Quality-related batch process monitoring based on multi-way orthogonal signal correction enhanced total principal component regression

Batch process quality-related fault detection is necessary for keeping operation safety and quality consistency. However, the process variables have a weak ability to explain the quality variables makes the batch process quality-related fault detection a difficult task. In this work, a multi-way orthogonal signal correction enhanced total principal component regression (MOSC-ETPCR) is proposed to achieve the nonlinear quality-related fault detection of the batch process. First, after batch process data expansion, the orthogonal signal correction algorithm is used to filter out the quality-irrelevant information in process variables and avoid the influence of quality-irrelevant data on process modeling. Secondly, the nonlinear characteristics of the process are extracted by the maximum information coefficient matrix, and the quality-related nonlinear regression model is constructed to ensure the maximum correlation between the extracted features and quality variables. Thirdly, the statistics and corresponding control limits are established based on the obtained regression model. Finally, the effectiveness of the MOSC-ETPCR algorithm was verified by numerical simulation and the penicillin fermentation process.