一种具有改进增强外频谱的适应性解卷方法及其用于轴承故障特征提取的应用
Fengxia He1, Chuansheng Zheng1, Chao Pang2
1School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
一种新的方法,改进的封面光谱-最大二次循环静止盲解卷 (IES-CYCBD),有效地将合轴承故障特征分开. 这种技术提高了复杂故障的诊断准确性,即使在杂的条件下.
科学领域:
- 机械工程 机械工程
- 振动分析 振动分析
- 错误诊断 错误诊断 错误诊断 是一个
背景情况:
- 复杂的轴承故障经常表现出合的振动信号,使准确的诊断变得复杂.
- 传统方法在高噪音和复合故障场景中难以进行特征分离.
研究的目的:
- 开发一种新的方法来分离复杂轴承系统中的合故障特征.
- 在具有挑战性的条件下提高轴承故障诊断的准确性和可靠性.
主要方法:
- 通过将共振带集成到循环光谱相干函数中,创建了一个改进的封面光谱 (IES).
- 应用IES-CYCBD方法来分离特定故障的特征频率.
- 模拟和实验验证使用复合轴承故障进行.
主要成果:
- IES-CYCBD方法成功地定位了对应于不同断层类型的共振波段.
- 在复合故障实验中,可以准确地分离内部和外部环故障特征.
- 该方法在高噪音水平下准确诊断故障方面表现强.
结论:
- 拟议的IES-CYCBD方法为轴承的复杂故障分离和诊断提供了一个强大的工具.
- 这种技术显著提高了在合的振动信号中识别单个故障信号的能力.
- 这些发现支持IES-CYCBD的实际应用,以加强机械健康监测.
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