Sequential Time Slice Alignment Based Unequal-Length Phase Identification and Modeling for Fault Detection of Irregular Batches

In practice, batch processes may have different durations, revealing unsynchronized operation events, due to the changes in operation conditions or control objectives. For batches with multiple operation phases, batch-wise process characteristics are irregular, and at the same time, phases are misaligned over batches, which have caused problems in phase analysis and modeling as well as online process monitoring. To solve the uneven-length problem in multiphase batch processes, this paper proposes a sequential time slice alignment based unequal-length phase identification and modeling method for fault detection of irregular batches. In comparison with previous work, the major contribution of the proposed method is as follows: (1) The irregular process characteristics are evaluated in sequence and directly related with the monitoring performance. (2) Multiple irregular phases are readily identified and modeled using sequential time slice alignment which avoids cumbersome postprocessing. (3) The sequential nature provides an easy way to real-time judge the phase affiliation of each new sample for online fault detection. Also, comparison is conducted between the proposed algorithm and clustering-based uneven-length phase division and process monitoring algorithm. The application to a typical batch process with varying durations illustrates the online monitoring performance of the proposed method.