基于改进的欧勒尔视频运动放大器的旋转机械故障诊断研究
Haifeng Zhao1, Xiaorui Zhang1, Dengpan Jiang1
1School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了欧勒视频运动放大 (EVMM) 的自动化方法,用于诊断旋转机械故障. 改进的技术增强了振动分析,使得更准确的故障检测无需手动参数调整.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 计算机视觉 计算机视觉
背景情况:
- 旋转机械状况监测对于维护至关重要.
- 机械的振动往往是没有感觉到的.
- 欧勒尔视频运动放大 (EVMM) 可视化微妙的运动.
研究的目的:
- 开发用于EVMM的自动空间切断波长计算.
- 为了提高旋转机械故障诊断的准确性和效率.
- 为了克服EVMM中手动参数调整的局限性.
主要方法:
- 将视频分解为图像序列.
- 将RGB转换为HSV颜色空间并选择Value通道进行亮度分析.
- 自动计算EVMM的空间切断频率和波长.
- 将改进的EVMM应用于具有各种故障条件的转子轴承测试装置.
主要成果:
- 与原始EVMM相比,拟议的方法显示了显著的亮度变化.
- 自动波长计算成功地确定了不同故障 (不平衡,松动螺栓,复合物,错位) 的独特振动模式.
- 在正常条件下,没有显著的位移,验证了该方法的灵敏度.
结论:
- 自动化的EVMM方法有效地诊断旋转机械的故障.
- 它消除了手动参数调整的需要,增强了实际应用.
- 这为状态监测和故障诊断提供了一种新的方法.
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