An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
- Submitting institution
-
The University of Reading
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 74380
- Type
- D - Journal article
- DOI
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10.1109/TCYB.2017.2771229
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 198
- Volume
- 49
- Issue
- 1
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
8 - CV
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Soft-sensing techniques are of great importance in industrial processes for monitoring and prediction of Key Performance Indicators (KPIs). The significance of this paper is its multidsciplinary contribution to merge machine learning techniques with industrial informatics with new metrics for fault detection. The new method has been successfully validated on a benchmark industrial system for fault detection.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -