LAUSR

Many fault detection and identification methods have been developed in recent years; each method works under its own assumption, which means a method that works well under one condition may not provide a satisfactory performance under another condition. In this paper, several data-based process monitoring methods are used in order to provide an effective monitoring scheme for processes under various conditions, and then the analytic hierarchy process approach is introduced for model prioritization. Compared with conventional ensemble systems, the proposed method is able to provide different priorities for different models in monitoring different process faults. Furthermore, a new fuzzy decision fusion system is designed for the purpose of online process monitoring. The effectiveness of the developed method is verified through using the Tennessee Eastman benchmark process.