Multi-grade principal component analysis for fault detection with multiple production grades

In many chemical industries, a production line usually produces various products with different grades to meet the demands of the worldwide market. A process with multiple grades is not suitable to be described using a traditional single model. In this paper, a multi-grade principal component analysis (MGPCA) model is proposed for multi-grade process modeling and fault detection purposes. The proposed MGPCA can use the measurements from different grades with unequal sizes and to extract the essential information from the multi-grade process. The model is derived in a probabilistic framework and the corresponding parameters are estimated by the expectation-maximization algorithm. Finally, a simulated case and a real industrial polyethylene process with multiple grades are tested to evaluate the property of the proposed method.