Abstract
Offline updating is a method that most of multiple model soft-sensors used to adapt the new operating conditions. Replacing online models with offline ones is bound to affect the efficiency of soft-sensors, and it costs manpower as well as time simultaneously. It takes maintenance staffs some time to re-train complete models, which requires a lot of historical data, and then the existing models will be changed with new ones. A soft-sensor that can be added or subtracted models online is proposed in this paper. Density-based spatial clustering of applications with noise (DBSCAN) is employed for clustering analysis. Compared with traditional kernel fuzzy clustering method (KFCM), DBSCAN improves the ability of filtering out noise and enhance the ability to decide whether there is a new working condition. However, the clustering results of DBSCAN are extremely sensitive to the input parameters. In this study, kernel density estimation (KDE) is applied to determine the number of subsets and a novel method is proposed to determine the parameters. The new sub-models can be directly added to the online models after trained. The results of soft-sensor achieved by a number of models according to the switching or weighted way. The method proposed in this paper is applied to the measurement of cracking depth of ethylene cracking furnace, which proves the practicability and effectiveness.
