Batch process monitoring based on self-adaptive subspace support vector data description

- A new self-adaptive subspace (SAS) construction algorithm is defined.
- The complex behavior of dynamic process can be learned by SAS.
- The most variation information is sufficiently utilized in SASSVDD
- SASSVDD has better monitoring performance than traditional methods.

Inherent time-varying dynamics, which is a general characteristic of batch processing, causes two problems in data-driven batch process monitoring methods: (1) changes in data trajectory and (2) changes in correlation between variables along time. These problems can be solved by employing monitoring methods based on moving time window technology. However, correlation behaviors between variables in dynamic batch processing are complex. As a consequence, traditional monitoring methods may fail to detect faults. Complex correlation behaviors of batch processing can be learned by placing variables with similar variation information in the same subspace and faults may be detected. In this study, a self-adaptive subspace support vector data description (SASSVDD) is proposed. Two-time unfolding three-dimensional data technology and moving time window technology are used to obtain modeling data. An online subspace is then constructed by using sensitive variables, which may highly yield variation information, and non-sensitive variables, which likely contain variation information and exhibit a higher correlation with sensitive variables. Support vector data description is applied as the subspace monitoring method. The availability of SASSVDD is verified through the fed-batch penicillin fermentation.