Abstract: In industrial process monitoring, traditional process monitoring methods fail to extract process dynamic information and the online fault-related information is not fully utilized in feature selection. To solve these problems, a fault detection method based on online weighted slow feature analysis (OWSFA) is proposed. First, the slow feature analysis(SFA)algorithm is utilized to extract the dynamic features. The threshold of slow feature is estimated based on benchmark data. The online features which exceed the threshold based on the slack factor are selected as the suspected fault features. Then, the monitoring statistics are built based on the weighted suspected fault features by introducing the weights. The proposed OWSFA algorithm is applied in a numerical system and the Tennessee Eastman process, which proves that the proposed method has superiorities compared with principal component analysis and the SFA method. According to the faulty information, the OWSFA algorithm generates the online weighted statistics to enhance the fault features in the monitoring model.

Key words: slow feature analysis (SFA), fault detection, weighted feature, dynamic process, chemical process