Minimum mean squared prediction error criterion based improved independent component analysis method for process monitoring

Independent component analysis (ICA) is a newly emerging feature extraction method for non-Gaussian process monitoring. However, the extracted feature by ICA may not represent the original process data well, which can result in the degraded monitoring performance. In this paper, an improved ICA method based on the minimum mean squared prediction error criterion is proposed for process monitoring. A new criterion which can make the extracted non-Gaussian feature be efficient representation for the original process data is constructed as the objective function of the improved ICA by integrating the maximum negentropy criterion of the conventional ICA with the minimum mean squared prediction error criterion. Then the gradient ascent algorithm is applied to optimize the constructed objective function for seeking the feature extraction directions. Finally, a monitoring statistic is built based on the extracted feature to detect process faults. The simulation studies on the Tennessee Eastman benchmark process demonstrate that the improve ICA is more effective than the conventional ICA for improving the monitoring performance in terms of the fault detection rate.