@inproceedings{01edf6a3df2945c29b58622bc9fcafb4,
title = "Soft sensing model of flow rate for independent metering valves based on LSTM",
abstract = "Flowmeters and mechanism models are common in hydraulic system for estimating flow rates. However, the flowmeters are costly and the establishment of high-precision mechanism models is difficult. To address these challenges, a soft sensing model of flow rate based on a long short term memory (LSTM) neural network is proposed and applied to four-spool independent metering (IM) valves. In this study, the data set was produced by experiments, and gray correlation analysis was utilized for feature filtering. Then the model was trained and tested. The results indicate that the proposed LSTM model has a highly flow rate soft sensing accuracy, with a root mean square error of 1.93 and an average absolute percentage prediction error of only 3.16\%.",
keywords = "IM valves, LSTM, hydraulic system, soft sensing model of flow rate",
author = "Wei Ren and Wenbin Su and Hui Sun and Canjie Liu and Xuhao Lu and Yingli Hua",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Fluid Power and Mechatronics, FPM 2023 ; Conference date: 18-08-2023 Through 21-08-2023",
year = "2023",
doi = "10.1109/FPM57590.2023.10565468",
language = "英语",
series = "2023 9th International Conference on Fluid Power and Mechatronics, FPM 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 9th International Conference on Fluid Power and Mechatronics, FPM 2023",
}