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Diagnosing batch processes with insufficient fault data: generation of pseudo batches.
- Source :
- International Journal of Production Research; 7/15/2005, Vol. 43 Issue 14, p2997-3009, 13p, 3 Diagrams, 2 Charts, 1 Graph
- Publication Year :
- 2005
-
Abstract
- To ensure the safety of a batch process and the quality of its final product, one needs to quickly identify an assignable cause of a fault. Cho and Kim (2003) recently proposed a diagnosis method for batch processes using Fisher's Discriminant Analysis (FDA), which showed a satisfactory performance on industrial batch processes. However, their method (or any other method based on empirical models) has a major limitation when the fault batches available for building an empirical diagnosis model are insufficient. This is a highly critical issue in practice because sufficient fault batches are likely to be unavailable. In this work, we propose a method to handle the insufficiency of the fault data in diagnosing batch processes. The basic idea is to generate so-called pseudo batches from known fault batches and utilise them as part of the diagnosis model data. The performance of the proposed method is demonstrated using a real data set from a PVC batch process. The proposed method is shown to be capable of handling the data insufficiency problem successfully, and yields a reliable diagnosis performance. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00207543
- Volume :
- 43
- Issue :
- 14
- Database :
- Complementary Index
- Journal :
- International Journal of Production Research
- Publication Type :
- Academic Journal
- Accession number :
- 17321629
- Full Text :
- https://doi.org/10.1080/00207540500066937