In this paper, a novel approach for processes monitoring, termed as filtering kernel independent component analysis-principal component analysis (FKICA-PCA), is developed. In FKICA-PCA, first, a method to calculate the variance of independent component is proposed, which is significant to make Gaussian features and non-Gaussian features comparable and to select dominant components legitimately; second, Genetic Algorithm is used to determine the kernel parameter through minimizing false alarm rate and maximizing detection rate; furthermore, exponentially weighted moving average (EWMA) scheme is used to filter the monitoring indices of KICA-PCA to improve monitoring performance. In addition, a novel contribution analysis scheme is developed for FKICA-PCA to diagnosis faults. The feasibility and effectiveness of the proposed method are validated on the Tennessee Eastman (TE) process.
Bibliographical noteThis work was supported by National Natural Science Foundation of China (61074081), the Fundamental Research Funds for the Central Universities (RC1101), Doctoral Fund of Ministry of Education of China (20100010120011), Beijing Nova Program (2011025), and the Fok Ying-Tong Education Foundation (131060).
- Process monitoring
- TE process
- Variable contribution analysis
- Variance of independent component