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| Received:July 03, 2007Revised:July 04, 2008 |
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| Sensor fault diagnosis of nonlinear processes based on structured kernel principal component analysis |
| Kechang FU, Liankui DAI , Tiejun WU, Ming ZHU |
| (Department of Control Engineering, Chengdu University of Information Technology, Chengdu Sichuan 610225, China; National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou Zhejiang 310027, China) |
| Abstract: |
| A new sensor fault diagnosis method based on structured kernel principal component analysis (KPCA) is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor (CSTR) process. |
| Key words: Sensor fault diagnosis Structured KPCA Incidence matrix optimization |
