Phase Partition and Online Monitoring for Batch Process Based on Multiway BEAM

Batch process can exhibit significantly different characteristics across different phases, hence it is significant to partition it reasonably and set up corresponding subphase models for online monitoring. Unlike traditional phase-partition algorithms that customarily exploit the result of PCA algorithm for advanced research, an innovative algorithm which directly extracts effective information from the covariance matrix is presented in this paper, which is called multiway beacon exception analysis for maintenance (MBEAM). Its theoretics and statistical characteristics are demonstrated adequately. Based on the accurate capture of the change in variable correlation caused by characteristic variance of the process, the algorithm can separate the process into major phases and transition patterns automatically. The time-varying characteristics will then remain relatively stable in each independent subphase and will be supervised by homologous monitoring model that reflects the inherent phase feature. Due to its simple and intuitive format, MBEAM has superior performance in computation efficiency and fault interpretation, which is illuminated later in this paper. Synthetical illustrations are given concerning the influences of major parameters on the monitoring performance. Comparison with the step-wise sequential phase partition algorithm is conducted for a clearer insight. Experiments are carried out to further confirm the validation of the proposed method.