Fault diagnosis of batch processes using discriminant model
SCIE
SCOPUS
- Title
- Fault diagnosis of batch processes using discriminant model
- Authors
- Cho, HW; Kim, KJ
- Date Issued
- 2004-02-01
- Publisher
- TAYLOR & FRANCIS LTD
- Abstract
- A new statistical online diagnosis method for a batch process is proposed. The proposed method consists of two phases: offline model building and online diagnosis. The offline model building phase constructs an empirical model, called a discriminant model, using various past batch runs. When a fault of a new batch is detected, the online diagnosis phase is initiated. The behaviour of the new batch is referenced against the model, developed in the offline model building phase, to make a diagnostic decision. The diagnosis performance of the proposed method is tested using a dataset from a PVC batch process. It has been shown that the proposed method outperforms existing PCA-based diagnosis methods, especially at the onset of a fault.
- Keywords
- PRINCIPAL COMPONENT ANALYSIS; PARTIAL LEAST-SQUARES; PATTERN-RECOGNITION; CHEMICAL-PROCESSES; SYSTEMS; REDUNDANCY; MULTIBLOCK; ALGORITHM; DESIGN; PLS
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/18208
- DOI
- 10.1080/00207540310001602928
- ISSN
- 0020-7543
- Article Type
- Article
- Citation
- INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, vol. 42, no. 3, page. 597 - 612, 2004-02-01
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