摘要
Frequent operation changes are inevitable to achieve different production aims, which leads to mixed periods of stationarity and non-stationarity in industrial processes and increases the monitoring difficulty. In this paper, a generalized monitoring scheme is proposed for industrial processes with stationary and non-stationary operational stages. Firstly, the Local Average Similarity (LAS) and Distance Average Similarity (DAS) are developed on offline training data to divide operational stages and identify repeating stages. The equilibrium relationship between variables in each stage can be guaranteed rather than in the whole process, which is the premise of refined modeling. Then, multiple cointegration analysis (CA) and detrended fluctuation regression (DFR) models are proposed to handle non-stationary variables with different integrated orders, so as to map all stages into stationary space. For online monitoring, the real-time stage identification is performed based on the comprehensive similar index (CSI) that combines LAS and DAS; and the process monitoring is realized by local static principal component analysis (PCA). Finally, the effectiveness of our proposed method is verified by an extended Tennessee Eastman simulation and batchfed penicillin fermentation process.