多尺度基于的特征提取用于检测轴向压缩机中的不稳定性开始
Yihan Fu1, Zheng Zhao1, Peng Lin1
1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Entropy (Basel, Switzerland)
|January 22, 2024
概括
这项研究引入了一种使用多尺度和统计特征来检测压缩机不稳定的早期迹象的新方法. 这种方法提高了轴向压缩机中不稳定性检测的可靠性.
科学领域:
- 机械工程 机械工程
- 空气动力学 在空气动力学.
- 信号处理 信号处理
背景情况:
- 压缩机的不稳定性会导致性能下降和运行故障.
- 早期发现不稳定的开始对于防止灾难性事件至关重要.
- 现有的方法可能会在不稳定性检测中常见的短时间序列数据中扎.
研究的目的:
- 开发一种强大的特征提取方法,用于检测轴向压缩机中的不稳定性初始化.
- 结合非线性和统计特征,以提高检测准确度.
- 为解决短时间序列数据的传统多尺度的局限性.
主要方法:
- 精制复合物多尺度被用于非线性特征提取.
- 主导的时间域统计特征是使用随机森林和平均下降精度来确定的.
- 通过将和统计特征与权重因子相结合,生成了一个组合特征.
主要成果:
- 提出的方法有效地从短时间序列数据中提取非线性和统计特征.
- 数字模拟表明了组合特征的有效性和稳定性.
- 该方法在合成噪声和压缩机不稳定性数据集中显示出可靠的性能.
结论:
- 开发的基于多尺度的特征提取对于检测轴向压缩机中的不稳定性开始是有效的.
- 与单个方法相比,综合特征方法提供了更好的准确性和稳定性.
- 该技术为实时监测和控制压缩机稳定性提供了一个有前途的工具.
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