A Dynamic Multi‐Stage Multi‐Batch Process Fault Detection Method Based on Baseline Standardization and <scp>MPCA</scp>

ABSTRACT In response to the challenges posed by the dynamic and multi‐stage characteristics of complex multi‐batch processes in process monitoring, the Baseline Standardization (BS) strategy is proposed, which, together with Multiway Principal Component Analysis (MPCA), forms the BS‐MPCA fault detection method. BS constructs a baseline batch that changes over time based on the mean of samples at the same time point from different batches. Due to inevitable fluctuations in the baseline batch, a Gaussian filter is applied to smooth the baseline batch in order to enhance its robustness. The dataset is standardized based on the standard deviation of samples at the same time point in different batches and the baseline batch, resulting in the standardized dataset. BS scales the data to a unified scale, which eliminates the dynamic and multi‐stage characteristics of the process. The resulting standardized dataset meets the assumption of independent and identically distributed statistics and . Based on this, the MPCA method is used for feature extraction and fault detection. BS‐MPCA is applied to fault detection in numerical processes and the Three‐tank process. Experimental results show that compared to MPCA, DPCA, MDPCA, VBSPCA, KNN, and WKNN methods, the BS‐MPCA fault detection method achieves a higher fault detection rate. Additionally, it eliminates the dynamic and multi‐stage characteristics of the process during the modeling phase, and its detection model outperforms the models of the comparison methods. Theoretical analysis and simulation experiments show that the BS‐MPCA method is suitable for fault detection in multi‐batch processes, whether they have simultaneous or individual dynamic and multi‐stage characteristics. It more effectively ensures the safety of the production process and the high quality of the products.