Modeling and monitoring of multimode process based on between-mode relative analysis

The between-mode relative analysis algorithm based on kernel independent component analysis (KICA) for multimode process monitoring is proposed in this paper. The main contributions of the proposed approach are as follows: 1) KICA algorithm is used to extract the independent components, and then find the relationship between different modes;2) according to the relative changes which are obtained by the proposed algorithm, each mode is divided into three parts which contain the increased part, the decreased part and the unchanged part; 3) the monitoring statistics are calculated to detect fault and recognize modes for the three parts above respectively. The performance of the proposed method is illustrated by Tennessee Eastman Process (TEP). Comparing to the traditional multimode method, the experiment results show the advantage of the proposed approach.

[1]  Chun-Chin Hsu,et al.  An Adaptive Forecast-Based Chart for Non-Gaussian Processes Monitoring: With Application to Equipment Malfunctions Detection in a Thermal Power Plant , 2011, IEEE Transactions on Control Systems Technology.

[2]  S. Joe Qin,et al.  Multivariate process monitoring and fault diagnosis by multi-scale PCA , 2002 .

[3]  Chunhui Zhao,et al.  Between-Mode Quality Analysis Based Multimode Batch Process Quality Prediction , 2014 .

[4]  Qiuqi Ruan,et al.  KICA for Face Recognition Based on Kernel Generalized Variance and Multiresolution Analysis , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[5]  A. J. Morris,et al.  Performance monitoring of a multi-product semi-batch process , 2001 .

[6]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[7]  Haihong Hu,et al.  Human Gait Recognition Based on Kernel Independent Component Analysis , 2007, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007).

[8]  Manabu Kano,et al.  Monitoring independent components for fault detection , 2003 .

[9]  A. Höskuldsson.  PLS regression methods , 1988 .

[10]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

[11]  Tianyou Chai,et al.  Knowledge-Based Global Operation of Mineral Processing Under Uncertainty , 2012, IEEE Transactions on Industrial Informatics.

[12]  Nan Yang,et al.  Fault Isolation of Nonlinear Processes Based on Fault Directions and Features , 2014, IEEE Transactions on Control Systems Technology.

[13]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[14]  Jin Cao,et al.  PCA-based fault diagnosis in the presence of control and dynamics , 2004 .