Nonlinear Control Systems Modelling Using Local Linear Mappings

Dzieliński, Andrzej; Graniszewski, Waldemar

doi:10.3182/20020721-6-es-1901.01016

Cited by 2 publications

(1 citation statement)

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“…It is a weak point that contribution plot analysis is strongly dependent on the process understanding of the analyst. Also, several mode identification methods based on fuzzy C-means clustering, 13 hierarchical clustering, 4 self-organizing neural networks, 14 and fuzzy rule-based identification techniques 15 can be found. However, because these methods must (i) update the defined operating modes, according to gradual process changes, or (ii) add an algorithm to isolate disturbances, they are not appropriate to be combined with the model update scheme.…”

Section: On-line Process State Classification For Model Updatementioning

confidence: 99%

On-Line Process State Classification for Adaptive Monitoring

Lee

Jin

Han

2006

Ind. Eng. Chem. Res.

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In process monitoring that is based on statistical models, adaptive monitoring techniques have been developed to reflect frequent changes in the operating conditions. The key to adaptive monitoring of real industrial processes is to distinguish process operating condition changes from variations due to disturbances. This paper proposes a systematic method for detecting process state changes and classifying them as operating condition changes or variations as a result of disturbances. The key idea of the proposed method is to extract process knowledge that is based on if−then rules for detecting the operating condition changes. When a state change is accepted by a defined set of rules, it is classified as an operating mode change. Otherwise, it is classified as a disturbance. A signed digraph and statistical data analysis are used to generate the rules from the process knowledge. A robust cumulative-sum algorithm is used for change detection. The proposed method was validated using a dataset from an industrial process heater. The results showed a classification power of 97%. Adaptive monitoring that is based on the proposed method could significantly reduce the number of false alarms, compared to previous remodeling approaches.

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Section: On-line Process State Classification For Model Updatementioning

confidence: 99%