一个基于NARX模型的转子系统状态监测方法
Yi Gao1, Changshuai Yu1, Yun-Peng Zhu2
1School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了一种使用非线性自动回归与外部输入 (NARX) 模型进行转子系统分析的新方法. 开发的非线性故障指数可以有效地检测和量化转子系统的故障,有助于状态监测.
科学领域:
- 机械工程 机械工程
- 振动分析 振动分析
- 状态监控 状态监控
背景情况:
- 转子系统在许多工业应用中至关重要.
- 早期检测转子系统的故障对于防止灾难性故障至关重要.
- 传统的监测方法可能无法捕捉复杂的非线性行为.
研究的目的:
- 为旋转系统开发数据驱动的频域分析方法.
- 提出一种基于模型的索引,用于使用非线性模型检测故障特征.
- 为了增强旋翼系统的状态监测能力.
主要方法:
- 使用基于振动信号的非线性自动回归与外部输入 (NARX) 模型.
- 收集多个旋转速度的位移振动信号.
- 开发了一个基于非线性响应频谱函数 (NRSF) 的非线性故障指数.
- 通过在不对齐的转子系统上实验应用验证了该方法.
主要成果:
- 纳克斯模型成功地描述了旋转器系统的动态.
- 非线性故障指数是在多频段计算的.
- 该指数显示出与错位错误严重程度的直接相关性.
- 实验结果证实了拟议方法的有效性.
结论:
- 数据驱动的频域分析方法为旋翼系统状态监测提供了有效的方法.
- 拟议的非线性故障指数是旋转系统健康状况和故障严重性的可靠指标.
- 这种方法有助于早期和准确地检测旋转机械的故障.
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