在复杂的工作条件下,连接轴旋转系统的负载识别
1College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Heliyon
|November 25, 2024
概括
这项研究引入了一种新的方法,通过融合振动和电机电流数据来识别滚动设备旋转系统负载. 增强方法达到91.7%的准确性,优于单信号方法,用于改进工业诊断.
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:
- 滚动生产呈现出复杂的条件,挑战了传统的负载识别.
- 单源响应信号在准确评估转子系统负载方面存在局限性.
- 准确的负载识别对于保持滚动设备的完整性至关重要.
研究的目的:
- 开发一种可靠的方法,用于对滚动设备连接轴旋转系统的定性负载识别.
- 通过合并多个数据流来克服单源信号分析的局限性.
- 提高工业环境中负载类型识别的准确性和可靠性.
主要方法:
- 振动和电机电流信号的功能级融合.
- 使用单数值分解和波形包分析进行预处理.
- 使用贝叶斯估计进行负载类型分析的定性识别.
主要成果:
- 拟议的融合方法实现了91.7%的负载识别精度.
- 与使用单回应信号的方法相比,综合方法显示出更高的性能.
- 在专门的测试平台上进行的实验验证证证了该方法的有效性.
结论:
- 振动和电机电流信号的融合为旋转系统负载识别提供了一种可行和准确的方法.
- 这种方法比传统的单信号分析技术有了显著的改进.
- 经过验证的方法在滚动设备监测和诊断方面具有实际应用的潜力.
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