Anomaly Detection Method for Multimode Complex Industrial Process Based on Multiple Subspaces Slow Feature Analysis

The multimode operation feature is widely presented in the modern industrial process, which is a typical cyber physical system. In order to achieve the accurate anomaly detection for multimode process, a novel method named multiple subspace slow feature analysis is proposed in this paper. Firstly, the neighborhood subtractive clustering algorithm is used to divide the mode. Then, to consider the local information and conduct fine-scale anomaly detection, Gaussian and non-Gaussian subspaces are built in each mode. Secondly, the static and dynamic features in each Gaussian and non-Gaussian subspaces are extracted through the slow feature analysis, and then the monitoring statistic and control limit are constructed. The control limit in each subspace is estimated according to different methods. During the online phase, the local outlier probability is used to determine the current mode for the online data, and the anomaly detection result can be achieved based on the built anomaly detection model. Finally, the effectiveness of the proposed MSSFA method is demonstrated in a numerical example and a typical industrial case.