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- Title
基于改进慢特征分析的CSTR 故障 诊断方法与实验平台.
- Authors
邓晓刚; 张学鹏; 王 平
- Abstract
Aiming at the problem that traditional slow feature analysis (SFA) can not fully analyze the nonlinear characteristics of continuous stirred tank reactor (CSTR), an improved fault diagnosis method of slow feature analysis random Fourier SFA (RFSFA) is proposed, and the corresponding simulation experimental platform is developed. In this method, random Fourier mapping technology is introduced to realize the nonlinear transformation of process variables, and then the nonlinear statistical monitoring model is established by using slow feature analysis. In order to overcome the influence of model random parameters, an ensemble learning model is constructed by using Bayesian inference theory. In order to verify the effectiveness of this method, an experimental platform for CSTR fault simulation and algorithm testing is designed, including the subsystems of normal condition simulation, fault condition simulation, fault detection, etc. The testing results show that the proposed RFSFA method has better fault detection performance than the traditional SFA method. The developed experimental platform is easy to operate along with good openness, and can well verify the effectiveness of the algorithm.
- Subjects
NONLINEAR statistical models; FOURIER analysis; BAYESIAN field theory; DIAGNOSIS methods; TEST design; FAULT diagnosis
- Publication
Experimental Technology & Management, 2022, Vol 39, Issue 9, p152
- ISSN
1002-4956
- Publication type
Article
- DOI
10.16791/j.cnki.sjg.2022.09.024