Application of improved DPCA to distillation column process monitoring
An improved dynamic principal component analysis (DPCA) is presented which uses principal component related variable residual (PVR) statistic to replace Q statistic, applied to multivariate statistical process monitoring. The improved DPCA can avoid the conservation of Q statistical detection and provide more explicit process variable information about the principal component. Thus effectively identify the change of T2 chart which is caused by the change of normal working condition or process fault. After all, the method is applied to distillation column process detection. The simulation result shows that the improved DPCA method avoids the conservation of the Q statistic. Compared with the traditional DPCA, the improved DPCA is more effective.
2016 IEEE International Conference on Mechatronics and Automation