Lu Jiaxing, Zhou Yuzhuo, Ge Yanlong, Liu Jiahong, Zhang Chuan
Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu 610039, China.
Key Laboratory of Fluid Machinery and Engineering, Xihua University, Chengdu 610039, China.
Sensors (Basel). 2024 Jun 27;24(13):4196. doi: 10.3390/s24134196.
Centrifugal pump pressure pulsation contains various signals in different frequency domains, which interact and superimpose on each other, resulting in characteristics such as intermittency, non-stationarity, and complexity. Computational Fluid Dynamics (CFD) and traditional time series models are unable to handle nonlinear and non-smooth problems, resulting in low accuracy in the prediction of pressure fluctuations. Therefore, this study proposes a new method for predicting pressure fluctuations. The pressure pulsation signals at the inlet of the centrifugal pump are processed using Variational Mode Decomposition-Particle Swarm Optimization (VMD-PSO), and the signal is predicted by Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model. The results indicate that the proposed prediction model combining VMD-PSO with four neural networks outperforms the single neural network prediction model in terms of prediction accuracy. Relatively high accuracy is achieved by the VMD-PSO-CNN-LSTM model for multiple forward prediction steps, particularly for a forward prediction step of 1 (Pre = 1), with a root mean square error of 0.03145 and an average absolute percentage error of 1.007%. This study provides a scientific basis for the intelligent operation of centrifugal pumps.
离心泵压力脉动在不同频域中包含各种信号，这些信号相互作用并相互叠加，从而产生间歇性、非平稳性和复杂性等特征。计算流体动力学（CFD）和传统时间序列模型无法处理非线性和非光滑问题，导致压力波动预测的准确性较低。因此，本研究提出了一种预测压力波动的新方法。利用变分模态分解-粒子群优化（VMD-PSO）对离心泵入口处的压力脉动信号进行处理，并通过卷积神经网络-长短期记忆（CNN-LSTM）模型对信号进行预测。结果表明，所提出的将VMD-PSO与四种神经网络相结合的预测模型在预测准确性方面优于单一神经网络预测模型。VMD-PSO-CNN-LSTM模型在多个向前预测步长上实现了相对较高的准确性，特别是对于向前预测步长为1（Pre = 1）的情况，均方根误差为0.03145，平均绝对百分比误差为1.007%。本研究为离心泵的智能运行提供了科学依据。
