Fault detection with improved just-in-time learning strategy for nonlinear multimode processes
To address the traditional strategy's drawbacks such as slow modeling speed and low model utilization efficiency,this study proposed an improved just-in-time learning(JITL)strategy,which approached these problems from two perspectives.At the offline stage,the K-means clustering algorithm was used to pre-classify historical data,and thus the scope of similar sample selection was changed from all historical data to historical data with the corresponding mode.At the online stage,a model update strategy was integrated to enhance the model utilization efficiency and consequently improve the modeling speed by reducing the model update frequency.The improved JITL strategy was applied to nonlinear multimode process fault detection with the just-in-time feature analysis(JITFA)algorithm as the model to calculate statistics.The proposed method was applied to a numerical example and a benchmark case and was compared with five different algorithms including JITFA.The simulation results demonstrate the superiority of the proposed strategy and its corresponding fault detection method.
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