Prediction of packing performance of pneumatic control valve based on stem mechanism model and VMD-SSA-LSTM

• Published in: Journal of physics. Conference series Vol. 2815; no. 1; pp. 12012 - 12019
• Main Authors: Shi, Limiao, Yang, Bin, Wang, Yujin, Wang, Xinze
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.08.2024
• Subjects: Actuators; Control valves; Decomposition; Parameters; Performance prediction; Pneumatic control; Prediction models; Search algorithms; Wear mechanisms
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2815/1/012012

Packing performance is one of the most important parts affecting the control operation of pneumatic control valves. In order to avoid the failure caused by packing wear and aging of pneumatic control valves after long-term use, this paper constructs a valve stem mechanism model and combines the advantage of deep learning to predict the packing performance of pneumatic control valves based on Long Short-term Memory (LSTM) and Variable Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA). Firstly, the historical data of the actuator is calculated based on the mechanism model of the valve stem. The time-friction force obtained by the calculation is the parameter set. After the parameter set is subject to the variational mode decomposition, the parameter set is imported into the SSA-LSTM prediction model for prediction, and the prediction data is obtained.