合并所有时间尺度分解方法及其在杆轮故障诊断中的应用
Zhengyang Cheng1, Yu Yang1, Chengcheng Duan1
1College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
集成所有时间尺度分解 (EATD) 在信号分析中有效抑制噪音诱导的模态混合. 这种先进的方法通过保留所有时间尺度分解来提高轮故障诊断.
科学领域:
- 信号处理 信号处理
- 机械工程 机械工程
- 错误诊断 错误诊断 错误诊断 是一个
背景情况:
- 所有时间尺度分解 (ATD) 是一种非参数自适应的信号分解方法.
- ATD 抑制了来自近距离组件频率的模态混合,但与噪声诱导的混合作斗争.
研究的目的:
- 引入一种新的噪声辅助信号分解方法,即集体所有时间尺度分解 (EATD).
- 解决和克服ATD在处理噪音引起的模式混合方面的局限性.
主要方法:
- EATD与ATD整合了互补集体实证模式分解.
- 添加了补充噪声来掩盖干扰,提高了信号分解的准确性.
主要成果:
- EATD成功克服了噪音引起的模式混合.
- 该方法保留了ATD抑制近距离频率混合的能力.
- 通过模拟和形轮故障信号进行验证.
结论:
- 在噪音的存在下,EATD有效地增强了信号分解.
- 拟议的方法显示了准确的轮故障诊断的巨大潜力.
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