使用变化模式分解和CNN-LSTM-SVM算法识别和测量非静止信号中的关键信息的先进方法
The Review of scientific instruments
|October 21, 2024
概括
本研究引入了一种使用变化模式分解 (VMD) 和深度学习 (CNN-LSTM-SVM) 的新方法,用于准确识别非静止工业信号中的关键信息,用于故障诊断等应用.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 工业监控 工业监控 工业监控
背景情况:
- 在工业环境 (物流,故障诊断) 中,非静止信号对准确的识别和测量构成挑战.
- 现有的方法难以应对这些信号的复杂性和可变性.
研究的目的:
- 开发一种可靠的方法来识别和测量来自非静止工业信号的关键信息.
- 在关键的工业应用中提高信号分析的准确性和可靠性.
主要方法:
- 利用变化模式分解 (VMD) 来进行信号重建.
- 使用上封面,移动库尔托斯和移动根平均平方构建特征矩阵.
- 采用混合卷积神经网络 (CNN),长期短期记忆 (LSTM) 网络和支持向量机 (SVM) 模型 (CNN-LSTM-SVM) 进行特征识别和测量.
主要成果:
- 拟议的CNN-LSTM-SVM方法在合成信号上实现了99.17%的高识别精度.
- 该方法在现实世界收集的信号上表现出色,达到99.02%的准确性.
结论:
- 综合VMD和CNN-LSTM-SVM方法有效地解决了识别非静止工业信号中的关键信息的局限性.
- 该方法为工业环境中的状态检测和故障诊断提供了显著的进步.
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