Quality-related fault monitoring for multi-phase batch process based on multiway weighted elastic network

Abstract

A quality-related fault monitoring method of multi-phase batch process based on multiway weighted elastic network is proposed in this paper. Firstly, to make the phase division for batch process more accurately, an improved affinity propagation clustering algorithm is developed. Secondly, a multiway weighted elastic network model is developed in each phase. On the one hand, quality-related subspace and quality-unrelated subspace are constructed in each phase to achieve dual monitoring of process fault and quality anomalies. On the other hand, kernel density estimation is used to measure the contribution of each element in each subspace to the fault. According to the difference of the contribution of each element to the fault, different weight is assigned to enhance the fault features and eliminate irrelevant features such as noise. Finally, support vector data description is used to establish monitoring indexes in both quality-related subspace and quality-unrelated subspace. Compared with traditional methods, the superiority and effectiveness of the proposed method have been verified by monitoring the penicillin fermentation process and the hot strip mill process.