Parallel supervised additive and multiplicative faults detection for nonlinear process |
| |
| Institution: | 1. College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China;2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;3. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;4. College of Engineering, University of Alabama, Tuscaloosa, AL 35487, USA;1. School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China;2. National Engineering Laboratory of Industrial Control System Security Technology, Zhejiang University, Hangzhou 310027, China;3. Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai 200240, China |
| |
| Abstract: | In this paper, a novel supervised nonlinear process monitoring method named comprehensive kernel principal component regression (C-KPCR) is proposed to monitor the quality-related/unrelated additive/multiplicative faults. Firstly, mutual information is used to classify the process variables into quality-related part and quality-unrelated part. Secondly, the original variables matrix and the variables variance matrix are constructed and the data is mapped into high-dimensional feature space to deal with the nonlinear problem. Then the quality-related additive and multiplicative faults can be detected based on the regression model using original variables matrix and variables variance matrix, respectively. Afterwards, the monitoring result of quality-unrelated fault is obtained through combining the quality-unrelated information in the regression model and the quality-unrelated process variables. Finally, the effectiveness of the proposed method is demonstrated by a numerical example and the Tennessee Eastman process. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
