Abstract
With the modern industrial processes getting advanced and complicated, accurate evaluation for the status of valves in control loops has become increasing significant. Nevertheless, control valves often suffer from stiction nonlinearity, which may bring about the malfunction of control loops, eventually resulting in an unanticipated breakdown or unacceptable performance deterioration. Hence, the recent past two decades have also witnessed a huge growth on valve stiction detection strategies for industry process. However, to the best of author’s knowledge, the question of how to quantify further aspects of valve stiction remains unanswered. In particular, most of the existing stiction quantification methods share the same limitation that they may occasionally fail to realize the estimation for the slipjump characteristic of valve stiction, and may be ineffective for the cases in which sticky valves are applied in the cascade control loops. Considering these drawbacks, this paper puts forward a novel quantification method for valve stiction based on the exponential and logarithmic maps attached onto the Riemannian manifold. The key idea is to exploit the relationship between process variables and controller output which constitutes a specific shape (e.g., ellipsoid) on a Riemannian manifold. The method can be capable to deal with three typical stiction features, including deadband, stickband, and slipjump for single control loops and simple cascade loops. The case studies of simulated examples validate that the proposed method is a promising method for quantifying the valve stiction’s deadband plus stickband, and slipjump accurately.
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Bo Huang received his B.S. degree in automation from the Huazhong University of Science and Technology, Hubei, China, in 2018. He is currently working toward a Ph.D. degree from the Department of Automation, Shanghai Jiao Tong University, Shanghai, China. His current research interests are in the fields of industrial process monitoring, fault detection, and Riemannian manifold theory.
Li-Sheng Hu received his B.S. degree in engineering science, and a Ph.D. degree in industrial automation, both from Zhejiang University, China, in 1986 and 1998, respectively. He was a postdoctoral fellow in Automation Engineering, Shanghai Jiao Tong University, Shanghai, China, from 1998 to 2000, and in Chemical Engineering, University of Alberta, Canada, from 2002 to 2003. He is now a Professor in the Department of Automation, Shanghai Jiao Tong University. His current research interests are in the areas of robust model prediction control, process monitoring, control performance limitation, performance assessment, and industrial fault detection.
Yunhong Peng received his B.S. degree in mechatronics from Yanshan University, Hebei, China, in 2001, and an M.S. degree in automation engineering, Shanghai Jiao Tong University, Shanghai, China, in 2009. He is now working in Shanghai Electric Power Generation Equipment Co., Ltd. Turbine Plant, Shanghai, China. His current research interests are in the areas of automation and maintenance of steam turbine platform, and industrial process monitoring.
Zhiwei You received his B.S. degree in thermal energy and power engineering from Dalian University of Technology, Liaoning, China, in 2007, and an M.S. degree in automation engineering, Shanghai Jiao Tong University, Shanghai, China, in 2020. He is now working in Shanghai Electric Power Generation Equipment Co., Ltd. Turbine Plant, Shanghai, China. His current research interests are in the areas of automation and maintenance of steam turbine platform, and industrial process monitoring.
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Huang, B., Hu, LS., Peng, Y. et al. Valve Stiction Quantification Based on Riemannian Manifold. Int. J. Control Autom. Syst. 21, 171–187 (2023). https://doi.org/10.1007/s12555-021-1100-2
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DOI: https://doi.org/10.1007/s12555-021-1100-2