Multimode process fault detection method based on variable local outlier factor

Recently, local outlier factor (LOF)-based process monitoring methods, which can handle multimodality and complex data distribution simultaneously, have been successfully applied to industrial process monitoring. However, local variable information cannot be reflected by tradition LOF method and would be submerged in process monitoring. To emphasize information of local variable, this paper proposes a novel multimode process fault detection method, referred to as variable local outlier factor (VLOF). Firstly, the neighbors of a sample are determined in training dataset using Euclidean distance. Then, Euclidean distance between data pairs is transformed into distance vector, with which vector-form reachability distance and LOF vector can be sequentially calculated. While monitoring multimode process, a fused monitoring statistic is constructed by combining whole LOF values of total variables. Finally, simulation on the Tennessee Eastman (TE) process is used to demonstrate the superiority of the proposed method.