通过双光谱驱动图域进行适应性特征提取技术,用于轴承故障诊断
Miaorui Yang1, Kun Zhang1, Haihong Tang2
1Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing 100124, China.
ISA transactions
|September 24, 2025
概括
这项研究引入了一种新的双光谱驱动图域方法,用于轴承故障诊断. 该技术增强了振动信号分析,以便更准确,更可靠地检测机械故障.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 数据科学数据科学数据科学
背景情况:
- 机械工业的进步增加了基于振动的故障诊断的复杂性.
- 图形信号处理 (GSP) 为信号特征提取提供了新的方法.
- 传统方法在振动信号中与复杂的调制元件作斗争.
研究的目的:
- 开发一种适应性特征提取技术,用于使用双光谱驱动图域进行轴承故障诊断.
- 为了提高图形信号处理用于机械故障检测的准确性和可解释性.
- 在具有挑战性的工业环境中提高故障诊断的可靠性.
主要方法:
- 使用双光谱构建图形信号以预先去调制调制元件.
- 开发一个最佳的节点识别技术,从图形信号固有值中选择有效的组件.
- 通过数学推导利用图形信号从特征提取中最优的固有值.
主要成果:
- 通过严格的数学推导来证明准确可靠的故障诊断.
- 提高了图形信号处理的准确性和可解释性.
- 验证了拟议方法在轴承故障信号上的实际有效性.
结论:
- 双光谱驱动图域技术为轴承故障诊断提供了有效的解决方案.
- 该方法成功地从振动信号中提取关键特征信息.
- 这种方法为机械系统的先进故障诊断提供了一个有希望的方向.
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