Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion

• Published in: Chinese journal of mechanical engineering Vol. 37; no. 1; pp. 38 - 17
• Main Authors: Su, Deying, Wang, Shaojie, Lin, Haojing, Xia, Xiaosong, Xu, Yubing, Hou, Liang
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 30.04.2024; Springer Nature B.V; SpringerOpen
• Edition: English ed.
• Subjects: Algorithms; Bayesian analysis; Control valves; Cylinders; Data fusion method; Data integration; Electrical Machines and Networks; Electronics and Microelectronics; Engineering; Engineering Thermodynamics; Heat and Mass Transfer; Instrumentation; Intelligent Manufacturing Technology; Machines; Manufacturing; Mechanical Engineering; Model accuracy; Original Article; Parameter estimation; Particle swarm optimization; Power Electronics; Processes; Simulation models; The Bayesian theory; Theoretical and Applied Mechanics; Valve-controlled cylinder system; Weight coefficients; The Bayesian theory; Parameter estimation; Data fusion method; Weight coefficients; Valve-controlled cylinder system
• ISSN: 2192-8258; 1000-9345; 2192-8258
• DOI: 10.1186/s10033-024-01022-8

The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system. Bench test data are easily obtained, but it is challenging to emulate actual loads in the research on parameter estimation of valve-controlled cylinder system. Despite the actual load information contained in the operating data of the control valve, its acquisition remains challenging. This paper proposes a method that fuses bench test and operating data for parameter estimation to address the aforementioned problems. The proposed method is based on Bayesian theory, and its core is a pool fusion of prior information from bench test and operating data. Firstly, a system model is established, and the parameters in the model are analysed. Secondly, the bench and operating data of the system are collected. Then, the model parameters and weight coefficients are estimated using the data fusion method. Finally, the estimated effects of the data fusion method, Bayesian method, and particle swarm optimisation (PSO) algorithm on system model parameters are compared. The research shows that the weight coefficient represents the contribution of different prior information to the parameter estimation result. The effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO algorithm. Increasing load complexity leads to a decrease in model accuracy, highlighting the crucial role of the data fusion method in parameter estimation studies.