对离心压力脉冲的预测方法的研究,基于变量模式分解-粒子群集优化和混合深度学习模型
Jiaxing Lu1,2,3, Yuzhuo Zhou1,2,3, Yanlong Ge4
1Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu 610039, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
这项研究引入了一种用于预测离心压力波动的新方法. 该VMD-PSO-CNN-LSTM模型准确预测压力脉冲,为智能操作提供科学基础.
科学领域:
- 流体力学 流体力学 流体力学
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 离心压力脉冲表现出复杂的,非静止的特征.
- 像CFD这样的传统方法与非线性和非平滑的压力波动预测作斗争.
- 准确的预测对于高效和智能离心运行至关重要.
研究的目的:
- 开发一种先进的方法来预测离心压力波动.
- 解决现有模型在处理复杂信号动态方面的局限性.
- 提高压力波动预测的准确性和可靠性.
主要方法:
- 用于信号处理的变化模式分解-粒子群集优化 (VMD-PSO).
- 采用卷积神经网络-长期短期记忆 (CNN-LSTM) 模型进行预测.
- 将VMD-PSO与CNN-LSTM结合起来,以提高压力脉冲信号的预测精度.
主要成果:
- 与单个神经网络模型相比,拟议的VMD-PSO-CNN-LSTM模型显示出更高的预测准确性.
- 实现了多个前进预测步骤的高准确性,对于一步预测的根平均平方误差为0.03145.
- 报告了一步预测的平均绝对百分比误差为1.007% (Pre = 1).
结论:
- 在VDD-PSO-CNN-LSTM方法有效预测离心压力波动.
- 这种方法为中复杂的非线性信号预测提供了强大的解决方案.
- 这些发现支持离心的智能操作和状态监测.
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