旋转机械故障诊断. 内核社区保护嵌入 (KNPE) 显著提高了诊断准确性,提高了机器可靠性.
科学领域:
- 机械工程 机械工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 旋转机械故障诊断对于工业运作至关重要.
- 现有的方法需要改进的特征融合技术,以获得更高的准确性.
研究的目的:
- 开发和验证用于旋转机械故障诊断的先进特征融合方法.
- 提高稀疏贝叶斯分类 (SBC) 模型在故障检测中的性能.
主要方法:
- 使用了一个修改后的稀疏贝叶斯分类 (Standard_SBC) 模型.
- 为功能融合提出了内核社区保护嵌入 (KNPE) 的建议.
- 通过滚动轴承和旋转轴的故障诊断案例研究验证了系统.
主要成果:
- 在KNPE的特征融合方法中,表现出了卓越的性能.
- 从93.96%到99.92% (案例1) 和98.67%到99.64% (案例2) 的准确度得到了改进.
- 在比较分析中表现优于核心主要组件分析 (KPCA) 和相关性矢量机 (RVM).
结论:
- 拟议的KNPE方法为旋转机械故障诊断中的特征融合提供了可靠的方法.
- 这项研究为先进的故障诊断系统提供了基础.
- 提高精度有助于提高工业机器的维护和可靠性.
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