Strategy of virtual measurement for optimization of dynamic dead-time processes in automated control systems

Dementjev, Alexander; Ribbecke, H.-D.; Kubin, H.; Kabitzsch, Klaus

doi:10.1109/indin.2007.4384819

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…However, in the literature the VS solutions which allow an adaptation (i.e. on-line adaptation to the long time process changes) are already presented, for example, in [5,14,3,4]. These solutions require the delayed feedback signal of main process parameter.…”

Section: Adaptive Virtual Sensormentioning

confidence: 99%

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Improvement of prediction performance for data-driven virtual sensors

Dementjev

Ribbecke

Kabitzsch

2010

2010 IEEE 15th Conference on Emerging Technologies &Amp; Factory Automation (ETFA 2010)

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Virtual sensors (VS) allow measurement of process parameters where direct measurement is too expensive or even not possible. For the virtual sensors which build their internal process model after the data-driven method, e.g. by use of an artificial neural network (ANN), there is a problem of the evaluation of the prediction performance. The up to date solutions solve this problem only partially and only for few ANN types, require huge development effort and are inapplicable for the real time operation. A new approach for the improvement of the VS prediction performance based on the statistical process control (SPC) methods is suggested in this article. It is valid for a wide class of the ANN and reduces the development effort severely. The simulation of this approach using the real process data has delivered promising results.

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Section: Adaptive Virtual Sensormentioning

confidence: 99%

“…The approach with online adaptation is quite difficult and cannot be applied universally [3]. In contrast, the only bias adaptation can be realized relatively simple under closed loop conditions [14].…”

Section: Adaptive Virtual Sensormentioning

confidence: 99%