基于不同算法优化VMD – BiLSTM的离心泵空化状态识别方法研究

Research on Centrifugal Pump Cavitation State Identification Using VMD – BiLSTM Optimized by Different Algorithms

摘要:

为了进一步提升离心泵空化状态下振动信号智能诊断的预测精度,提出了结合优化算法与长短期记忆网络(Long short term memory networks,BiLSTM)的变分模态分解(Variational mode decomposition,VMD)方法。用此算法进行离心泵内流体的多种空化状态的识别。引入算法来优化BiLSTM模型,从而改进了时间序列数据的预测准确性。为了提高预测精度,采用MSO ( Mirage swarm optimization)、SSA ( Sparrow search algorithm)和POA ( Pelican optimization algorithm)算法进行对比并进行参数寻优,获得了不同的预测模型。针对离心泵内流体的空化试验振动数据,首先通过VMD将原始振动信号分解为多个本征模态函数(Intrinsic mode function, IMF)的分量;然后根据这些分量的构造特征,利用深度学习算法对流体的空化状态进行了准确识别和预测。基于3种不同工况条件下(未空化、初生空化、严重空化)的离心泵振动数据,试验结果显示了VMD-SSA-BiLSTM训练后预测模型拟合度均高于LSTM、BiLSTM、VMD – BiLSTM、VMD-POA-BiLSTM、VMD-MSO-BiLSTM等方法,该研究为识别离心泵内流体的多空化状态提供了可靠的工具。

Abstract:

Aiming to further improve the prediction accuracy of intelligent diagnosis of vibration signals in the cavitation state of centrifugal pumps, a combination of optimization algorithms and long short term memory networks was proposed, i. e. , the method of variational mode decomposition ( VMD) of BiLSTM. This algorithm was used to identify various cavitation states in centrifugal pumps. An algorithm was introduced to optimize the BiLSTM model, thereby improving the prediction accuracy of time series data. In order to improve the prediction accuracy, mirage swarm optimization (MSO), sparrow search algorithm (SSA) and Pelican optimization algorithm ( POA) were adopted. The POA algorithms were compared and parameter optimization was carried out, obtaining different prediction models. For the vibration data of the cavitation experiment, the original vibration signal was firstly decomposed into multiple intrinsic mode function (IMF) components through VMD. As environmental pressure decreased, cavitation would gradually intensify. Acceleration sensors were placed at five positions to collect vibration signals. These measurement points were as follows: the outlet flange, the radial direction on the upper side of the pump body, the axial direction of the pump body, the radial direction on the right side of the pump body, and the pump feet. The collected vibration acceleration signal was subjected to the fast Fourier transform (FFT) to obtain the time-domain signal at the rated speed of 1450r/min, and the time-domain signal was converted into a frequency-domain signal based on the fast Fourier transform (FFT). Spectral analysis indicated that the vibration energy was mainly concentrated between the axis passing frequency (APF) and the blade passing frequency (BPF). Among them, the shaft frequency APF was 24.2 Hz, and the blade passing frequency BPF was 145.0Hz. Then, based on these components, features were constructed, and deep learning algorithms were used to accurately identify and predict the cavitation state. Based on the vibration data of centrifugal pumps under three different working conditions (no cavitation, primary cavitation, and severe cavitation), the experimental results showed that the prediction model fitting of VMD-SSA-BiLSTM after training was higher than that of LSTM, BiLSTM, VMD – BiLSTM, VMD-POA-BiLSTM, and VMD-MSO-BiLSTM. The research result can provide a reliable tool for identifying the multi-cavitation state of centrifugal pumps.