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Enhanced robust multimode process monitoring under dirty data via difference-based decomposition of matrix.
- Source :
-
Journal of Process Control . Dec2023, Vol. 132, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Traditional data-driven methods generally suppose the training dataset is not corrupted by outliers. However, outliers are inevitable in the real industrial processes even with a relatively high ratio, which degrades the accuracy of data-based models. For multimode process monitoring, outliers may deteriorate the accuracy of both mode identification and fault detection. However, the existing robust methods can hardly deal with a large percentage of outliers, i.e., dirty data in neither single nor multimode processes. In this paper, a robust multimode process monitoring scheme is developed by alleviating the negative effect of dirty data. A difference-based decomposition of matrix (DDM) algorithm is first proposed to divide the data into a basic subpart and a non-basic subpart. The optimization function of the proposed DDM algorithm is solved by the alternating direction method of multipliers (ADMM). In the off-line procedure, an iterative decomposition strategy is designed based on the proposed DDM approach to identify dirty data and obtain the clean dataset of each mode. In the on-line procedure, an on-line sample identification strategy is developed by the type indicator derived from the DDM algorithm to determine whether the current sample belongs to a mode, the dirty data, or the fault. A numerical example and an industrial-scale multiphase flow facility indicate the proposed method can improve the accuracy of both mode identification and fault detection for multimode processes even under dirty data. • The proposed method monitors multimode processes corrupted with dirty data. • The proposed DDM algorithm selects non-similar data from a certain class of data. • The method detects dirty data and identifies modes concurrently without mode numbers. • Numerical case and multiphase flow process proved the effectiveness of our proposal. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09591524
- Volume :
- 132
- Database :
- Academic Search Index
- Journal :
- Journal of Process Control
- Publication Type :
- Academic Journal
- Accession number :
- 174036252
- Full Text :
- https://doi.org/10.1016/j.jprocont.2023.103080