Identification of critical moving characteristics in high speed on/off valve based on time derivative of the coil current. (August 2021)

• Record Type: Journal Article
• Authors: Gao, Qiang; Zhu, Yuchuan; Wu, Changwen; Jiang, Yulei
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 7(2021)
• Page Start: 1084
• Page End: 1099
• Publication Date: 2021-08
• Subjects: High speed on/off valve -- critical moving characteristics -- identification -- coil current -- derivative; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651820979086
• Languages: English
• ISSNs: 0959-6518

Abstract

This article focuses on accurate identification of the critical moving characteristics of the high speed on/off valve. Typically, there are two strategies for identifying the critical moving characteristics, including calculation strategy based on force balance and strategy for detecting the coil current's certain points. However, the accuracy of the two above strategies needs to be improved. Therefore, to improve the identification accuracy of the high speed on/off valve's critical moving characteristics, an identification strategy for detecting the time derivative of the coil current is proposed. First, a mathematical model of the high speed on/off valve (including electromagnetic sub-model and mechanical-fluid sub-model) is established. And on this basis, relationship between the coil current's derivative and the valve's critical moving characteristics is analyzed which reveals the changing rule of the coil current's derivative causing by the ball valve's moving. Finally, the changing rule of the coil current's derivative is verified by the comparative simulations and experiments, which also indicate that, with the proposed identification strategy, the maximum identification error of the critical opening/closing time is only within 6%, and the maximum identification error of the total opening/closing time is still small (2.9%), compared to other identification strategies in the previous literatures.