在旋转机械中诊断转子元件流失:以数据为导向的方法
Sikai Zhang1, Qizhe Lin2, Jiayao Lin1
1Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325027, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
在旋转机械中早期检测旋转器部件脱落至关重要. 本研究使用振动分析与主要组件分析 (PCA) 和线性区分分析 (LDA) 进行精确的故障诊断,提高安全性和降低成本.
科学领域:
- 机械工程 机械工程
- 可靠性工程可靠性工程
- 信号处理 信号处理
背景情况:
- 旋转机械中的旋转器部件脱落带来了重大安全风险和经济成本.
- 现有的故障诊断方法可能缺乏关键机械所需的精度和早期检测能力.
- 尽量减少停机时间和维护费用,需要先进的诊断技术.
研究的目的:
- 开发和验证数据驱动的故障诊断技术,用于早期检测旋转器部件脱落.
- 提高旋转机械的运行安全性和可靠性.
- 为即将发生的转子故障提供及时警告,减少灾难性事件和成本.
主要方法:
- 对转子振动数据的频率分析,以确定波幅作为故障指示器.
- 主要组件分析 (PCA) 用于降低维度和对角化传感器数据.
- 用于特征选择的K折交叉验证和用于故障预测的线性差异分析 (LDA) 模型.
主要成果:
- 在振动数据中识别特定的波幅,作为初始转子脱落的可靠指标.
- 通过特征选择证明了PCA-LDA诊断算法的稳定性和通用性.
- 对16台工业压缩机和轮机的成功应用,验证了该方法的有效性.
结论:
- 拟议的数据驱动方法可以在早期阶段有效地检测旋转器部件的脱落.
- 该方法通过提供及时的故障警告,提高了运营安全性和可靠性.
- 这种技术为旋转机械的预测性维护提供了有价值的工具,减少了停机时间和成本.
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