滚动轴承复合故障诊断方法基于增强的波向量分析
Jiantao Lu1, Qitao Yin1, Shunming Li1,2
1College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
一种增强的波向量分析 (EHVA) 方法通过分离重叠的故障信号,有效地诊断滚动轴承的复合故障. 这种技术提高了确定轴承缺陷的准确性和效率.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 状态监控 状态监控
背景情况:
- 滚动轴承中的复合故障诊断是具有挑战性的,因为其特征频率重叠.
- 在复合信号中准确识别单个故障信号对于有效的维护至关重要.
研究的目的:
- 提出一种增强的波向量分析 (EHVA) 方法,用于滚动轴承的复合故障诊断.
- 为了提高从复杂的振动数据中分离和识别单个故障信号的准确性和效率.
主要方法:
- 波段值 (WT) 降低振动信号中的噪声.
- 波向量分析 (HVA) 采用cepstrum值和Wiener-like掩盖,用于盲信号分离.
- 后向投影用于频率尺度对齐和库尔图грам分析用于故障特征增强.
主要成果:
- 通过EHVA方法,成功地将单个故障信号与复合滚动轴承故障信号分开.
- 与传统的HVA,FICA和FMBD相比,EHVA显示了更好的分离精度和增强的故障特征.
- 半物理模拟实验验证实了EHVA方法的有效性.
结论:
- 拟议的EHVA方法对于滚动轴承的复合故障诊断是有效的.
- 在轴承故障分析的准确性和效率方面,EHVA提供了卓越的性能.
- 这种方法为复杂的机器状态监控提供了强大的解决方案.
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