Abstract
Batch process has been widely applied in the chemicals, pharmaceutical, and semiconductor industries. Based on the limitation of measurement devices, we cannot measure the quality characteristics of products immediately and then have no information to monitor and control. Hence, how to use the fault detection and classification (FDC) methodology to find the root cause by using data of the process variables is an important issue. In general, the variability of process variables between batches can be divided into the time axis variability and the variable axis variability. Both of them would affect the preciseness of implementing a process control scheme seriously and usually relate with products’ quality characteristics. Hence, it is necessary to obtain scale parameters that characterize the variability. In this work, we propose a statistical analysis procedure for batch profiles. First, we divided batch process data into time scaling, location scaling and residuals which characterize the time axis variability, the shift or drift of process variables and the batch profiles with no shift and drift respectively. Next, we analysis the residuals by modified health index and provide analysis and monitor methods for scale parameters. The proposed statistical analysis procedure can not only monitor faults in the process but also investigate the relationship of process variables to achieve the goal of executing process monitoring and process improvement.