Prediction for water hammer pressure signal based on empirical mode filter and recurrent neural network
ZHANG Bo1, XU Zhuofei2, LI Xiaozhou2, MAO Zhenkai1, GUO Pengcheng2*
1. Power China Northwest Engineering Corporation Limited, Xi′an, Shaanxi 710065, China; 2. School of Water Resources and Hydroelectric Engineering, Xi′an University of Technology, Xi′an, Shaanxi 710048, China
Abstract：To accurately predict the change trend of water hammer impact signal and get the characte-ristics of impact strength and energy in advance, a prediction model based on Empirical Mode Decomposition(EMD)and Recurrent Neural Network(RNN)was proposed for the pressure signals from water hammer impact. Firstly, a series of Intrinsic Mode Function(IMF)was obtained from EMD method, and then high frequency noise components were eliminated according frequency characteristics in water hammer to realize the filtering and reconstruction. The filtered signal energy loss is less than 0.1% and the smoothness is good. Secondly, a prediction model for time series based on RNN model is established and the test platform is built to obtain the water hammer impact signal, and the RNN model was trained with a few samples. Then, a sequence prediction for water hammer impact signals under different flow rates was realized. Although the flow rates are different between the training and testing sets, the result is accuracy and reliable according assessment based on energy loss, amplitude loss and R2 coefficient. Comparing the predicted water hammer signal with the actual signal, the R2 coefficient is greater than 0.9900, and the amplitude and energy loss are less than 1%, which verifies the correctness of the proposed method.
张博,徐卓飞,李小周,毛振凯,郭鹏程*. 基于经验模式滤波与循环神经网络的水锤压力信号预测[J]. 排灌机械工程学报, 2022, 40(11): 1120-1125.
ZHANG Bo,XU Zhuofei,LI Xiaozhou,MAO Zhenkai,GUO Pengcheng*. Prediction for water hammer pressure signal based on empirical mode filter and recurrent neural network. Journal of Drainage and Irrigation Machinery Engin, 2022, 40(11): 1120-1125.
